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From the 1 of 11 linked papers with an AI index.

most citedIdealGPT: Iteratively Decomposing Vision and Language Reasoning via Large Language Models

4 citations · 6 across the 7 of their papers we have counts for

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cs.CV2026

ABot-World-0: Infinite Interactive World Rollout on a Single Desktop GPU

Fan Jiang, Zhaoxu Sun, Mengchao Wang +38

We present ABot-World-0, an action-conditioned video world model for real-time, long-horizon closed-loop interaction, supported by a multi-source data infrastructure spanning AAA g…

cs.CV20264 cited

IdealGPT: Iteratively Decomposing Vision and Language Reasoning via Large Language Models

Haoxuan You, Rui Sun, Zhecan Wang +5

The field of vision-and-language (VL) understanding has made unprecedented progress with end-to-end large pre-trained VL models (VLMs). However, they still fall short in zero-shot…

cs.CV20262 cited

UniFine: A Unified and Fine-grained Approach for Zero-shot Vision-Language Understanding

Rui Sun, Zhecan Wang, Haoxuan You +3

Vision-language tasks, such as VQA, SNLI-VE, and VCR are challenging because they require the model's reasoning ability to understand the semantics of the visual world and natural…

cs.CV2025

Detecting Multimodal Situations with Insufficient Context and Abstaining from Baseless Predictions

Junzhang Liu, Zhecan Wang, Hammad Ayyubi +5

Despite the widespread adoption of Vision-Language Understanding (VLU) benchmarks such as VQA v2, OKVQA, A-OKVQA, GQA, VCR, SWAG, and VisualCOMET, our analysis reveals a pervasive…

cs.CV2025

JourneyBench: A Challenging One-Stop Vision-Language Understanding Benchmark of Generated Images

Zhecan Wang, Junzhang Liu, Chia-Wei Tang +11

Existing vision-language understanding benchmarks largely consist of images of objects in their usual contexts. As a consequence, recent multimodal large language models can perfor…