Vision-Language Retrieval

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8 papers in the last four weeks, up 14% on the four weeks before. 0.1% of all new papers.

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Latest papers 62

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VCG: A Multimodal Retrieval Framework for E-Commerce Video Feeds under Extreme Cold-Start Conditions
Jun 17, 2026Katya Mirylenka, Egor Malykh, Mahdyar Ravanbakhsh +10Multimodal IR Cold-Start Recommendation
TU Wien, Vienna, Austria · Zalando Switzerland AG, Zurich, Switzerland · Zalando SE, Berlin, Germany
ReSiReg: Towards Spatially Consistent Semantics in Language-Conditioned Robotic Tasks
Jun 17, 2026Simon Schwaiger, David Seyser, Alessandro Scherl +2Vision-Language Models Vision-Language Grounding
Graz University of Technology, Institute of Software Engineering and Artificial Intelligence · University of Applied Sciences Technikum Wien, Department of Industrial Engineering · 1Graz University of Technology, Institute of Software Engineering and Artificial Intelligence +2
DREAM: Extending Vision-Language Models with Dual-Objective Encoding for Cross-Modal Retrieval
Jun 17, 2026Kaleem Ullah, Altaf Hussain, Muhammad Munsif +1Cross-Modal Retrieval Video Representation Learning
Sejong University, Seoul 143-747, South Korea · KAIST InnoCORE PRISM-AI Center, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea · Ulsan National Institute of Science and Technology (UNIST), Ulsan, 44919, Republic of Korea
LARE: Low-Attention Region Encoding for Text-Image Retrieval
Jun 17, 2026Abdulmalik Alquwayfili, Faisal Almeshal, Jumanah Almajnouni +8Visual Representation Learning Fine-Grained Image Retrieval
Saudi Data and Artificial Intelligence Authority (SDAIA), Riyadh, Saudi Arabia.
FARM: Find Anything using Relational Spatial Memory
Jun 13, 2026Siming He, Leo Huang, Adam Lilja +6Visual Spatial Reasoning Relational Reasoning
1UC Berkeley · 2Stanford University
Beyond Scalar Distances: Semantic Attribute Gradients from Frozen MLLMs for Visual Embeddings
Jun 13, 2026Shubhang Bhatnagar, Dheeraj Baiju, Narendra AhujaVisual Representation Learning Fine-Grained Image Retrieval
University of Illinois Urbana-Champaign
MAVIS: Multi-Agent Video Retrieval via Structured Video Understanding
Jun 8, 2026Jie Zhang, Qilang Ye, Hao Zhou +2Video Understanding Multi-Agent Collaboration
School of Computing and Information Technology, Great Bay University · College of Computer Science, Nankai University · Tsinghua Shenzhen International Graduate School, Tsinghua University +1
Driving Video Retrieval for Complex Queries with Structured Grounding
Jun 8, 2026Manyi Yao, Sparsh Garg, Christian Shelton +2Autonomous Driving Vision-Language Retrieval
University of California, Riverside · NEC Laboratories, America
Decoupling Semantics and Logic: A Training-Free Coarse-to-Fine Pipeline for Video Retrieval-Augmented Generation
Jun 6, 2026Jiaxin Dai, Zehang Wei, Jiamin Yan +1Multimodal RAG Agentic RAG
School of AI and Automation, Huazhong University of Science and Technology, China · School of Computer Science & Tech, Huazhong University of Science and Technology
TEVI: Text-Conditioned Editing of Visual Representations via Sparse Autoencoders for Improved Vision-Language Alignment
Jun 5, 2026Sweta Mahajan, Sukrut Rao, Jiahao Xie +2Vision-Language Retrieval Vision-Language Alignment
Max Planck Institute for Informatics, Saarland Informatics Campus, Saarbrücken, Germany · Department of Language Science and Technology, Saarland University, Saarbrücken, Germany
VidMsg: A Benchmark for Implicit Message Inference in Short Videos
Jun 2, 2026Issar Tzachor, Michael Green, Rami Ben-AriVLM Evaluation Video QA
OriginAI, Israel
Reason-Then-Retrieve for CoVR-R with Structured Edit Prompts and Dense-Sparse Fusion
Jun 1, 2026DongQing Liu, MengShi Qi, HongWei JiCross-Modal Retrieval Video Reasoning
Beijing University of Posts and Telecommunications
Dual-Route Top-K Retrieval with 1v1 VLM Reranking for the CoVR-R
May 31, 2026Yuyang Sun, Yongliang Wu, Xingyu Zhu +8Multimodal Reranking Cross-Modal Retrieval
Southeast University · National University of Singapore · Independent Researcher +2
Can Retrieval Heads See Images? Multimodal Retrieval Heads in Long-Context Vision-Language Models
May 26, 2026Aaron Branson Cigres Li, Zhaowei Wang, Yu Zhao +9Vision-Language Models VLM Interpretability
1HKUST · University of Edinburgh · 4CUHK +2
Supervised Classification Heads as Semantic Prototypes: Unlocking Vision-Language Alignment via Weight Recycling
May 21, 2026David Méndez, Roberto Confalonieri, Natalia Díaz RodríguezCross-Modal Learning VLM Adaptation
