69 citations · 133 across the 34 of their papers we have counts for
41 papers
Unlocking the Potential of Vision Foundation Models for Multi-Task Dense Prediction via Mixture of Task Adapters
Yunzhi Zhuge, Xinzhuo Yu, Sitong Gong +3
Multi-task dense prediction seeks a single, coherent representation that supports simultaneously discriminative semantic recognition and regularized geometric/structural inference.…
MMCoT: Empowering 3D Scene Understanding with 3D-Text Interleaved Multi-Modal Chain-of-Thought Reasoning
Yongqi Shan, Yifan Wang, Yunzhi Zhuge +2
Understanding and reasoning over 3D scenes from point clouds is essential for a broad range of AI applications. Recent 3D large language models have shown promising results, yet th…
Spectral Invariant Fusion and Refinement for Domain Generalized Semantic Segmentation
Yansong Bai, Yunzhi Zhuge, Xinzhuo Yu +4
Domain generalized semantic segmentation is challenging because it requires accurate dense prediction on unseen domains without any access to target-domain data during training. Ex…
Plan-Bench: Can Existing VLMs Go Well Beyond Perception to Reason and Plan in Embodied Worlds?
Juntong Feng, Qingan Liu, Jingru Dong +8
Vision-Language Models (VLMs) have achieved strong performance in visual perception and spatial reasoning, leading to growing interest in using them as high-level planners for embo…
Game2Real-Bench: Do Toy Games Reveal and Transfer to Real-World Visual Reasoning?
Qingan Liu, Juntong Feng, Yue Zhu +6
Visual reasoning is an important capability for vision-language models (VLMs) to understand visual evidence, spatial structure, constraints, and state changes. Existing evaluations…
BeatDance: Generating Beat-Consistent 3D Dance with Hierarchical Spatial-Temporal Modeling
Xiaojian Shen, Dahu Shi, Jianrong Zhang +7
Generating realistic 3D dance from music is a challenging task that requires accurate synchronization with musical rhythms while capturing the spatial complexity of human motion. A…
When to Retrieve, When to Stay: Uncertainty-Aware Temporal Evidence Allocation for Streaming Video-LLMs
Xiang Hu, Jiazuo Yu, Lu Zhang +2
Streaming video understanding requires Video Large Language Models (Video-LLMs) to reason over continuous visual streams under causal constraints. As the visual history grows, a bo…
WorldGuide: Learning Success-Failure Boundaries in Latent World Models for Vision-Language-Action Policies
Lin Liu, Lu Zhang, Ziying Song +6
Latent world models offer a promising way to improve Vision-Language-Action policies by capturing the consequences of actions. However, models trained primarily on expert demonstra…
TRACE: Trajectory-robust Admission with Evidence Ordering for Efficient GUI Agents
Yuhao Wang, Mu Qiao, Xindong Zhang +3
GUI agents accumulate high-resolution screenshots as the trajectory unfolds, increasing inference latency and memory usage. Training-free visual token pruning can reduce this cost,…
Paths: Prompt-aware Spatio-temporal Transformer with Hierarchical Multi-modal Fusion for RGB-Event Video Person Re-Identification
Yakun Huo, Yingquan Wang, Yangyang Liu +4
RGB-Event Video Person Re-Identification (RE-VReID) aims to retrieve specific person across non-overlapping cameras with complementary RGB videos and event streams. However, existi…
AnyTrack: Unifying Visual Object Tracking with Any Modalities
Hao Li, Yunzhi Zhuge, Wenning Hao +4
Visual object tracking aims to continuously locate specific targets within sequential frames, evolving from single-modal methods to multi-modal ones. However, existing multi-modal…
GROVE: Growing and Reasoning over Temporally Stratified Memory from Streaming Video Experience
Sitong Gong, Caixin Kang, Tianyu Yan +7
A wearable assistant should both answer questions about its visual history and recognize when that history is useful to the present situation. Existing video-memory systems primari…
ERA: Entropy-Guided Visual Token Pruning with Rectified Attention for Efficient MLLMs
Yuhao Wang, Mu Qiao, Haiwen Diao +5
Multimodal Large Language Models (MLLMs) incur prohibitive inference costs due to long visual token sequences. Training-free visual token reduction provides an efficient solution.…
Progressive Online Video Understanding with Evidence-Aligned Timing and Transparent Decisions
Kecheng Zhang, Zongxin Yang, Mingfei Han +6
Visual agents operating in the wild must respond to queries precisely when sufficient evidence first appears in a video stream, a critical capability that is overlooked by conventi…
VISTA-Bench: Do Vision-Language Models Really Understand Visualized Text as Well as Pure Text?
Qing'an Liu, Juntong Feng, Yuhao Wang +6
Vision-Language Models (VLMs) have achieved impressive performance in cross-modal understanding across textual and visual inputs, yet existing benchmarks predominantly focus on pur…
Towards Cross-Platform Generalization: Domain Adaptive 3D Detection with Augmentation and Pseudo-Labeling
Xiyan Feng, Wenbo Zhang, Lu Zhang +3
This technical report represents the award-winning solution to the Cross-platform 3D Object Detection task in the RoboSense2025 Challenge. Our approach is built upon PVRCNN++, an e…
The RoboSense Challenge: Sense Anything, Navigate Anywhere, Adapt Across Platforms
Lingdong Kong, Shaoyuan Xie, Zeying Gong +135
Autonomous systems are increasingly deployed in open and dynamic environments -- from city streets to aerial and indoor spaces -- where perception models must remain reliable under…
Parameter Aware Mamba Model for Multi-task Dense Prediction
Xinzhuo Yu, Yunzhi Zhuge, Sitong Gong +3
Understanding the inter-relations and interactions between tasks is crucial for multi-task dense prediction. Existing methods predominantly utilize convolutional layers and attenti…
VFXMaster: Unlocking Dynamic Visual Effect Generation via In-Context Learning
Baolu Li, Yiming Zhang, Qinghe Wang +8
Visual effects (VFX) are crucial to the expressive power of digital media, yet their creation remains a major challenge for generative AI. Prevailing methods often rely on the one-…
FineRS: Fine-grained Reasoning and Segmentation of Small Objects with Reinforcement Learning
Lu Zhang, Jiazuo Yu, Haomiao Xiong +4
Multi-modal Large Language Models (MLLMs) have shown remarkable capabilities across a wide range of vision-language tasks. However, due to the restricted input resolutions, MLLMs f…
Complementary and Contrastive Learning for Audio-Visual Segmentation
Sitong Gong, Yunzhi Zhuge, Lu Zhang +2
Audio-Visual Segmentation (AVS) aims to generate pixel-wise segmentation maps that correlate with the auditory signals of objects. This field has seen significant progress with num…
Layout-Conditioned Autoregressive Text-to-Image Generation via Structured Masking
Zirui Zheng, Takashi Isobe, Tong Shen +12
Although autoregressive (AR) models have demonstrated remarkable success in image generation, extending these models to layout-conditioned generation remains challenging due to the…
Reinforcing Video Reasoning Segmentation to Think Before It Segments
Sitong Gong, Lu Zhang, Yunzhi Zhuge +3
Video reasoning segmentation (VRS) endeavors to delineate referred objects in videos guided by implicit instructions that encapsulate human intent and temporal logic. Previous appr…
Regularizing Subspace Redundancy of Low-Rank Adaptation
Yue Zhu, Haiwen Diao, Shang Gao +8
Low-Rank Adaptation (LoRA) and its variants have delivered strong capability in Parameter-Efficient Transfer Learning (PETL) by minimizing trainable parameters and benefiting from…