Vision-Language Grounding

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Overview

Vision-language grounding studies how models connect language to specific visual evidence, enabling them to locate a described object, segment a region, track an entity through video, or act on an instruction. Common approaches align text with image regions using contrastive learning, detection, segmentation, and point or box prediction; newer multimodal models also use attention, structured scene representations, visual search, and step-by-step evidence grounding. Evaluation spans referring-expression benchmarks, high-resolution and video localization, document understanding, and embodied tasks such as navigation and robotic manipulation. A central challenge is that correct answers do not prove models used the image: language priors, ambiguity, small or changing targets, and long-horizon context can all undermine faithful and robust grounding.

Latest papers

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420 papers49 papers in the last four weeks, down 6% on the four weeks before. 0.3% of all new papers.

Mar 6, 2026

Step-Level Visual Grounding Faithfulness Predicts Out-of-Distribution Generalization in Long-Horizon Vision-Language Models

We uncover a behavioral law of long-horizon vision-language models: models that maintain temporally grounded beliefs generalize better. Standard benchmarks measure only final-answer accuracy, which obscures how models use visual information; a model can guess correctly while its step-by-step reasoning is entirely unanchored to the visual input. We formalize this as behavioral faithfulness over long horizons, an empirically measurable property that quantifies whether a model's intermediate reasoning remains consistent with the evolving visual state. Across eight models on three long-horizon benchmarks, we demonstrate that temporal grounding quality is a leading indicator of robustness: the Step Grounding Rate (SGR) predicts out-of-distribution retention with r=0.83r = 0.83 (permutation test p=0.003p = 0.003), a relationship that holds within capacity-matched models and cannot be explained by scale or in-distribution accuracy. Critically, grounding quality varies by up to 10.8 percentage points within parameter-matched 7B models despite similar accuracy, revealing it as an independent axis of model capability. Multiple robustness checks confirm the signal reflects genuine visual reliance: counterfactual traces drop SGR by 26--41 percentage points, cross-architecture verifiers agree at ρ=0.96ρ= 0.96, random reasoning scores near chance (∼18%\sim 18\%), and the predictor remains strong even without explicit reasoning disclosure (r=0.78r = 0.78).

Md Ashikur Rahman, Md Arifur Rahman, Niamul Hassan Samin +2

The KOW Company · University of Dhaka · American International University - Bangladesh (AIUB)

Vision-Language GroundingVLM RobustnessFaithfulness in VLMsOOD Generalization
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Mar 5, 2026

Keeping the Evidence Chain: Semantic Evidence Allocation for Training-Free Token Pruning in Video Temporal Grounding

Video Temporal Grounding (VTG) localizes the temporal boundaries of query-relevant moments in long, untrimmed videos, making video-language-model prohibitively expensive. While recent training-free token pruning has shown success in video question answering, naively applying these objectives to VTG causes drastic degradation, as VTG crucially depends on boundary-sensitive evidence and cross-frame reasoning chains. We therefore identify two VTG-specific pruning principles: evidence retention, which keeps query-critical patches especially around event boundaries, and connectivity strength, which preserves cross-frame connectivity for long-range evidence aggregation. Building on these insights, we propose SemVID, a training-free pruning framework that constructs a compact yet coherent token subset with complementary semantic roles. SemVID first allocates per-frame budgets by balancing query relevance and inter-frame variation to avoid over-pruned segments, and then selects three types of tokens: object tokens for diverse query-critical evidence, motion tokens to capture meaningful transitions and serve as cross-frame relays, and context tokens for scene continuity. Extensive experiments show that SemVID achieves a strong accuracy-efficiency trade-off, retaining up to 95.4% mIoU with only 12.5% visual tokens and delivering up to a 5.8x prefill speedup, consistently outperforming prior methods under the same budgets. Our code is available at https://github.com/JiaqiLi404/SemVID

Jiaqi Li, Shuntian Zheng, Yixian Shen +4

University of Warwick, Coventry CV4 7AL, United Kingdom · University of Amsterdam, Amsterdam 1012 WX, Netherlands · Amazon AGI, The United States of America

Temporal Video GroundingVision-Language GroundingVisual Token PruningVisual Token Compression
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Mar 3, 2026

Seeing Clearly without Training: Mitigating Hallucinations in Multimodal LLMs for Remote Sensing

Multimodal large language models (MLLMs) suffer from pronounced hallucinations in remote sensing visual question-answering (RS-VQA), primarily caused by visual grounding failures in large-scale scenes or misinterpretation of fine-grained small targets. To systematically analyze these issues, we introduce RSHBench, a protocol-based benchmark for fine-grained diagnosis of factual and logical hallucinations. To mitigate grounding-induced factual hallucinations, we further propose Relative Attention-Driven Actively Reasoning (RADAR), a training-free inference method that leverages intrinsic attention in MLLMs to guide progressive localization and fine-grained local reasoning at test time. Extensive experiments across diverse MLLMs demonstrate that RADAR consistently improves RS-VQA performance and reduces both factual and logical hallucinations. Code and data will be publicly available at: https://github.com/MiliLab/RADAR

Yi Liu, Jing Zhang, Di Wang +3

School of Computer Science, Wuhan University, China · Zhongguancun Academy, China · School of Computer Science, Chongqing University, China

Remote Sensing VQAVLM Hallucination MitigationMultimodal Large Language ModelsVision-Language GroundingHallucination Detection in VLMsRemote Sensing Benchmarks
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Mar 2, 2026

Better Eyes, Better Thoughts: Why Vision Chain-of-Thought Fails in Medicine

Large vision-language models (VLMs) often benefit from chain-of-thought (CoT) prompting in general domains, yet its efficacy in medical vision-language tasks remains underexplored. We report a counter-intuitive trend: on medical visual question answering, CoT frequently underperforms direct answering (DirA) across general-purpose and medical-specific models. We attribute this to a medical perception bottleneck: subtle, domain-specific cues can weaken visual grounding, and CoT may compound early perceptual uncertainty rather than correct it. To probe this hypothesis, we introduce two training-free, inference-time grounding interventions: (i) perception anchoring via region-of-interest cues and (ii) description grounding via high-quality textual guidance. Across multiple benchmarks and model families, these interventions improve accuracy, mitigate CoT degradation, and in several settings reverse the CoT--DirA inversion. Our findings suggest that reliable clinical VLMs require robust visual grounding and cross-modal alignment, beyond extending text-driven reasoning chains. Code is available here.

