activity
20202026
most citedHuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face

265 citations · 360 across the 67 of their papers we have counts for

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13 papers · 1 filter

cs.CV2026

WorldMemBench: A Comprehensive Benchmark for Memory of Video World Models

Nan Zhou, Jianxiong Gao, Qiuyu Wang +10

As video models enable longer and more realistic interactive experiences, maintaining a world consistent with its history becomes essential. This requires both preserving previousl…

cs.CV2026

Perceive-to-Reason: Decoupling Perception and Reasoning for Fine-Grained Visual Reasoning

Hongxing Li, Xiufeng Huang, Dingming Li +11

Fine-grained visual reasoning remains challenging for vision-language models, especially when small but critical visual cues are buried in high-resolution images. Existing approach…

cs.CV2026

SpatialEvo: Self-Evolving Spatial Intelligence via Deterministic Geometric Environments

Dinging Li, Yingxiu Zhao, Xinrui Cheng +16

Spatial reasoning over three-dimensional scenes is a core capability for embodied intelligence, yet continuous model improvement remains bottlenecked by the cost of geometric annot…

cs.CV2026

UI-Zoomer: Uncertainty-Driven Adaptive Zoom-In for GUI Grounding

Fei Tang, Bofan Chen, Zhengxi Lu +8

GUI grounding, which localizes interface elements from screenshots given natural language queries, remains challenging for small icons and dense layouts. Test-time zoom-in methods…

cs.CV2026

Seeing but Not Thinking: Routing Distraction in Multimodal Mixture-of-Experts

Haolei Xu, Haiwen Hong, Hongxing Li +7

Multimodal Mixture-of-Experts (MoE) models have achieved remarkable performance on vision-language tasks. However, we identify a puzzling phenomenon termed Seeing but Not Thinking:…

cs.CV2025

SpatialLadder: Progressive Training for Spatial Reasoning in Vision-Language Models

Hongxing Li, Dingming Li, Zixuan Wang +7

Spatial reasoning remains a fundamental challenge for Vision-Language Models (VLMs), with current approaches struggling to achieve robust performance despite recent advances. We id…

cs.CV2025★ 1 cited

GSM8K-V: Can Vision Language Models Solve Grade School Math Word Problems in Visual Contexts

Fan Yuan, Yuchen Yan, Yifan Jiang +9

Mathematical reasoning is a key capability for vision-language models (VLMs), yet current benchmarks mainly evaluate text-based or explicitly symbolic visual inputs. It remains unc…

cs.CV2025

Test-Time Reinforcement Learning for GUI Grounding via Region Consistency

Yong Du, Yuchen Yan, Fei Tang +5

Graphical User Interface (GUI) grounding, the task of mapping natural language instructions to precise screen coordinates, is fundamental to autonomous GUI agents. While existing m…

cs.CV2025

SVGenius: Benchmarking LLMs in SVG Understanding, Editing and Generation

Siqi Chen, Xinyu Dong, Haolei Xu +10

Large Language Models (LLMs) and Multimodal LLMs have shown promising capabilities for SVG processing, yet existing benchmarks suffer from limited real-world coverage, lack of comp…

cs.CV2025

ViewSpatial-Bench: Evaluating Multi-perspective Spatial Localization in Vision-Language Models

Dingming Li, Hongxing Li, Zixuan Wang +9

Vision-language models (VLMs) have demonstrated remarkable capabilities in understanding and reasoning about visual content, but significant challenges persist in tasks requiring c…

cs.CV2025

2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining

Wenqi Zhang, Hang Zhang, Xin Li +6

Compared to image-text pair data, interleaved corpora enable Vision-Language Models (VLMs) to understand the world more naturally like humans. However, such existing datasets are c…

cs.CV2024

Multimodal Self-Instruct: Synthetic Abstract Image and Visual Reasoning Instruction Using Language Model

Wenqi Zhang, Zhenglin Cheng, Yuanyu He +8

Although most current large multimodal models (LMMs) can already understand photos of natural scenes and portraits, their understanding of abstract images, e.g., charts, maps, or l…

cs.CV2023★ 2 cited

PUMGPT: A Large Vision-Language Model for Product Understanding

Wei Xue, Zongyi Guo, Baoliang Cui +5

E-commerce platforms benefit from accurate product understanding to enhance user experience and operational efficiency. Traditional methods often focus on isolated tasks such as at…