Fine-Grained Visual Perception

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14 papers in the last four weeks, up 250% on the four weeks before. 0.1% of all new papers.

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Latest papers 111

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Pest-Thinker: Learning to Think and Reason like Entomologists via Reinforcement Learning
May 7, 2026Xueheng Li, Yu Wang, Tao Hu +6Agricultural Image Analysis VLM Reasoning
Institute of Intelligent Machines, Hefei Institute of Physical Science, Chinese Academy of Sciences · University of Science and Technology of China · 4Hefei University of Technology +1
FoodCHA: Multi-Modal LLM Agent for Fine-Grained Food Analysis
May 6, 2026Woojin Lee, Pranav Mekkoth, Ye Tian +2Hierarchical Classification Fine-Grained Image Classification
Department of Computer Science and Engineering University of California, San Diego (UCSD)
SpecPL: Disentangling Spectral Granularity for Prompt Learning
May 6, 2026Jingtao Zhou, Xirui Kang, Feiyang Huang +1VLM Adaptation Frequency-Domain Feature Learning
Department of Electrical Engineering, City University of Hong Kong, Hong Kong SAR.
Chart-FR1: Visual Focus-Driven Fine-Grained Reasoning on Dense Charts
May 3, 2026Hongkun Pan, Yuwei Wu, Wanyi Hong +8Data Visualization Multimodal CoT Reasoning
Zhejiang University · State Key Lab of CAD&CG, Zhejiang University · HiThink Research
The Perceptual Bandwidth Bottleneck in Vision-Language Models: Active Visual Reasoning via Sequential Experimental Design
May 2, 2026Anjie Liu, Ziqin Gong, Yan Song +6VLM Reasoning Visually Grounded Reasoning
The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China · University College London, London, United Kingdom · ShanghaiTech University, Shanghai, China +2
ShapeY: A Principled Framework for Measuring Shape Recognition Capacity via Nearest-Neighbor Matching
Apr 27, 2026Jong Woo Nam, Amanda S. Rios, Bartlett W. MelInvariant Representation Learning Fine-Grained Visual Perception
Understanding Representation Gaps Across Scales in Tropical Tree Species Classification from Drone Imagery
Apr 24, 2026Sulagna Saha, Arthur Ouaknine, Etienne Laliberté +7Representation Learning Tree Species Classification
Mila – Quebec AI Institute · McGill University · Université de Montréal +4
Micro-DualNet: Dual-Path Spatio-Temporal Network for Micro-Action Recognition
Apr 22, 2026Naga VS Raviteja Chappa, Evangelos Sariyanidi, Lisa Yankowitz +4Video Action Recognition Fine-Grained Visual Perception
The Children’s Hospital of Philadelphia, USA · University of Pennsylvania, USA
Geometry-Guided Self-Supervision for Ultra-Fine-Grained Recognition with Limited Data
Apr 21, 2026Shijie Wang, Yadan Luo, Zijian Wang +3Fine-Grained Image Classification Geometric Representation Learning
College of Computer Science and Engineering, Shandong University of Science and Technology, China · School of Information Technology and Electrical Engineering, The University of Queensland, Australia
Divide-and-Conquer Approach to Holistic Cognition in High-Similarity Contexts with Limited Data
Apr 21, 2026Shijie Wang, Zijian Wang, Yadan Luo +3Visual Representation Learning Fine-Grained Visual Perception
Shandong University of Science and Technology, China · The University of Queensland, Australia
PestVL-Net: Enabling Multimodal Pest Learning via Fine-grained Vision-Language Interaction
Apr 19, 2026Xueheng Li, Tao Hu, Ke Cao +5Agricultural Image Analysis Vision-Language Models
Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences · University of Science and Technology of China · 3Zhongke Hefei Institute of Technology Innovation Engineering
Adaptive receptive field-based spatial-frequency feature reconstruction network for few-shot fine-grained image classification
Apr 18, 2026Linyue Zhang, Wenyi Zeng, Zicheng Pan +6Frequency-Domain Feature Learning Fine-Grained Image Classification
School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi’an, Shaanxi Province, China · School of Electrical Engineering and Computer Science, the University of Queensland, QLD, Australia · Institute for Integrated and Intelligent Systems, Griffith University, QLD, Australia +4
Bridging Coarse and Fine Recognition: A Hybrid Approach for Open-Ended Multi-Granularity Object Recognition in Interactive Educational Games
Apr 18, 2026Hanling Yi, Feng Lin, Mao Luo +3Multimodal Large Language Models Fine-Grained Visual Perception
Intellifusion Inc.
UniDoc-RL: Coarse-to-Fine Visual RAG with Hierarchical Actions and Dense Rewards
Apr 16, 2026Jun Wang, Shuo Tan, Zelong Sun +5Document Understanding Multimodal RAG
Glint Lab
Frequency-Enhanced Dual-Subspace Networks for Few-Shot Fine-Grained Image Classification
Apr 16, 2026Meijia Wang, Guochao Wang, Haozhen Chu +4Frequency-Domain Feature Learning Fine-Grained Image Classification
School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi’an 710000, China
Coverage-aware Semantic Representation Learning for Underrepresented Visual Concepts
Feb 18, 2026Sakib Ahammed, Xia Cui, Wenqi Lu +4Representation Learning Visual Representation Learning
Department of Computing and Mathematics, Manchester Metropolitan University The Dalton Building, Chester Street, M1 5GD Manchester
MATANet: A Multi-context Attention and Taxonomy-Aware Network for Fine-Grained Underwater Recognition of Marine Species
Jan 7, 2026Donghwan Lee, Byeongjin Kim, Geunhee Kim +3Fine-Grained Visual Perception Image Classification
Department of Industrial Engineering Yonsei University Seoul 03722, Republic of Korea
RoadBench: Benchmarking MLLMs on Fine-Grained Spatial Understanding and Reasoning under Urban Road Scenarios
Nov 22, 2025Jun Zhang, Xin Zhang, Jie Feng +5VLM Evaluation Spatial Reasoning Benchmarks
Department of Electronic Engineering, BNRist, Tsinghua University, Beijing, China · Zhongguancun Academy · Amap, Alibaba Group, Beijing, China
Training-free Uncertainty Guidance for Complex Visual Tasks with MLLMs
Oct 1, 2025Sanghwan Kim, Rui Xiao, Stephan Alaniz +2Multimodal Large Language Models Long-Video Understanding
1Technical University of Munich · 2Munich Center for Machine Learning · 20263Helmholtz Munich +2
Fine-Grained Open-Vocabulary Object Detection with Fined-Grained Prompts: Task, Dataset and Benchmark
Mar 19, 2025Ying Liu, Yijing Hua, Haojiang Chai +2Open-Vocabulary Object Detection Object Detection
department of software engineering, Northeastern University, China. · His work was done in Articul8 AI.
Phase-Aware Spatial-Frequency Fusion for Few-Shot Fine-Grained Image Classification
Date pendingRuiling Liu, Linyue Zhang, Wenyi Zeng +5Frequency-Domain Feature Learning Fine-Grained Image Classification

