2 citations · 3 across the 10 of their papers we have counts for
6 papers · 1 filter
CrossVid: A Comprehensive Benchmark for Evaluating Cross-Video Reasoning in Multimodal Large Language Models
Jingyao Li, Jingyun Wang, Molin Tan +6
Cross-Video Reasoning (CVR) presents a significant challenge in video understanding, which requires simultaneous understanding of multiple videos to aggregate and compare informati…
LTCA: Long-range Temporal Context Attention for Referring Video Object Segmentation
Cilin Yan, Jingyun Wang, Guoliang Kang
Referring Video Segmentation (RVOS) aims to segment objects in videos given linguistic expressions. The key to solving RVOS is to extract long-range temporal context information fr…
USB-Rec: An Effective Framework for Improving Conversational Recommendation Capability of Large Language Model
Jianyu Wen, Jingyun Wang, Cilin Yan +3
Recently, Large Language Models (LLMs) have been widely employed in Conversational Recommender Systems (CRSs). Unlike traditional language model approaches that focus on training,…
Object-centric Video Question Answering with Visual Grounding and Referring
Haochen Wang, Qirui Chen, Cilin Yan +5
Video Large Language Models (VideoLLMs) have recently demonstrated remarkable progress in general video understanding. However, existing models primarily focus on high-level compre…
Improving the Reasoning of Multi-Image Grounding in MLLMs via Reinforcement Learning
Bob Zhang, Haoran Li, Tao Zhang +5
Multimodal Large Language Models (MLLMs) perform well in single-image visual grounding but struggle with real-world tasks that demand cross-image reasoning and multi-modal instruct…
Rethinking the Global Knowledge of CLIP in Training-Free Open-Vocabulary Semantic Segmentation
Jingyun Wang, Cilin Yan, Guoliang Kang
Recent works modify CLIP to perform open-vocabulary semantic segmentation in a training-free manner (TF-OVSS). In vanilla CLIP, patch-wise image representations mainly encode homog…