Yuan Wu, Zongxian Yang, Jiayu Qian +5

Department of Computer Science, City University of Hong Kong (Dongguan) · College of Computing and Data Science (CCDS), Nanyang Technological University · School of Computer Science, Northwestern Polytechnical University

Medical VLMsVLM ReasoningMedical VQAVision-Language Grounding
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Feb 27, 2026

Robust Skills, Brittle Grounding: Diagnosing Restricted Generalization in Vision-Language Action Policies via Multi-Object Picking

Vision-language action (VLA) policies often report strong manipulation benchmark performance with relatively few demonstrations, but it remains unclear whether this reflects robust language-to-object grounding or reliance on object--location correlations that do not transfer beyond the training distribution. We present a controlled multi-object picking study that progressively increases object placement variability up to full workspace randomization and evaluates held-out object--location pairings that break familiar associations without increasing spatial difficulty. Across these stress tests and data scaling, we find that for representative VLA policies, including SmolVLA and π0.5π_{0.5}, execution of the manipulation primitive remains substantially more reliable than instruction-conditioned task success in harder regimes, suggesting that manipulation skill acquisition is decoupled from instruction following. We recommend augmenting manipulation benchmarks with task ladders and decomposed metrics that separately measure primitive execution and instruction-conditioned success to better diagnose instruction-grounded generalization.

David Emukpere, Romain Deffayet, Jean-Michel Renders

Naver Labs Europe, 6 Chem. de Maupertuis, Meylan, France.

Instruction Generalization in VLA ModelsGeneralization in Robotic ManipulationVision-Language GroundingRobot Manipulation Benchmarks
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Feb 27, 2026

Ref-Adv: Exploring MLLM Visual Reasoning in Referring Expression Tasks

Referring Expression Comprehension (REC) links language to region level visual perception. Standard benchmarks (RefCOCO, RefCOCO+, RefCOCOg) have progressed rapidly with multimodal LLMs but remain weak tests of visual reasoning and grounding: (i) many expressions are very short, leaving little reasoning demand; (ii) images often contain few distractors, making the target easy to find; and (iii) redundant descriptors enable shortcut solutions that bypass genuine text understanding and visual reasoning. We introduce Ref-Adv, a modern REC benchmark that suppresses shortcuts by pairing linguistically nontrivial expressions with only the information necessary to uniquely identify the target. The dataset contains referring expressions on real images, curated with hard distractors and annotated with reasoning facets including negation. We conduct comprehensive ablations (word order perturbations and descriptor deletion sufficiency) to show that solving Ref-Adv requires reasoning beyond simple cues, and we evaluate a broad suite of contemporary multimodal LLMs on Ref-Adv. Despite strong results on RefCOCO, RefCOCO+, and RefCOCOg, models drop markedly on Ref-Adv, revealing reliance on shortcuts and gaps in visual reasoning and grounding. We provide an in depth failure analysis and aim for Ref-Adv to guide future work on visual reasoning and grounding in MLLMs.

Qihua Dong, Kuo Yang, Lin Ju +6

Northeastern University

Referring Expression ComprehensionVLM EvaluationVision-Language GroundingVisually Grounded Reasoning
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Feb 27, 2026

Vision-Language Semantic Grounding for Multi-Domain Crop-Weed Segmentation

Fine-grained crop-weed segmentation is essential for enabling targeted herbicide application in precision agriculture. However, existing deep learning models struggle to generalize across heterogeneous agricultural environments due to reliance on dataset-specific visual features. We propose Vision-Language Weed Segmentation (VL-WS), a novel framework that addresses this limitation by grounding pixel-level segmentation in semantically aligned, domain-invariant representations. Our architecture employs a dual-encoder design, where frozen Contrastive Language-Image Pretraining (CLIP) embeddings and task-specific spatial features are fused and modulated via Feature-wise Linear Modulation (FiLM) layers conditioned on natural language captions. This design enables image level textual descriptions to guide channel-wise feature refinement while preserving fine-grained spatial localization. Unlike prior works restricted to training and evaluation on single-source datasets, VL-WS is trained on a unified corpus that includes close-range ground imagery (robotic platforms) and high-altitude UAV imagery, covering diverse crop types, weed species, growth stages, and sensing conditions. Experimental results across four benchmark datasets demonstrate the effectiveness of our framework, with VL-WS achieving a mean Dice score of 91.64% and outperforming the CNN baseline by 4.98%. The largest gains occur on the most challenging weed class, where VL-WS attains 80.45% Dice score compared to 65.03% for the best baseline, representing a 15.42% improvement. VL-WS further maintains stable weed segmentation performance under limited target-domain supervision, indicating improved generalization and data efficiency. These findings highlight the potential of vision-language alignment to enable scalable, label-efficient segmentation models deployable across diverse real-world agricultural domains.