Department of Computer Science and Artificial Intelligence, DaSCI Institute, University of Granada, Granada, Spain · Department of Mathematics “Tullio Levi-Civita”, University of Padova, Padova, Italy.
USV: Towards Understanding the User-generated Short-form Videos
May 20, 2026Haoyue Cheng, Su Xu, Liwei Jin +3Vision-Language Retrieval
State Key Laboratory for Novel Software Technology, Nanjing University, China · SenseTime Research
What Matters for Grocery Product Retrieval with Open Source Vision Language Models
May 18, 2026Emmanuel G. Maminta, Rowel O. AtienzaVLM Evaluation Fine-Grained Image Retrieval
AI Graduate Program, University of the Philippines, Diliman, Quezon City · EEEI, University of the Philippines, Diliman, Quezon City
Memory-Augmented Query Intent Understanding for Efficient Chat-based Image Retrieval
May 17, 2026Xianke Chen, Daizong Liu, Yushuo Lou +5Vision-Language Retrieval Image Retrieval
School of Computer Science and Technology, and the School of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou 310035, China · the School of Computer Science and Technology, Zhejiang Gongshang University, and the Zhejiang Key Laboratory of Big Data and Future E-Commerce Technology, Hangzhou 310035, China
Neuroscience-inspired Staged Representation Learning with Disentangled Coarse- and Fine-Grained Semantics for EEG Visual Decoding
May 16, 2026Xiang Gao, Hui Tian, Yanming Zhu +2Disentangled Representation Learning Cross-Modal Representation Learning
School of Information and Communication Technology, Griffith University, Parklands Drive, Southport, 4222, QLD, Australia
GeoFlowVLM: Geometry-Aware Joint Uncertainty for Frozen Vision-Language Embedding
May 13, 2026Mayank Nautiyal, Li Ju, Andreas Hellander +2Cross-Modal Learning Flow Matching
Department of Information Technology, Uppsala University, Uppsala, Sweden · SciLifeLab, Uppsala University, Uppsala, Sweden
From Clouds to Hallucinations: Atmospheric Retrieval Hijacking in Remote Sensing Vision-Language RAG
May 8, 2026Jiaju Han, Chao Li, Chengyin Hu +8Multimodal RAG Retrieval-Augmented Generation
China University of Petroleum, Beijing at Karamay, Karamay, Xinjiang 834000, China
Zero-Shot Satellite Image Retrieval through Joint Embeddings: Application to Crisis Response
May 6, 2026James Walsh, William Fawcett, Grace Colverd +1Geospatial Foundation Models Disaster Response
1Trillium Technologies · University of Cambridge · 3Universidad de Antioquia
Open-SAT: LLM-Guided Query Embedding Refinement for Open-Vocabulary Object Retrieval in Satellite Imagery
May 6, 2026Md Adnan Arefeen, Biplob Debnath, Ravi K. Rajendran +2Remote Sensing Image Understanding Image-Text Retrieval
NEC Laboratories America Princeton, NJ, USA
Interactive Multi-Turn Retrieval for Health Videos
May 2, 2026Chengzheng Wu, Ke Qiu, Baoming Zhang +3Healthcare Cross-Modal Retrieval
Wuxi Dipont School of Arts and Science · Malou Tech · Chongqing University +2
UniCVR: From Alignment to Reranking for Unified Zero-Shot Composed Visual Retrieval
Apr 22, 2026Haokun Wen, Xuemeng Song, Haoyu Zhang +3Cross-Modal Learning Learning to Rank
Harbin Institute of Technology (Shenzhen) Shenzhen, China · Southern University of Science and Technology Shenzhen, China · Pengcheng Laboratory Shenzhen, China +1
Towards Robust Text-to-Image Person Retrieval: Multi-View Reformulation for Semantic Compensation
Apr 20, 2026Chao Yuan, Yujian Zhao, Haoxuan Xu +1VLM Robustness Vision-Language Retrieval
Beihang University
KIRA: Knowledge-Intensive Image Retrieval and Reasoning Architecture for Specialized Visual Domains
Apr 18, 2026Parthaw Goswami, Jaynto Goswami DeepMultimodal RAG Visual Reasoning
University of Missouri · SAP Prague
CLAY: Conditional Visual Similarity Modulation in Vision-Language Embedding Space
Apr 13, 2026Sohwi Lim, Lee Hyoseok, Jungjoon Park +1Vision-Language Retrieval
KAIST
LaVPR: Benchmarking Language and Vision for Place Recognition
Feb 3, 2026Ofer Idan, Dan Badur, Yosi Keller +1Multimodal Robustness Visual Place Recognition
Faculty of Engineering, Bar-Ilan University cccccbkvingvvnljreflvddntlcirvicklffknccbbue Ramat Gan, Israel
R3G: A Reasoning-Retrieval-Reranking Framework for Vision-Centric Answer Generation
Jan 25, 2026Zhuohong Chen, Zhengxian Wu, Zirui Liao +6Multimodal RAG Visual Question Answering
1The Shenzhen International Graduate School, Tsinghua University, China · School of Computer Science and Information Engineering, Hefei University of Technology, China · 2State Key Laboratory of Nuclear Power Safety Technology and Equipment, China

