Fine-R1 uses chain-of-thought supervised fine-tuning on a structured FGVR reasoning dataset plus triplet augmented policy optimization to outperform general MLLMs and CLIP models on seen and unseen fine-grained categories with 4-shot training.
African or european swallow? bench- marking large vision-language models for fine-grained object classification
3 Pith papers cite this work. Polarity classification is still indexing.
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Introduces FG-BMK benchmark and evaluates twelve LVLMs on fine-grained semantic recognition and feature tasks, identifying influences from training paradigms and perturbation sensitivity.
Longer textual reasoning chains degrade MLLM accuracy on fine-grained visual tasks; a new normalization and constrained-reward training framework mitigates the effect and sets new SOTA numbers.
citing papers explorer
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Fine-R1: Make Multi-modal LLMs Excel in Fine-Grained Visual Recognition by Chain-of-Thought Reasoning
Fine-R1 uses chain-of-thought supervised fine-tuning on a structured FGVR reasoning dataset plus triplet augmented policy optimization to outperform general MLLMs and CLIP models on seen and unseen fine-grained categories with 4-shot training.
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Benchmarking Large Vision-Language Models on Fine-Grained Image Tasks: A Comprehensive Evaluation
Introduces FG-BMK benchmark and evaluates twelve LVLMs on fine-grained semantic recognition and feature tasks, identifying influences from training paradigms and perturbation sensitivity.
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Can Textual Reasoning Improve the Performance of MLLMs on Fine-grained Visual Classification?
Longer textual reasoning chains degrade MLLM accuracy on fine-grained visual tasks; a new normalization and constrained-reward training framework mitigates the effect and sets new SOTA numbers.