15 citations · 21 across the 23 of their papers we have counts for
26 papers
CoEvoWhen: Policy-Tool Coevolution for Ultra-Long Video Temporal Grounding
Yiduo Jia, Muzhi Zhu, Jinchuan Shi +4
Ultra-long video temporal grounding requires balancing long-range evidence search with fine-grained event understanding under a limited visual budget, yet existing agentic methods…
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…
OmniVChat: Synthesizing, Benchmarking, and Training for Native Audio-Visual Dialogue
Haolin He, Yunfei Chu, Qi Chen +15
We define OmniVChat (Omni Video Chat) as the task of native audio-visual dialogue between a user and an omni model. In OmniVChat, omni models directly and simultaneously receive au…
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…
Where to Look: Can Foundation Models Reach a Target Viewpoint Through Active Exploration?
Liyang Li, Muzhi Zhu, Zhiyue Zhao +5
Humans can reproduce the viewpoint specified by a target image through active head and body motion, yet spatial intelligence in foundation models has largely been studied as passiv…
FLaG: Fine-Grained Latent Grouping for Hallucination Detection
Wentao Ye, Liyao Li, Zhiqing Xiao +6
Hallucinations in large language models (LLMs) arise from heterogeneous failure mechanisms, making reliable detection difficult for any single global uncertainty score. In this wor…
Exploring Spatial Intelligence from a Generative Perspective
Muzhi Zhu, Shunyao Jiang, Huanyi Zheng +9
Spatial intelligence is essential for multimodal large language models, yet current benchmarks largely assess it only from an understanding perspective. We ask whether modern gener…
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…
LLaDA2.1: Speeding Up Text Diffusion via Token Editing
Tiwei Bie, Maosong Cao, Xiang Cao +47
While LLaDA2.0 showcased the scaling potential of 100B-level block-diffusion models and their inherent parallelization, the delicate equilibrium between decoding speed and generati…
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-…
NoTVLA: Semantics-Preserving Robot Adaptation via Narrative Action Interfaces
Zheng Huang, Mingyu Liu, Xiaoyi Lin +9
Vision-Language-Action (VLA) models represent a pivotal advance in embodied intelligence, yet they confront critical barriers to real-world deployment, most notably catastrophic fo…
GUI-Shepherd: Reliable Process Reward and Verification for Long-Sequence GUI Tasks
Cong Chen, Kaixiang Ji, Hao Zhong +9
Autonomous agents for long-sequence Graphical User Interface tasks are hindered by sparse rewards and the intractable credit assignment problem. To address these challenges, we int…
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…
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…
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…
Unleashing the Potential of the Diffusion Model in Few-shot Semantic Segmentation
Muzhi Zhu, Yang Liu, Zekai Luo +5
The Diffusion Model has not only garnered noteworthy achievements in the realm of image generation but has also demonstrated its potential as an effective pretraining method utiliz…
A Simple Image Segmentation Framework via In-Context Examples
Yang Liu, Chenchen Jing, Hengtao Li +4
Recently, there have been explorations of generalist segmentation models that can effectively tackle a variety of image segmentation tasks within a unified in-context learning fram…
Generative Active Learning for Long-tailed Instance Segmentation
Muzhi Zhu, Chengxiang Fan, Hao Chen +4
Recently, large-scale language-image generative models have gained widespread attention and many works have utilized generated data from these models to further enhance the perform…
DiverGen: Improving Instance Segmentation by Learning Wider Data Distribution with More Diverse Generative Data
Chengxiang Fan, Muzhi Zhu, Hao Chen +4
Instance segmentation is data-hungry, and as model capacity increases, data scale becomes crucial for improving the accuracy. Most instance segmentation datasets today require cost…
De novo protein design using geometric vector field networks
Weian Mao, Muzhi Zhu, Zheng Sun +4
Innovations like protein diffusion have enabled significant progress in de novo protein design, which is a vital topic in life science. These methods typically depend on protein st…