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Vision-DeepResearch Benchmark: Rethinking Visual and Textual Search for Multimodal Large Language Models
arXiv – CS AI|Yu Zeng, Wenxuan Huang, Zhen Fang, Shuang Chen, Yufan Shen, Yishuo Cai, Xiaoman Wang, Zhenfei Yin, Lin Chen, Zehui Chen, Shiting Huang, Yiming Zhao, Xu Tang, Yao Hu, Philip Torr, Wanli Ouyang, Shaosheng Cao||4 views
🤖AI Summary
Researchers introduce Vision-DeepResearch Benchmark (VDR-Bench) with 2,000 VQA instances to better evaluate multimodal AI systems' visual and textual search capabilities. The benchmark addresses limitations in existing evaluations where answers could be inferred without proper visual search, and proposes a multi-round cropped-search workflow to improve model performance.
Key Takeaways
- →VDR-Bench comprises 2,000 carefully curated VQA instances designed to test real-world visual-textual search capabilities.
- →Existing benchmarks fail to properly evaluate visual search as answers are often leaked through textual cues or prior knowledge.
- →Current evaluation scenarios are overly idealized with image searches relying on near-exact matching rather than complex visual reasoning.
- →A new multi-round cropped-search workflow is proposed to improve multimodal AI performance in realistic visual retrieval tasks.
- →The benchmark provides practical guidance for designing future multimodal deep-research systems under realistic conditions.
Read Original →via arXiv – CS AI
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