AI RESEARCH
Can Textual Reasoning Improve the Performance of MLLMs on Fine-grained Visual Classification?
arXiv CS.CV
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ArXi:2601.06993v2 Announce Type: replace Multi-modal large language models (MLLMs) exhibit strong general-purpose capabilities, yet still struggle on Fine-Grained Visual Classification (FGVC), a core perception task that requires subtle visual discrimination and is crucial for many real-world applications. A widely adopted strategy for boosting performance on challenging tasks such as math and coding is Chain-of-Thought (CoT) reasoning. However, several prior works have reported that CoT can actually harm performance on visual perception tasks.