24 citations · 65 across the 38 of their papers we have counts for
41 papers
GeoScaffold: Learning Compact Geometric Latents via Reconstruction for Efficient Vision-Language Navigation
Yixuan Jiang, Wentong Li, An Liu +5
Recent vision-and-language navigation (VLN) systems increasingly adopt streaming Video-LLM policies that map egocentric RGB observations and instructions directly to low-level acti…
EmbodiedSkills: World Action Model-augmented Cascaded Skills for Vision-Language-Action Agent
Wei Wang, Wenqiao Zhang, Yutong Lin +14
Vision-language-action (VLA) policies generate executable robot actions, but plausible proposals can have different consequences for the task. We present EmbodiedSkills, a skill-ba…
Explainable Forensics of Manipulated Segments in Untrimmed Long Videos
Yue Feng, Jingjing Li, Qijia Lu +9
The rapid advancement of AI-driven video generation has transformed content creation, while simultaneously increasing the risk of misinformation through localized manipulations in…
InstructSAM: Segment Any Instance with Any Instructions
Yuqian Yuan, Wentong Li, Zhaocheng Li +6
In this paper, we introduce InstructSAM, a unified and streamlined framework designed for multi-instance segmentation under arbitrary instructions. We formulates instruction-driven…
Weighted Reverse Convolution for Feature Upsampling
Wentong Li, Zhiyuan Qi, Zichen Zhao +2
Pre-trained vision foundation models (VFMs) provide strong semantic representations, yet their patch-level features are inherently coarse, limiting their effectiveness on tasks req…
LMMs Meet Object-Centric Vision: Understanding, Segmentation, Editing and Generation
Yuqian Yuan, Wenqiao Zhang, Juekai Lin +7
Large Multimodal Models (LMMs) have achieved remarkable progress in general-purpose vision--language understanding, yet they remain limited in tasks requiring precise object-level…
DecoVLN: Decoupling Observation, Reasoning, and Correction for Vision-and-Language Navigation
Zihao Xin, Wentong Li, Yixuan Jiang +4
Vision-and-Language Navigation (VLN) requires agents to follow long-horizon instructions and navigate complex 3D environments. However, existing approaches face two major challenge…
AgentVLN: Towards Agentic Vision-and-Language Navigation
Zihao Xin, Wentong Li, Yixuan Jiang +6
Vision-and-Language Navigation (VLN) requires an embodied agent to ground complex natural-language instructions into long-horizon navigation in unseen environments. While Vision-La…
Forging Spatial Intelligence: A Roadmap of Multi-Modal Data Pre-Training for Autonomous Systems
Song Wang, Lingdong Kong, Xiaolu Liu +4
The rapid advancement of autonomous systems, including self-driving vehicles and drones, has intensified the need to forge true Spatial Intelligence from multi-modal onboard sensor…
VisionTrim: Unified Vision Token Compression for Training-Free MLLM Acceleration
Hanxun Yu, Wentong Li, Xuan Qu +3
Multimodal large language models (MLLMs) suffer from high computational costs due to excessive visual tokens, particularly in high-resolution and video-based scenarios. Existing to…
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…
Scalable Autoregressive Monocular Depth Estimation
Jinhong Wang, Jian Liu, Dongqi Tang +5
This paper shows that the autoregressive model is an effective and scalable monocular depth estimator. Our idea is simple: We tackle the monocular depth estimation (MDE) task with…
ReliOcc: Towards Reliable Semantic Occupancy Prediction via Uncertainty Learning
Song Wang, Zhongdao Wang, Jiawei Yu +4
Vision-centric semantic occupancy prediction plays a crucial role in autonomous driving, which requires accurate and reliable predictions from low-cost sensors. Although having not…