Nazia Hossain, Xintong Jiang, Yu Tian +3

Department of Bioresource Engineering, McGill University, Sainte-Anne-de-Bellevue, QC, Canada · Department of Plant Science, McGill University, Sainte-Anne-de-Bellevue, QC, Canada

Domain Generalization for Semantic SegmentationSemantic SegmentationText-Guided Image SegmentationVision-Language GroundingRemote Sensing Image Segmentation
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Feb 26, 2026

Towards Long-Form Spatio-Temporal Video Grounding

In real scenarios, videos can span several minutes or even hours. However, existing research on spatio-temporal video grounding (STVG), given a textual query, mainly focuses on localizing targets in short videos of tens of seconds, typically less than one minute, which limits real-world applications. In this paper, we explore Long-Form STVG (LF-STVG), which aims to locate targets in long-term videos. Compared with short videos, long-term videos contain much longer temporal spans and more irrelevant information, making it difficult for existing STVG methods that process all frames at once. To address this challenge, we propose an AutoRegressive Transformer architecture for LF-STVG, termed ART-STVG. Unlike conventional STVG methods that require the entire video sequence to make predictions at once, ART-STVG treats the video as streaming input and processes frames sequentially, enabling efficient handling of long videos. To model spatio-temporal context, we design spatial and temporal memory banks and apply them to the decoders. Since memories from different moments are not always relevant to the current frame, we introduce simple yet effective memory selection strategies to provide more relevant information to the decoders, significantly improving performance. Furthermore, instead of parallel spatial and temporal localization, we propose a cascaded spatio-temporal design that connects the spatial decoder to the temporal decoder, allowing fine-grained spatial cues to assist complex temporal localization in long videos. Experiments on newly extended LF-STVG datasets show that ART-STVG significantly outperforms state-of-the-art methods, while achieving competitive performance on conventional short-form STVG. Our code is at: https://github.com/HengLan/ART-STVG.

Xin Gu, Bing Fan, Jiali Yao +5

University of Chinese Academy of Sciences · 2UNT · Institute of Software Chinese Academy of Sciences +2

Temporal Video GroundingLong-Context ModelingVision-Language Grounding
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Feb 25, 2026

StoryMovie: A Dataset for Semantic Alignment of Visual Stories with Movie Scripts and Subtitles

Visual storytelling models that correctly ground entities in images may still hallucinate semantic relationships, generating incorrect dialogue attribution, character interactions, or emotional states. We introduce StoryMovie, a dataset of 1,757 stories aligned with movie scripts and subtitles through LCS matching. Our alignment pipeline synchronizes screenplay dialogue with subtitle timestamps, enabling dialogue attribution by linking character names from scripts to temporal positions from subtitles. Using this aligned content, we generate stories that maintain visual grounding tags while incorporating authentic character names, dialogue, and relationship dynamics. We fine-tune Qwen Storyteller3 on this dataset, building on prior work in visual grounding and entity re-identification. Evaluation using DeepSeek V3 as judge shows that Storyteller3 achieves an 89.9% win rate against base Qwen2.5-VL 7B on subtitle alignment. Compared to Storyteller, trained without script grounding, Storyteller3 achieves 48.5% versus 38.0%, confirming that semantic alignment progressively improves dialogue attribution beyond visual grounding alone.

Daniel Oliveira, David Martins de Matos

INESC-ID Lisboa, R. Alves Redol 9, 1000-029 Lisboa, Portugal · Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisboa, Portugal

Visual StorytellingVision-Language AlignmentVision-Language Grounding
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Feb 23, 2026

A Multimodal Framework for Aligning Human Linguistic Descriptions with Visual Perceptual Data

Establishing stable mappings between natural language expressions and visual percepts is a foundational problem for both cognitive science and artificial intelligence. Humans routinely ground linguistic reference in noisy, ambiguous perceptual contexts, yet the mechanisms supporting such cross-modal alignment remain poorly understood. In this work, we introduce a computational framework designed to model core aspects of human referential interpretation by integrating linguistic utterances with perceptual representations derived from large-scale, crowd-sourced imagery. The system approximates human perceptual categorization by combining scale-invariant feature transform (SIFT) alignment with the Universal Quality Index (UQI) to quantify similarity in a cognitively plausible feature space, while a set of linguistic preprocessing and query-transformation operations captures pragmatic variability in referring expressions. We evaluate the model on the Stanford Repeated Reference Game corpus (15,000 utterances paired with tangram stimuli), a paradigm explicitly developed to probe human-level perceptual ambiguity and coordination. Our framework achieves robust referential grounding. It requires 65% fewer utterances than human interlocutors to reach stable mappings and can correctly identify target objects from single referring expressions 41.66% of the time (versus 20% for humans).These results suggest that relatively simple perceptual-linguistic alignment mechanisms can yield human-competitive behavior on a classic cognitive benchmark, and offers insights into models of grounded communication, perceptual inference, and cross-modal concept formation. Code is available at https://anonymous.4open.science/r/metasequoia-9D13/README.md .

Joseph Bingham

Faculty of Biology, Technion University, Haifa, Israel.

Referring Expression ComprehensionMultimodal GroundingCross-Modal AlignmentVision-Language Grounding
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Feb 19, 2026

Focusing Where Vision Matters: Selective Training for Large Vision Language Models via Visual Information Gain

Large Vision Language Models (LVLMs) have achieved remarkable progress, yet they often suffer from language bias, producing answers without relying on visual evidence. While prior work attempts to mitigate this issue through decoding strategies, architectural modifications, or curated instruction data, they typically lack a quantitative measure of how much individual training samples or tokens actually benefit from the image. In this work, we introduce Visual Information Gain (VIG), a perplexity-based metric that measures the reduction in prediction uncertainty provided by visual input. VIG enables fine-grained analysis at both sample and token levels, effectively highlighting visually grounded elements such as colors, spatial relations, and attributes. Leveraging this, we propose a VIG-guided selective training scheme that prioritizes high-VIG samples and tokens. This approach improves visual grounding and mitigates language bias, achieving superior performance with significantly reduced supervision by focusing exclusively on visually informative samples and tokens.

