VLM$^2$-Bench: A Closer Look at How Well VLMs Implicitly Link Explicit Matching Visual Cues

• URL: http://arxiv.org/abs/2502.12084v2
• Date: Mon, 24 Feb 2025 04:56:36 GMT
• Title: VLM$^2$-Bench: A Closer Look at How Well VLMs Implicitly Link Explicit Matching Visual Cues
• Authors: Jianshu Zhang, Dongyu Yao, Renjie Pi, Paul Pu Liang, Yi R. Fung,
• Abstract summary: VLM$2$-Bench is a benchmark designed to assess whether vision-language models can Visually Link Matching cues.<n>We identify critical challenges in models' ability to link visual cues, highlighting a significant performance gap where even GPT-4o lags 34.80% behind humans.
• Score: 34.95077625513563
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visually linking matching cues is a crucial ability in daily life, such as identifying the same person in multiple photos based on their cues, even without knowing who they are. Despite the extensive knowledge that vision-language models (VLMs) possess, it remains largely unexplored whether they are capable of performing this fundamental task. To address this, we introduce VLM$^2$-Bench, a benchmark designed to assess whether VLMs can Visually Link Matching cues, with 9 subtasks and over 3,000 test cases. Comprehensive evaluation across eight open-source VLMs and GPT-4o, along with further analysis of various language-side and vision-side prompting methods, leads to a total of eight key findings. We identify critical challenges in models' ability to link visual cues, highlighting a significant performance gap where even GPT-4o lags 34.80% behind humans. Based on these insights, we advocate for (i) enhancing core visual capabilities to improve adaptability and reduce reliance on prior knowledge, (ii) establishing clearer principles for integrating language-based reasoning in vision-centric tasks to prevent unnecessary biases, and (iii) shifting vision-text training paradigms toward fostering models' ability to independently structure and infer relationships among visual cues.

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