9 citations · 28 across the 47 of their papers we have counts for
18 papers · 1 filter
VisualClaw: A Real-Time, Personalized Agent for the Physical World
Haoqin Tu, Jianwen Chen, Zijun Wang +14
Vision language models are serving as general-purpose interfaces for complex multimodal tasks. However, deployment still faces three gaps: VLMs typically incur high latency and cos…
AgentVista: Evaluating Multimodal Agents in Ultra-Challenging Realistic Visual Scenarios
Zhaochen Su, Jincheng Gao, Hangyu Guo +10
Real-world multimodal agents solve multi-step workflows grounded in visual evidence. For example, an agent can troubleshoot a device by linking a wiring photo to a schematic and va…
SimpleOCR: Rendering Visualized Questions to Teach MLLMs to Read
Yibo Peng, Peng Xia, Ding Zhong +6
Despite the rapid advancements in Multimodal Large Language Models (MLLMs), a critical question regarding their visual grounding mechanism remains unanswered: do these models genui…
Skywork-R1V4: Toward Agentic Multimodal Intelligence through Interleaved Thinking with Images and DeepResearch
Yifan Zhang, Liang Hu, Haofeng Sun +12
Despite recent progress in multimodal agentic systems, existing approaches often treat image manipulation and web search as disjoint capabilities, rely heavily on costly reinforcem…
Knowing the Answer Isn't Enough: Fixing Reasoning Path Failures in LVLMs
Chaoyang Wang, Yangfan He, Yiyang Zhou +6
We reveal a critical yet underexplored flaw in Large Vision-Language Models (LVLMs): even when these models know the correct answer, they frequently arrive there through incorrect…
Agent0-VL: Exploring Self-Evolving Agent for Tool-Integrated Vision-Language Reasoning
Jiaqi Liu, Kaiwen Xiong, Peng Xia +6
Vision-language agents have achieved remarkable progress in a variety of multimodal reasoning tasks; however, their learning remains constrained by the limitations of human-annotat…
IKOD: Mitigating Visual Attention Degradation in Large Vision-Language Models
Jiabing Yang, Chenhang Cui, Yiyang Zhou +6
Recent advancements in Large Vision-Language Models (LVLMs) have demonstrated significant progress across multiple domains. However, these models still face the inherent challenge…
Thinking with Images for Multimodal Reasoning: Foundations, Methods, and Future Frontiers
Zhaochen Su, Peng Xia, Hangyu Guo +12
Recent progress in multimodal reasoning has been significantly advanced by textual Chain-of-Thought (CoT), a paradigm where models conduct reasoning within language. This text-cent…
MJ-VIDEO: Fine-Grained Benchmarking and Rewarding Video Preferences in Video Generation
Haibo Tong, Zhaoyang Wang, Zhaorun Chen +11
Recent advancements in video generation have significantly improved the ability to synthesize videos from text instructions. However, existing models still struggle with key challe…
Neighbor Does Matter: Density-Aware Contrastive Learning for Medical Semi-supervised Segmentation
Feilong Tang, Zhongxing Xu, Ming Hu +6
In medical image analysis, multi-organ semi-supervised segmentation faces challenges such as insufficient labels and low contrast in soft tissues. To address these issues, existing…
MMedPO: Aligning Medical Vision-Language Models with Clinical-Aware Multimodal Preference Optimization
Kangyu Zhu, Peng Xia, Yun Li +3
The advancement of Large Vision-Language Models (LVLMs) has propelled their application in the medical field. However, Medical LVLMs (Med-LVLMs) encounter factuality challenges due…
MMIE: Massive Multimodal Interleaved Comprehension Benchmark for Large Vision-Language Models
Peng Xia, Siwei Han, Shi Qiu +9
Interleaved multimodal comprehension and generation, enabling models to produce and interpret both images and text in arbitrary sequences, have become a pivotal area in multimodal…
OphNet: A Large-Scale Video Benchmark for Ophthalmic Surgical Workflow Understanding
Ming Hu, Peng Xia, Lin Wang +11
Surgical scene perception via videos is critical for advancing robotic surgery, telesurgery, and AI-assisted surgery, particularly in ophthalmology. However, the scarcity of divers…
TP-DRSeg: Improving Diabetic Retinopathy Lesion Segmentation with Explicit Text-Prompts Assisted SAM
Wenxue Li, Xinyu Xiong, Peng Xia +2
Recent advances in large foundation models, such as the Segment Anything Model (SAM), have demonstrated considerable promise across various tasks. Despite their progress, these mod…
Generalizing to Unseen Domains in Diabetic Retinopathy with Disentangled Representations
Peng Xia, Ming Hu, Feilong Tang +6
Diabetic Retinopathy (DR), induced by diabetes, poses a significant risk of visual impairment. Accurate and effective grading of DR aids in the treatment of this condition. Yet exi…
HGCLIP: Exploring Vision-Language Models with Graph Representations for Hierarchical Understanding
Peng Xia, Xingtong Yu, Ming Hu +4
Object categories are typically organized into a multi-granularity taxonomic hierarchy. When classifying categories at different hierarchy levels, traditional uni-modal approaches…
NurViD: A Large Expert-Level Video Database for Nursing Procedure Activity Understanding
Ming Hu, Lin Wang, Siyuan Yan +7
The application of deep learning to nursing procedure activity understanding has the potential to greatly enhance the quality and safety of nurse-patient interactions. By utilizing…
LMPT: Prompt Tuning with Class-Specific Embedding Loss for Long-tailed Multi-Label Visual Recognition
Peng Xia, Di Xu, Ming Hu +2
Long-tailed multi-label visual recognition (LTML) task is a highly challenging task due to the label co-occurrence and imbalanced data distribution. In this work, we propose a unif…