Seulbi Lee, Sangheum Hwang

Department of Data Science, Seoul National University of Science and Technology, Seoul, Republic of Korea. · Currently with Tomocube, Daejeon, Republic of Korea.

Vision-Language ModelsLarge Vision-Language ModelsVision-Language GroundingFaithfulness in VLMs
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Feb 18, 2026

Saliency-Aware Multi-Route Thinking: Revisiting Vision-Language Reasoning

Vision-language models (VLMs) aim to reason by jointly leveraging visual and textual modalities. While allocating additional inference-time computation has proven effective for large language models (LLMs), achieving similar scaling in VLMs remains challenging. A key obstacle is that visual inputs are typically provided only once at the start of generation, while textual reasoning (e.g., early visual summaries) is generated autoregressively, causing reasoning to become increasingly text-dominated and allowing early visual grounding errors to accumulate. Moreover, vanilla guidance for visual grounding during inference is often coarse and noisy, making it difficult to steer reasoning over long texts. To address these challenges, we propose Saliency-Aware Principle (SAP) selection. SAP operates on high-level reasoning principles rather than token-level trajectories, which enable stable control over discrete generation under noisy feedback while allowing later reasoning steps to re-consult visual evidence when renewed grounding is required. In addition, SAP supports multi-route inference, enabling parallel exploration of diverse reasoning behaviors. SAP is model-agnostic and data-free, requiring no additional training. Empirical results show that SAP achieves competitive performance, especially in reducing object hallucination, under comparable token-generation budgets while yielding more stable reasoning and lower response latency than CoT-style long sequential reasoning.

Mingjia Shi, Yinhan He, Yaochen Zhu +1

University of Virginia.

VLM ReasoningReasoning for VLMsTest-Time Scaling for VLMsVision-Language GroundingVLM Hallucination Mitigation
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Feb 17, 2026

Revealing and Enhancing Core Visual Regions: Harnessing Internal Attention Dynamics for Hallucination Mitigation in LVLMs

LVLMs have achieved strong multimodal reasoning capabilities but remain prone to hallucinations, producing outputs inconsistent with visual inputs or user instructions. Existing training-free methods, including contrastive decoding and auxiliary expert models, which incur several times more computational overhead and may introduce potential interference, as well as static internal signal enhancement, are often vulnerable to the attention sink phenomenon. We find that internal Positive Attention Dynamics (PAD) in LVLMs naturally reveal semantically core visual regions under the distortions of attention sinks. Based on this, we propose Positive Attention Dynamics Enhancement (PADE), a training-free attention intervention that constructs a PAD map to identify semantically core visual regions, applies per-head Median Absolute Deviation Scaling to adaptively control the intervention strength, and leverages System-Token Compensation to maintain attention to complex user instructions and support long-term output consistency. Experiments on multiple LVLMs and benchmarks show that PADE improves visual grounding and reduces hallucinations, validating the effectiveness of leveraging internal attention dynamics for reliable multimodal reasoning.

Guangtao Lyu, Qi Liu, Chenghao Xu +5

School of Electronic Engineering, Xidian University, Xi’an, China · College of Computer and Information, Hohai University, Nanjing, China · School of Computer Science and Technology, Xidian University, Xi’an, China +1

Attention Intervention in VLMsVLM Hallucination MitigationVision-Language ModelsLarge Vision-Language ModelsObject Hallucination in VLMsVision-Language Grounding
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Feb 9, 2026

Do MLLMs Really See It: Reinforcing Visual Attention in Multimodal LLMs

While chain-of-thought (CoT) reasoning has substantially improved multimodal large language models (MLLMs) on complex reasoning tasks, existing approaches largely rely on long textual reasoning trajectories and provide limited mechanisms for learning stable visual attention policies. Our analysis shows that current MLLMs exhibit weak visual focus: early-stage visual misalignment is rarely corrected during subsequent reasoning, leading to error propagation and failed inferences. We argue that this limitation stems from inadequate credit assignment for visual attention during training. To address this issue, we propose SAYO, a visual reasoning model trained with a reinforcement learning (RL) framework that introduces a region-level visual attention-based reward. This reward explicitly aligns optimization signals with visually grounded reasoning steps, enabling the model to learn more reliable attention behaviors. Extensive experiments across multiple multimodal benchmarks demonstrate that SAYO consistently improves performance on diverse reasoning and perception tasks. Project is open at https://github.com/Cratileo/Sayo.

Siqu Ou, Tianrui Wan, Zhiyuan Zhao +2

TeleAI · Shanghai Jiao Tong University · Northwestern Polytechnical University

Reasoning for VLMsAttention Intervention in VLMsVisual Evidence Sensitivity in VLMsRL for VLMsVision-Language Grounding
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Jan 27, 2026

PixSearch: Region-Grounded Retrieval for Knowledgeable Large Multimodal Models

Multimodal Retrieval-Augmented Generation (MM-RAG) augments visual question answering (VQA) with external knowledge, but many systems rely on modular pipelines that separate visual grounding, query construction, and answer generation. This separation can propagate errors from intermediate detections, masks, or captions into retrieval while adding latency and limiting coordination with ongoing reasoning. These limitations are especially consequential for egocentric images, where questions often concern small, off-center, or long-tail entities that global retrieval can overlook. We propose PixSearch, an end-to-end segmenting Large Multimodal Model (LMM) that unifies region-level visual grounding with retrieval-augmented reasoning. During autoregressive generation, PixSearch learns when to retrieve, how to route queries across text, whole-image, and region-level retrieval, and how to crop the target entity as a visual query using a jointly trained mask decoder. A two-stage supervised fine-tuning regimen with search-interleaved supervision teaches retrieval triggering, query selection, and evidence-grounded answer generation while preserving segmentation ability. On egocentric and entity-centric VQA benchmarks, PixSearch achieves an 18.3% relative accuracy gain on CRAG-MM over whole-image retrieval, with particularly strong gains on egocentric images, while remaining competitive on broader VQA and text-only QA tasks. It also reduces inference latency by 36.4% compared with a cached GroundedSAM-LLaVA pipeline.

