24 citations · 65 across the 38 of their papers we have counts for
12 papers · 1 filter
Text-guided Controllable Diffusion for Realistic Camouflage Images Generation
Yuhang Qian, Haiyan Chen, Wentong Li +2
Camouflage Images Generation (CIG) is an emerging research area that focuses on synthesizing images in which objects are harmoniously blended and exhibit high visual consistency wi…
GA2-CLIP: Generic Attribute Anchor for Efficient Prompt Tuningin Video-Language Models
Bin Wang, Ruotong Hu, Wentong Li +5
Visual and textual soft prompt tuning can effectively improve the adaptability of Vision-Language Models (VLMs) in downstream tasks. However, fine-tuning on video tasks impairs the…
PixelRefer: A Unified Framework for Spatio-Temporal Object Referring with Arbitrary Granularity
Yuqian Yuan, Wenqiao Zhang, Xin Li +6
Multimodal large language models (MLLMs) have demonstrated strong general-purpose capabilities in open-world visual comprehension. However, most existing MLLMs primarily focus on h…
MUVR: A Multi-Modal Untrimmed Video Retrieval Benchmark with Multi-Level Visual Correspondence
Yue Feng, Jinwei Hu, Qijia Lu +11
We propose the Multi-modal Untrimmed Video Retrieval task, along with a new benchmark (MUVR) to advance video retrieval for long-video platforms. MUVR aims to retrieve untrimmed vi…
Point2RBox-v3: Self-Bootstrapping from Point Annotations via Integrated Pseudo-Label Refinement and Utilization
Teng Zhang, Ziqian Fan, Mingxin Liu +8
Driven by the growing need for Oriented Object Detection (OOD), learning from point annotations under a weakly-supervised framework has emerged as a promising alternative to costly…
Tailored Teaching with Balanced Difficulty: Elevating Reasoning in Multimodal Chain-of-Thought via Prompt Curriculum
Xinglong Yang, Quan Feng, Zhongying Pan +7
The effectiveness of Multimodal Chain-of-Thought (MCoT) prompting is often limited by the use of randomly or manually selected examples. These examples fail to account for both mod…
EOC-Bench: Can MLLMs Identify, Recall, and Forecast Objects in an Egocentric World?
Yuqian Yuan, Ronghao Dang, Long Li +8
The emergence of multimodal large language models (MLLMs) has driven breakthroughs in egocentric vision applications. These applications necessitate persistent, context-aware under…
PointLoRA: Low-Rank Adaptation with Token Selection for Point Cloud Learning
Song Wang, Xiaolu Liu, Lingdong Kong +6
Self-supervised representation learning for point cloud has demonstrated effectiveness in improving pre-trained model performance across diverse tasks. However, as pre-trained mode…
OrderChain: Towards General Instruct-Tuning for Stimulating the Ordinal Understanding Ability of MLLM
Jinhong Wang, Shuo Tong, Jian liu +7
Despite the remarkable progress of multimodal large language models (MLLMs), they continue to face challenges in achieving competitive performance on ordinal regression (OR; a.k.a.…
Uncertainty-Instructed Structure Injection for Generalizable HD Map Construction
Xiaolu Liu, Ruizi Yang, Song Wang +3
Reliable high-definition (HD) map construction is crucial for the driving safety of autonomous vehicles. Although recent studies demonstrate improved performance, their generalizat…
Inst3D-LMM: Instance-Aware 3D Scene Understanding with Multi-modal Instruction Tuning
Hanxun Yu, Wentong Li, Song Wang +2
Despite encouraging progress in 3D scene understanding, it remains challenging to develop an effective Large Multi-modal Model (LMM) that is capable of understanding and reasoning…
VideoRefer Suite: Advancing Spatial-Temporal Object Understanding with Video LLM
Yuqian Yuan, Hang Zhang, Wentong Li +9
Video Large Language Models (Video LLMs) have recently exhibited remarkable capabilities in general video understanding. However, they mainly focus on holistic comprehension and st…