30 citations · 40 across the 19 of their papers we have counts for
20 papers
Show-Harness: Just a VLM Agent Can Play Robots
Yanzhe Chen, Zechen Bai, Zhijun Cao +7
Foundation vision-language models (VLMs) exhibit broad intelligence about the world, yet translating this intelligence into robot control remains challenging. We present Show-Harne…
Demo2Tutorial: From Human Experience to Multimodal Software Tutorials
Zechen Bai, Zhiheng Chen, Yiqi Lin +5
Human experience in digital environments offers a vast, underexplored resource of authentic, untrimmed interactions that contain rich procedural knowledge. We introduce Demo2Tutori…
Escaping the Diversity Trap in Robotic Manipulation via Anchor-Centric Adaptation
Yanzhe Chen, Kevin Yuchen Ma, Qi Lv +4
While Vision-Language-Action (VLA) models offer broad general capabilities, deploying them on specific hardware requires real-world adaptation to bridge the embodiment gap. Since r…
Kiwi-Edit: Versatile Video Editing via Instruction and Reference Guidance
Yiqi Lin, Guoqiang Liang, Ziyun Zeng +3
Instruction-based video editing has witnessed rapid progress, yet current methods often struggle with precise visual control, as natural language is inherently limited in describin…
SIGMA: Selective-Interleaved Generation with Multi-Attribute Tokens
Xiaoyan Zhang, Zechen Bai, Haofan Wang +1
Recent unified models such as Bagel demonstrate that paired image-edit data can effectively align multiple visual tasks within a single diffusion transformer. However, these models…
World-VLA-Loop: Closed-Loop Learning of Video World Model and VLA Policy
Xiaokang Liu, Zechen Bai, Hai Ci +2
Reinforcement learning (RL) can refine Vision-Language-Action (VLA) policies beyond behavior cloning, but real-world RL remains expensive due to extensive rollouts, resets, supervi…
EVOLVE-VLA: Test-Time Training from Environment Feedback for Vision-Language-Action Models
Zechen Bai, Chen Gao, Mike Zheng Shou
Achieving truly adaptive embodied intelligence requires agents that learn not just by imitating static demonstrations, but by continuously improving through environmental interacti…
Impossible Videos
Zechen Bai, Hai Ci, Mike Zheng Shou
Synthetic videos nowadays is widely used to complement data scarcity and diversity of real-world videos. Current synthetic datasets primarily replicate real-world scenarios, leavin…
Factorized Visual Tokenization and Generation
Zechen Bai, Jianxiong Gao, Ziteng Gao +4
Visual tokenizers are fundamental to image generation. They convert visual data into discrete tokens, enabling transformer-based models to excel at image generation. Despite their…
ShowUI: One Vision-Language-Action Model for GUI Visual Agent
Kevin Qinghong Lin, Linjie Li, Difei Gao +6
Building Graphical User Interface (GUI) assistants holds significant promise for enhancing human workflow productivity. While most agents are language-based, relying on closed-sour…
One Token to Seg Them All: Language Instructed Reasoning Segmentation in Videos
Zechen Bai, Tong He, Haiyang Mei +6
We introduce VideoLISA, a video-based multimodal large language model designed to tackle the problem of language-instructed reasoning segmentation in videos. Leveraging the reasoni…
Show-o: One Single Transformer to Unify Multimodal Understanding and Generation
Jinheng Xie, Weijia Mao, Zechen Bai +7
We present a unified transformer, i.e., Show-o, that unifies multimodal understanding and generation. Unlike fully autoregressive models, Show-o unifies autoregressive and (discret…
Bridging Information Asymmetry in Text-video Retrieval: A Data-centric Approach
Zechen Bai, Tianjun Xiao, Tong He +4
As online video content rapidly grows, the task of text-video retrieval (TVR) becomes increasingly important. A key challenge in TVR is the information asymmetry between video and…
Adaptive Slot Attention: Object Discovery with Dynamic Slot Number
Ke Fan, Zechen Bai, Tianjun Xiao +5
Object-centric learning (OCL) extracts the representation of objects with slots, offering an exceptional blend of flexibility and interpretability for abstracting low-level percept…
LOVA3: Learning to Visual Question Answering, Asking and Assessment
Henry Hengyuan Zhao, Pan Zhou, Difei Gao +2
Question answering, asking, and assessment are three innate human traits crucial for understanding the world and acquiring knowledge. By enhancing these capabilities, humans can mo…
Hallucination of Multimodal Large Language Models: A Survey
Zechen Bai, Pichao Wang, Tianjun Xiao +4
This survey presents a comprehensive analysis of the phenomenon of hallucination in multimodal large language models (MLLMs), also known as Large Vision-Language Models (LVLMs), wh…
Skip \n: A Simple Method to Reduce Hallucination in Large Vision-Language Models
Zongbo Han, Zechen Bai, Haiyang Mei +3
Recent advancements in large vision-language models (LVLMs) have demonstrated impressive capability in visual information understanding with human language. Despite these advances,…
ASSISTGUI: Task-Oriented Desktop Graphical User Interface Automation
Difei Gao, Lei Ji, Zechen Bai +10
Graphical User Interface (GUI) automation holds significant promise for assisting users with complex tasks, thereby boosting human productivity. Existing works leveraging Large Lan…
Object-Centric Multiple Object Tracking
Zixu Zhao, Jiaze Wang, Max Horn +13
Unsupervised object-centric learning methods allow the partitioning of scenes into entities without additional localization information and are excellent candidates for reducing th…
Unsupervised Open-Vocabulary Object Localization in Videos
Ke Fan, Zechen Bai, Tianjun Xiao +11
In this paper, we show that recent advances in video representation learning and pre-trained vision-language models allow for substantial improvements in self-supervised video obje…