Jeonghwan Kim, Renjie Tao, Sanat Sharma +8

Meta Reality Labs · University of Illinois Urbana-Champaign

Multimodal RAGVisual Question AnsweringLarge Vision-Language ModelsVision-Language GroundingImage Retrieval
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Jan 17, 2026

Visual-Language-Guided Task Planning for Horticultural Robots

Crop monitoring is essential for precision agriculture, but current systems lack high-level reasoning. We introduce a novel, modular framework that uses a Vision Language Model (VLM) to guide robotic task planning by actively querying heterogeneous data sources, including enriched RGB camera feeds and 2D semantic occupancy maps, interleaved with robotic action primitives. We contribute a comprehensive benchmark for short- and long-horizon crop monitoring tasks in monoculture and polyculture environments. Our results show that while zero-shot VLMs perform robustly for short-horizon tasks (achieving 87% success, comparable to human experts), success drops significantly to under 10% for complex long-horizon, multi-target tasks. Despite this decline, task completion rates remain above 76% under noiseless conditions. Critically, the system degrades when relying on noisy semantic maps, demonstrating a key limitation in current VLM context grounding for sustained robotic operations. This work offers a deployable framework and critical insights into VLM capabilities and shortcomings for complex agricultural robotics.

Jose Cuaran, Kendall Koe, Aditya Potnis +2

The Siebel School of Computing and Data Science, University of Illinois, Urbana-Champaign · National Center for Supercomputing Applications at University of Illinois, Urbana-Champaign · The Department of Agricultural and Biological Engineering, University of Illinois, Urbana-Champaign

Large Language Model-Based Robot PlanningVision-Language GroundingAgricultural RoboticsVLMs for RoboticsPrecision Agriculture
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Dec 19, 2025

PathFLIP: Fine-grained Language-Image Pretraining for Versatile Computational Pathology

While Vision-Language Models (VLMs) have achieved notable progress in computational pathology (CPath), the gigapixel scale and spatial heterogeneity of Whole Slide Images (WSIs) continue to pose challenges for multimodal understanding. Existing alignment methods struggle to capture fine-grained correspondences between textual descriptions and visual cues across thousands of patches from a slide, compromising their performance on downstream tasks. In this paper, we propose PathFLIP (Pathology Fine-grained Language-Image Pretraining), a novel framework for holistic WSI interpretation. PathFLIP decomposes slide-level captions into region-level subcaptions and generates text-conditioned region embeddings to facilitate precise visual-language grounding. By harnessing Large Language Models (LLMs), PathFLIP can seamlessly follow diverse clinical instructions and adapt to varied diagnostic contexts. Furthermore, it exhibits versatile capabilities across multiple paradigms, efficiently handling slide-level classification and retrieval, fine-grained lesion localization, and instruction following. Extensive experiments demonstrate that PathFLIP outperforms existing large-scale pathological VLMs on four representative benchmarks while requiring significantly less training data, paving the way for fine-grained, instruction-aware WSI interpretation in clinical practice.

Fengchun Liu, Songhan Jiang, Linghan Cai +2

Harbin Institute of Technology, Shenzhen, School of Computer Science and Technology · National University of Singapore, Department of Electronic and Computer Engineering

Computational PathologyVision-Language GroundingMedical VLMsWhole-Slide Image Analysis
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Dec 12, 2025

VLM2GeoVec: Toward Universal Multimodal Embeddings for Remote Sensing

Satellite imagery differs from natural images in viewpoint, resolution, scale variation, and the prevalence of small objects -- demanding both region-level spatial reasoning and holistic scene understanding. Existing remote-sensing approaches are fragmented: dual-encoder retrieval models scale well but cannot interleave modalities, whereas generative assistants support grounding, yet are inefficient for retrieval. Benchmarks mirror this split: interleaved evaluations mainly target generative assistants, while cross-modal retrieval benchmarks target dual encoders. To bridge this gap, we introduce RSMEB, a unified remote sensing benchmark that evaluates cross-modal and interleaved retrieval across 21 tasks under a single ranking protocol, enabling comprehensive comparison of retrieval models on region- and geo-aware capabilities as well as conventional retrieval. As a strong reference baseline, we present VLM2GeoVec, an instruction-conditioned, single-encoder interleaving formulation tailored to remote sensing that packs image, text, bounding-box, and geo-coordinate tokens into one sequence and learns a unified embedding via contrastive training. Across RSMEB, VLM2GeoVec achieves 26.6%\textbf{26.6\%} P@1 in region-caption retrieval (+25\textbf{+25} percentage points), 32.5%\textbf{32.5\%} in referring-expression retrieval (+19\textbf{+19}), and 17.8%\textbf{17.8\%} in semantic geo-aware retrieval (\textbf{>3}$$\times prior best), while remaining competitive in conventional scene classification and text--image retrieval in zero-shot settings. Together, the proposed suite and reference baseline standardize evaluation and deliver a unified embedder for scalable retrieval and region-/geo-aware grounding. The code, the model checkpoints, and the data are available at https://github.com/emasa/VLM2GeoVec.