May 7, 2026, cs.CVJ/K move · Enter open · S save

Pest-Thinker: Learning to Think and Reason like Entomologists via Reinforcement Learning

Xueheng Li, Yu Wang, Tao Hu, Ji Huang+5

Institute of Intelligent Machines, Hefei Institute of Physical Science, Chinese Academy of Sciences · University of Science and Technology of China · 4Hefei University of Technology+1

Agricultural Image Analysis VLM Reasoning Fine-Grained Visual Perception RL for VLMs

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Pest-induced crop losses pose a major threat to global food security and sustainable agricultural development. While recent advances in Multimodal Large Language Models (MLLMs) have shown strong potential for visual understanding and smart agriculture, their direct application to pest recognition remains limited due to the domain's unique challenges such as high inter-species complexity, intra-species variability, and the scarcity of expert-annotated data. In this work, we introduce Pest-Thinker, a knowledge-driven reinforcement learning (RL) framework that enables MLLMs to reason over fine-grained pest morphology. We first construct two high-definition pest benchmarks, QFSD and AgriInsect, comprising diverse species and expert-annotated morphological traits. Leveraging these datasets, we synthesize Chain-of-Thought (CoT) reasoning trajectories to facilitate structured learning of pest-specific visual cues through Supervised Fine-Tuning (SFT). Subsequently, we employ Group Relative Policy Optimization (GRPO) with a novel feature reward that guides the model to focus on observable morphological evidence, assessed by an LLM-as-a-Judge strategy. Extensive experiments demonstrate that Pest-Thinker substantially improves both in-domain and out-of-domain morphological understanding, marking a step toward expert-level visual reasoning for intelligent agricultural pest analysis. The datasets and source code are available upon acceptance.

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