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VCG: A Multimodal Retrieval Framework for E-Commerce Video Feeds under Extreme Cold-Start Conditions

Katya Mirylenka, Egor Malykh, Mahdyar Ravanbakhsh, Michael Gygli+9

TU Wien, Vienna, Austria · Zalando Switzerland AG, Zurich, Switzerland · Zalando SE, Berlin, Germany

Multimodal IR Cold-Start Recommendation Recommender Systems Vision-Language Retrieval Multimodal Recommendation Position Bias

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The digital commerce landscape is shifting from static, search-driven catalogs to dynamic, immersive video feeds. This transition introduces an ``extreme cold-start'' problem: unlike traditional items, new short-form videos lack the dense interaction history required for collaborative filtering. Furthermore, immersive feeds introduce strong position and duration biases that distort standard engagement signals. In this paper, we demonstrate the Video Candidate Generation (VCG) system, a scalable multimodal retrieval engine designed to solve these challenges in a large-scale e-commerce environment. By leveraging a domain-adapted vision-language model (based on CLIP), we map users and videos into a shared semantic space, enabling zero-shot retrieval based on visual content rather than behavioral history. We detail the system's architecture and present a rigorous evaluation comparing generative (LLM) vs. discriminative (CLIP) embeddings. Our results show that while generative models excel at attribute prediction, they suffer from embedding space collapse in retrieval tasks. Online A/B testing demonstrates that VCG effectively mitigates engagement biases, yielding a 50% uplift in deep video completion. To showcase the system's capabilities, we present an interactive demonstration featuring three bi-directional retrieval scenarios: Product-to-Video, Video-to-Product, and Zero-Shot Semantic Search.

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