Emanuel Sánchez Aimar, Gulnaz Zhambulova, Fahad Shahbaz Khan +2

Linköping University, Sweden

Multimodal UnderstandingCross-Modal LearningMultimodal EmbeddingCross-Modal RetrievalVision-Language GroundingRemote Sensing Benchmarks+2
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Oct 15, 2025

MMLongCite: A Benchmark for Evaluating Faithfulness of Long-Context Vision-Language Models

The rapid advancement of long-context vision language models (LCVLMs) has led to a significant expansion of their context windows. However, an extended context window does not guarantee the effective utilization of the context, posing a critical challenge for real-world applications. Current evaluations of such long-context faithfulness in multimodal settings remain limited to short contexts. To bridge this gap, we introduce MMLongCite, the first benchmark evaluating the faithfulness of LCVLMs via multimodal citation generation. MMLongCite features 2,280 examples across 8 tasks and diverse modalities (image, video, interleaved), with context lengths scaled from 16K to 128K tokens. To test spatial localization capabilities of LCVLMs, we also introduce MMLongCite-HR, evaluating fine-grained visual grounding amidst dense pixel spaces. Through extensive benchmarking of cutting-edge LCVLMs, we provide a systematic analysis of current multimodal citation capabilities. Our results reveal a significant discrepancy between answer correctness and citation faithfulness. We also conduct attention pattern investigations and in-depth error analyses to reveal the underlying phenomena of failures in LCVLMs. MMLongCite establishes a rigorous foundation for diagnosing and advancing the faithfulness of LCVLMs. We hope our findings provide meaningful insights to drive further improvements in the long-context capabilities of LCVLMs.

Keyan Zhou, Zecheng Tang, Lingfeng Ming +9

Soochow University · ByteDance · Harbin Institute of Technology +1

VLM EvaluationVision-Language Grounding
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Oct 10, 2025

Constructive Distortion: Improving MLLMs with Attention-Guided Image Warping

Multimodal large language models (MLLMs) often miss small details and spatial relations in cluttered scenes, leading to errors in fine-grained perceptual grounding. We introduce AttWarp, a lightweight method that allocates more resolution to query-relevant content while compressing less informative areas, all while preserving global context. At test time, the approach uses an MLLM's cross-modal attention to perform rectilinear warping of the input image, reallocating spatial resolution toward regions the model deems important, without changing model weights or architecture. This attention-guided warping preserves all original image information but redistributes it non-uniformly, so small objects and subtle relationships become easier for the same model to read while the global layout remains intact. Across five benchmarks (TextVQA, GQA, DocVQA, POPE, MMMU) and four MLLMs (LLaVA, Qwen-VL, InternVL, and InstructBLIP), AttWarp consistently improves accuracy, strengthens compositional reasoning, and reduces hallucinations, outperforming four competitive baselines that manipulate raw images at test time. Together, these results show that attention-guided warping prioritizes information relevant to the query while preserving context, and that the same MLLMs perform better when given such warped inputs.

Dwip Dalal, Gautam Vashishtha, Utkarsh Mishra +6

University of Illinois Urbana–Champaign · Skan AI · Texas A&M University +1

Object Hallucination in VLMsMultimodal Large Language ModelsVision-Language Grounding
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Sep 29, 2025

Talk in Pieces, See in Whole: Disentangled and Hierarchical Representation Learning in Language-based Object Detection

Vision-language models (VLMs) have advanced multimodal perception, demonstrated by open-vocabulary object detection with simple language queries. State-of-the-art VLMs still struggle to handle complex queries involving descriptive attributes and relational clauses. To address this problem, we propose restructuring linguistic representations according to the hierarchical relations within sentences for language-based object detection. A key insight is that textual tokens should be disentangled into core components-objects, attributes, and relations-and aggregated into hierarchically structured sentence-level representations. Building on this principle, we introduce the TaSe (Talk in Pieces, See in Whole) framework with three main contributions: (1) a hierarchical synthetic captioning dataset spanning three tiers from category names to descriptive sentences; (2) the three-component disentanglement module guided by a novel disentanglement loss function, transforms text embeddings into subspace compositions; and (3) aggregating disentangled components into hierarchically structured embeddings guided by the proposed hierarchical objectives. Experimental results under the OmniLabel benchmark show a 24% performance improvement, demonstrating the importance of linguistic compositionality.

Sojung An, Kwanyong Park, Yong Jae Lee +1

Korea University · University of Seoul · University of Wisconsin-Madison

Open-Vocabulary Object DetectionDisentangled Representation LearningHierarchical Representation LearningVision-Language Grounding
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Sep 26, 2025

RefAM: Attention Magnets for Zero-Shot Referral Segmentation

Most existing approaches to referring segmentation achieve strong performance only through fine-tuning or by composing multiple pre-trained models, often at the cost of additional training and architectural modifications. Meanwhile, large-scale generative diffusion models encode rich semantic information, making them attractive as general-purpose feature extractors. In this work, we introduce a new method that directly exploits features (attention scores) from diffusion transformers for downstream tasks, requiring neither architectural modifications nor additional training. To systematically evaluate these features, we extend benchmarks with vision-language grounding tasks spanning both images and videos. Our key insight is that stop words act as attention magnets: they accumulate surplus attention and can be filtered to reduce noise. Moreover, we identify global attention sinks (GAS) emerging in deeper layers and show that they can be safely suppressed or redirected onto auxiliary tokens, leading to sharper and more accurate grounding maps. We further propose an attention redistribution strategy, where appended stop words partition background activations into smaller clusters, yielding sharper and more localized heatmaps. Building on these findings, we develop RefAM, a simple training-free grounding framework that combines cross-attention maps, GAS handling, and redistribution. Across zero-shot referring image and video segmentation benchmarks, our approach achieves strong performance and surpasses prior methods on most datasets, establishing a new state of the art among training-free methods, without task-specific training or architectural modification. For video, we localize the referent from the spatio-temporal features of a video diffusion transformer via a first-frame query that is then propagated; we therefore do not explicitly resolve temporal (which-frame) expressions.

Anna Kukleva, Enis Simsar, Alessio Tonioni +4

Max Planck Institute for Informatics, SIC · ETH Zürich · Google +1

Attention MechanismsImage SegmentationVideo Object SegmentationDiffusion TransformerReferring Video Object SegmentationReferring Image Segmentation+4
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Sep 2, 2025

PixFoundation 2.0: Do Video Multi-Modal LLMs Use Motion in Visual Grounding?

