5 citations · 6 across the 8 of their papers we have counts for
9 papers
DTP: A Simple yet Effective Distracting Token Pruning Framework for Vision-Language Action Models
Chenyang Li, Jieyuan Liu, Bin Li +5
Vision-Language Action (VLA) models have shown remarkable progress in robotic manipulation by leveraging the powerful perception abilities of Vision-Language Models (VLMs) to under…
CPCLDETECTOR: Knowledge Enhancement and Alignment Selection for Chinese Patronizing and Condescending Language Detection
Jiaxun Yang, Yifei Han, Long Zhang +5
Chinese Patronizing and Condescending Language (CPCL) is an implicitly discriminatory toxic speech targeting vulnerable groups on Chinese video platforms. The existing dataset lack…
Benchmarking Vision-Language Models on Chinese Ancient Documents: From OCR to Knowledge Reasoning
Haiyang Yu, Yuchuan Wu, Fan Shi +18
Chinese ancient documents, invaluable carriers of millennia of Chinese history and culture, hold rich knowledge across diverse fields but face challenges in digitization and unders…
HiCoLoRA: Addressing Context-Prompt Misalignment via Hierarchical Collaborative LoRA for Zero-Shot DST
Shuyu Zhang, Yifan Wei, Xinru Wang +5
Zero-shot Dialog State Tracking (zs-DST) is essential for enabling Task-Oriented Dialog Systems (TODs) to generalize to new domains without costly data annotation. A central challe…
MVCL-DAF++: Enhancing Multimodal Intent Recognition via Prototype-Aware Contrastive Alignment and Coarse-to-Fine Dynamic Attention Fusion
Haofeng Huang, Yifei Han, Long Zhang +3
Multimodal intent recognition (MMIR) suffers from weak semantic grounding and poor robustness under noisy or rare-class conditions. We propose MVCL-DAF++, which extends MVCL-DAF wi…
See the Forest and the Trees: A Synergistic Reasoning Framework for Knowledge-Based Visual Question Answering
Junjie Wang, Yunhan Tang, Yijie Wang +4
Multimodal Large Language Models (MLLMs) have pushed the frontiers of Knowledge-Based Visual Question Answering (KBVQA), yet their reasoning is fundamentally bottlenecked by a reli…
ReGraP-LLaVA: Reasoning enabled Graph-based Personalized Large Language and Vision Assistant
Yifan Xiang, Zhenxi Zhang, Bin Li +6
Multimodal Large Language Models have shown strong performance across multimodal tasks, and recent personalized MLLMs can recognize user-specific concepts and generate contextual c…
MTVQA: Benchmarking Multilingual Text-Centric Visual Question Answering
Jingqun Tang, Qi Liu, Yongjie Ye +14
Text-Centric Visual Question Answering (TEC-VQA) in its proper format not only facilitates human-machine interaction in text-centric visual environments but also serves as a de fac…
TextSquare: Scaling up Text-Centric Visual Instruction Tuning
Jingqun Tang, Chunhui Lin, Zhen Zhao +15
Text-centric visual question answering (VQA) has made great strides with the development of Multimodal Large Language Models (MLLMs), yet open-source models still fall short of lea…