4 citations · 24 across the 56 of their papers we have counts for
42 papers · 1 filter
Metric-Bench: Exploring In-context Spatial Metric Reasoning in VLMs for Indoor Scenes
Yuling Xi, Haokai Zhang, Muzhi Zhu +10
Metric reasoning is a critical and challenging task for Vision Language Models (VLMs), playing a pivotal role in embodied AI tasks such as robotic manipulation and autonomous navig…
Can We Defend Against AI-Generated Video Attacks on Real-World Crisis Events? A Systematic Evaluation of Detectors, Generators and Social Dissemination
Shuo Liang, Yixing Ma, Pengfei Zhou +33
Recent video generators can fabricate realistic depictions of wars, disasters, public emergencies, and other real-world crises, creating substantial risks of misinformation. Existi…
MemDreamer: Decoupling Perception and Reasoning for Long Video Understanding via Hierarchical Graph Memory and Agentic Retrieval Mechanism
Cong Chen, Guo Gan, Kaixiang Ji +7
Current Vision-Language Models struggle with hours-long videos because processing full-length visual sequences induces prohibitive token explosion and attention dilution. To overco…
Eliciting Complex Spatial Reasoning in MLLMs through Wide-Baseline Matching
Hao Zhong, Muzhi Zhu, Shenyan Zeng +8
Wide-baseline matching (WBM) requires integrating geometric understanding, viewpoint changes, fine-grained perception, and occlusion reasoning, making it a challenging testbed for…
MARBLE: Multi-Aspect Reward Balance for Diffusion RL
Canyu Zhao, Hao Chen, Yunze Tong +3
Reinforcement learning fine-tuning has become the dominant approach for aligning diffusion models with human preferences. However, assessing images is intrinsically a multi-dimensi…
OmniJigsaw: Enhancing Omni-Modal Reasoning via Modality-Orchestrated Reordering
Yiduo Jia, Muzhi Zhu, Hao Zhong +7
To extend the reinforcement learning post-training paradigm to omni-modal models for concurrently bolstering video-audio understanding and collaborative reasoning, we propose OmniJ…
AGILE: Hand-Object Interaction Reconstruction from Video via Agentic Generation
Jin-Chuan Shi, Binhong Ye, Tao Liu +6
Reconstructing dynamic hand-object interactions from monocular videos is critical for dexterous manipulation data collection and creating realistic digital twins for robotics and V…
Preserving Source Video Realism: High-Fidelity Face Swapping for Cinematic Quality
Zekai Luo, Zongze Du, Zhouhang Zhu +7
Video face swapping is crucial in film and entertainment production, where achieving high fidelity and temporal consistency over long and complex video sequences remains a signific…
Unified Open-World Segmentation with Multi-Modal Prompts
Yang Liu, Yufei Yin, Chenchen Jing +7
In this work, we present COSINE, a unified open-world segmentation model that consolidates open-vocabulary segmentation and in-context segmentation with multi-modal prompts (e.g.,…
GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert
Mingyu Liu, Zheng Huang, Xiaoyi Lin +6
Vision-language models demonstrate strong reasoning and planning abilities, yet grounding these predictions into precise robot actions remains a central challenge. Existing Vision-…
HieraTok: Multi-Scale Visual Tokenizer Improves Image Reconstruction and Generation
Cong Chen, Ziyuan Huang, Cheng Zou +6
In this work, we present HieraTok, a novel multi-scale Vision Transformer (ViT)-based tokenizer that overcomes the inherent limitation of modeling single-scale representations. Thi…
Towards Efficient Pixel Labeling for Industrial Anomaly Detection and Localization
Jingqi Wu, Hanxi Li, Lin Yuanbo Wu +3
Industrial product inspection is often performed using Anomaly Detection (AD) frameworks trained solely on non-defective samples. Although defective samples can be collected during…
Tinker: Diffusion's Gift to 3D--Multi-View Consistent Editing From Sparse Inputs without Per-Scene Optimization
Canyu Zhao, Xiaoman Li, Tianjian Feng +3
We introduce Tinker, a versatile framework for high-fidelity 3D editing that operates in both one-shot and few-shot regimes without any per-scene finetuning. Unlike prior technique…
Generative Video Matting
Yongtao Ge, Kangyang Xie, Guangkai Xu +6
Video matting has traditionally been limited by the lack of high-quality ground-truth data. Most existing video matting datasets provide only human-annotated imperfect alpha and fo…
Self-Navigated Residual Mamba for Universal Industrial Anomaly Detection
Hanxi Li, Jingqi Wu, Lin Yuanbo Wu +4
In this paper, we propose Self-Navigated Residual Mamba (SNARM), a novel framework for universal industrial anomaly detection that leverages ``self-referential learning'' within te…
SurfaceSplat: Connecting Surface Reconstruction and Gaussian Splatting
Zihui Gao, Jia-Wang Bian, Guosheng Lin +2
Surface reconstruction and novel view rendering from sparse-view images are challenging. Signed Distance Function (SDF)-based methods struggle with fine details, while 3D Gaussian…
Revisiting Depth Representations for Feed-Forward 3D Gaussian Splatting
Duochao Shi, Weijie Wang, Donny Y. Chen +4
Depth maps are widely used in feed-forward 3D Gaussian Splatting (3DGS) pipelines by unprojecting them into 3D point clouds for novel view synthesis. This approach offers advantage…
Omni-R1: Reinforcement Learning for Omnimodal Reasoning via Two-System Collaboration
Hao Zhong, Muzhi Zhu, Zongze Du +6
Long-horizon video-audio reasoning and fine-grained pixel understanding impose conflicting requirements on omnimodal models: dense temporal coverage demands many low-resolution fra…
ACTIVE-o3: Empowering MLLMs with Active Perception via Pure Reinforcement Learning
Muzhi Zhu, Hao Zhong, Canyu Zhao +9
Active vision, also known as active perception, refers to actively selecting where and how to look in order to gather task-relevant information. It is a critical component of effic…
SegAgent: Exploring Pixel Understanding Capabilities in MLLMs by Imitating Human Annotator Trajectories
Muzhi Zhu, Yuzhuo Tian, Hao Chen +5
While MLLMs have demonstrated adequate image understanding capabilities, they still struggle with pixel-level comprehension, limiting their practical applications. Current evaluati…
PerturboLLaVA: Reducing Multimodal Hallucinations with Perturbative Visual Training
Cong Chen, Mingyu Liu, Chenchen Jing +5
This paper aims to address the challenge of hallucinations in Multimodal Large Language Models (MLLMs) particularly for dense image captioning tasks. To tackle the challenge, we id…
A Physical Coherence Benchmark for Evaluating Video Generation Models via Optical Flow-guided Frame Prediction
Yongfan Chen, Xiuwen Zhu, Tianyu Li
Recent advances in video generation models demonstrate their potential as world simulators, but they often struggle with videos deviating from physical laws, a key concern overlook…
DICEPTION: A Generalist Diffusion Model for Visual Perceptual Tasks
Canyu Zhao, Yanlong Sun, Mingyu Liu +6
This paper's primary objective is to develop a robust generalist perception model capable of addressing multiple tasks under constraints of computational resources and limited trai…
REO-VLM: Transforming VLM to Meet Regression Challenges in Earth Observation
Xizhe Xue, Guoting Wei, Hao Chen +4
The rapid evolution of Vision Language Models (VLMs) has catalyzed significant advancements in artificial intelligence, expanding research across various disciplines, including Ear…