Multi-modal large language models (MLLMs) have shown impressive generalization across tasks using images and text modalities. While their extension to video has enabled tasks such as video question answering and video captioning, their pixel-level visual grounding abilities are less studied. In this work, we raise the pertinent question of whether motion is used in pixel-level visual grounding and whether video MLLMs can segment objects based on natural language expressions describing their motion patterns. We identify the shortcomings in the current benchmarks, where we show that a single frame can often suffice for capturing the referring expression without any temporal reasoning. To address this, we introduce novel motion-centric probing, particularly designed for the visual grounding task, to study video MLLMs' ability to identify true motion from a fake one and their ability to grasp the ordering of motion. Consequently, we introduce MoCentric-Bench, a motion-centric benchmark designed to evaluate video MLLMs based on their ability to capture the interaction between motion and language, rather than relying primarily on static cues. We further establish strong single-image baselines that either match or surpass prior methods. Finally, we explore a simple motion-centric adaptation that provides state-of-the-art performance on our MoCentric-Bench. Code is available at https://msiam.github.io/PixFoundationSeries/.

Mennatullah Siam

Vision-Language GroundingVideo-Language Model EvaluationTemporal Reasoning in VLMsTemporal Video Grounding
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Aug 19, 2025

VMMU: A Vietnamese Multitask Multimodal Understanding and Reasoning Benchmark

We introduce VMMU, a Vietnamese Multitask Multimodal Understanding and Reasoning Benchmark designed to evaluate how vision-language models (VLMs) interpret and reason over visual and textual information beyond English. VMMU consists of 2.5k multimodal questions across 7 tasks, covering a diverse range of problem contexts, including STEM problem solving, data interpretation, rule-governed visual reasoning, and abstract visual reasoning. All questions require genuine multimodal integration, rather than reliance on text-only cues or OCR-based shortcuts. We evaluate a diverse set of state-of-the-art proprietary and open-source VLMs on VMMU. Despite strong Vietnamese OCR performance, proprietary models achieve only 66% mean accuracy. Further analysis shows that the primary source of failure is not OCR, but instead multimodal grounding and reasoning over text and visual evidence. Code and data are available at https://vmmu-bench.github.io/

Vy Tuong Dang, An Vo, Emilio Villa-Cueva +4

KAIST · MBZUAI

VLM EvaluationMultimodal ReasoningMultilingual VLM EvaluationVision-Language GroundingVisually Grounded ReasoningMultimodal Foundation Models+2
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Aug 11, 2025

TAR: Temporal Anchor-Constrained Reasoning for Video Temporal Grounding

Video Temporal Grounding (VTG) aims to localize specific video segments corresponding to natural language queries. While recent Large Vision-Language Models (LVLMs) employ Reinforcement Learning to generate Chains-of-Thought (CoT), they typically rely solely on outcome-based supervision. Consequently, this often leads to hallucinations, where the reasoning process becomes disconnected from the visual content and the final prediction. Existing attempts to mitigate this by relying on external supervision from larger models or separate reward models are computationally expensive and prone to rigid patterns. To address these challenges, we propose TAR (Temporal Anchor-Constrained Reasoning), a framework that introduces the temporal anchor (T-anchor) as a transparent and auditable checkpoint mechanism. T-anchor enforces progressive refinement within the CoT, compelling the model to continuously ground its intermediate thoughts in visual evidence and iteratively calibrate temporal predictions, thereby significantly enhancing the faithfulness and autonomy of the reasoning process and final accuracy. Furthermore, we introduce a bootstrapping paradigm that automatically harvests high-quality CoT data using only a standard 7B model, eliminating the dependency on ultra-large models. Extensive experiments demonstrate that TAR achieves state-of-the-art performance and generates faithful, autonomous, and progressively refined reasoning traces.

Chaohong Guo, Xun Mo, Yongwei Nie +3

South China University of Technology · Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) · Bytedance Inc.

Temporal Video GroundingVision-Language ModelsVision-Language GroundingVLM ReasoningFaithfulness in VLMs
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Jul 14, 2025

MLLM-Guided Autonomous Object Detection for Open-World Understanding

Object detection typically relies on predefined category vocabularies, while open-world and open-vocabulary approaches remain limited by generic unknown labels or externally specified detection concepts. We propose LAOD, an MLLM-Guided Autonomous Object Detection framework in which a Multimodal Large Language Model (MLLM) generates an image-specific detection vocabulary that is grounded by an open-vocabulary detector. This decomposition separates semantic concept discovery from spatial localization and removes the need to manually enumerate target categories at inference time. We evaluate LAOD using Class-Agnostic Average Precision (CAAP), Semantic Naming Average Precision (SNAP), and Semantic Localization Average Precision (SemLoc-AP), which measure localization, semantic naming, and their correct association, respectively. Experiments on COCO-Val, LVIS-minival, and COCO-OOD across multiple MLLMs and detectors demonstrate strong performance across common, long-tailed, and out-of-distribution objects, while showing that grounding quality remains a major determinant of end-to-end performance. LAOD also substantially outperforms both direct MLLM-based object detection and dedicated models trained specifically for open-ended detection. Overall, the results support a modular formulation of autonomous object detection in which MLLMs determine what to detect and specialized open-vocabulary detectors determine where those concepts occur.

Furkan Mumcu, Michael J. Jones, Anoop Cherian +1

University of South Florida · Mitsubishi Electric Research Laboratories (MERL)

Open-Vocabulary Object DetectionVision-Language GroundingLarge Vision-Language Models
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Mar 20, 2025

FORGE: Forensic Reasoning with Grounded Evidence

Forensic deepfake analysis demands more than binary classification: investigators need region-grounded natural language explanations they can verify against the image. Multimodal large language models (MLLMs) are a natural fit, but pretrained MLLMs fail systematically, producing globally coherent text that misses the small localized cues defining manipulations. We argue this is an inductive bias problem rather than a capacity issue: the image-text contrastive objective training MLLM visual encoders optimizes for whole-image semantic summaries, not patch-level forensic detail. The same mismatch explains why prior deepfake reasoning methods target either face manipulation or fully AI-generated content, never both. We propose FORGE, which addresses the mismatch by routing a second visual stream into the language model from a Vision-Only Model (VOM) trained on dense patch prediction rather than image-text alignment. The MLLM's native encoder and the VOM operate on a shared patch grid, which lets us interleave their tokens with preserved spatial correspondence; we show this beats naive concatenation. A two-stage adapter training protocol (generic image-caption alignment, then joint task-specific optimization) prevents the localized stream from overfitting to training-domain manipulations. Across face-manipulated and fully synthetic content, FORGE produces region-referential explanations answering fine-grained attribute queries ("Does the eyes/nose/mouth look real or fake?") and substantially outperforms in-domain baselines on cross-domain evaluations; region-specific evaluation and human studies confirm explanation faithfulness.

Rohit Kundu, Shan Jia, Vishal Mohanty +2

University of California, Riverside · Google LLC

VLM AdaptationExplainable Deepfake DetectionMultimodal Large Language ModelsVision-Language GroundingDeepfake DetectionMultimodal Deepfake Detection
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Mar 14, 2025

Fine-Grained Instruction-Guided Graph Reasoning for Vision-and-Language Navigation

Vision and Language Navigation (VLN) requires an embodied agent to traverse complex environments by following natural language instructions, demanding accurate alignment between visual observations and linguistic guidance. To address these challenges, we propose a fine grained instruction guided graph reasoning framework (FIGR) that enhances both spatial representation and instruction understanding during navigation. Specifically, an observation graph interaction mechanism is introduced to disentangle angular and visual cues while strengthening directed edge representations through geometric embedding, enabling more reliable spatial reasoning within the navigation graph. The key detail guidance module is implemented as Adaptive Open Vocabulary Guidance (AOVG), where a contextual role parser dynamically identifies location, object, spatial relation, and other contextual cues. This design avoids exact string matching and supports previously unseen entities and compositional expressions. For multilingual instructions, a Multilingual Semantic Adapter (MSA) maps language-specific representations into a shared navigation-semantic space. By jointly integrating structured graph reasoning with instruction critical semantic cues, the proposed approach significantly improves the agent ability to follow complex navigation instructions. On the validation-unseen splits, FIGR achieves 67 SPL on R2R and 64.8 sDTW on RxR, exceeding SPENav by 1 percentage point in SPL and PRET by 2.4 points in sDTW, respectively.

Yaohua Liu, Binkai Ou, Rong Fu +2

Faculty of Science and Technology, University of Macau, Macau 999078, China

Vision-Language NavigationVision-Language GroundingGraph Reasoning
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Feb 6, 2025

PixFoundation: Are We Heading in the Right Direction with Pixel-level Vision Foundation Models?

Multiple works have emerged to push the boundaries of multi-modal large language models (MLLMs) towards pixel-level understanding. The current trend is to train MLLMs with pixel-level grounding supervision in terms of masks on large-scale labelled data and specialized decoders for the segmentation task. However, we show that such MLLMs when evaluated on recent challenging vision-centric benchmarks, exhibit a weak ability in visual question answering (VQA). Surprisingly, some of these methods even downgrade the grounding ability of MLLMs that were never trained with such pixel-level supervision. In this work, we propose two novel challenging benchmarks with paired evaluation for both VQA and grounding. We demonstrate that simple baselines that are not unified achieve performance that matches or surpasses some of the pixel-level MLLMs. Our paired benchmarks and evaluation enable additional analysis on whether the failure stems from VQA and/or grounding abilities. Furthermore, we propose a paired prompt sensitivity analysis on both the language and visual prompts tailored for the grounding task. Our benchmarking pushes pixel-level MLLMs to improve in both accuracy and robustness, without sacrificing language abilities. More importantly, we study the research question of ``When does grounding emerge in MLLMs with respect to the output tokens?'' We propose a post-hoc interpretability tool that can be plugged into any MLLM to study the aforementioned question. We show that grounding does not necessarily coincide with the exact referring expression in the output, but can coincide with the object parts, its location, appearance, context or state. Code and datasets are publicly available at https://msiam.github.io/PixFoundationSeries/.

Mennatullah Siam

Large Vision-Language ModelsVision-Language GroundingVLM EvaluationVLM InterpretabilityPrompt Sensitivity
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Dec 1, 2024

Paint Outside the Box: Synthesizing and Selecting Training Data for Visual Grounding

Visual grounding aims to localize the image regions based on a textual query. Given the difficulty of large-scale data curation, we investigate how to effectively learn visual grounding under data-scarce settings in this paper. To address the data scarcity, we propose a novel framework, POBF (Paint Outside the Box and Filter). POBF synthesizes images by inpainting outside the box, tackling a label misalignment issue encountered in previous works. Furthermore, POBF leverages an innovative filtering scheme to select the most effective training data. This scheme combines a hardness score and an overfitting score, balanced by a penalty term. Extensive experiments across four benchmark datasets demonstrate that POBF consistently improves performance, achieving an average gain of 5.83% over the real-data-only method and outperforming leading baselines by 2.29%-3.85% in accuracy. Additionally, we validate the robustness and generalizability of POBF across various generative models, training data sizes, and model architectures.

Zilin Du, Haoxin Li, Jianfei Yu +1

Nanyang Technological University Singapore · Nanjing University of Science and Technology China

Vision-Language GroundingTraining Data SelectionSynthetic Data Augmentation
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