6 citations · 15 across the 12 of their papers we have counts for
14 papers
UniGen-AR: Unifying Visual Generation with Auto-Regressive Modeling
Zhipeng Bao, Zhen Zhu, Nupur Kumari +4
Modern computer vision pipelines remain fragmented, with tasks such as text-to-image generation, editing, restoration, and classical perception handled by separate models. We study…
Walk through Paintings: Egocentric World Models from Internet Priors
Anurag Bagchi, Zhipeng Bao, Homanga Bharadhwaj +3
What if a video generation model could not only imagine a plausible future, but the correct one -- accurately reflecting how the world changes with each action? We answer this by p…
Diff-2-in-1: Bridging Generation and Dense Perception with Diffusion Models
Shuhong Zheng, Zhipeng Bao, Ruoyu Zhao +2
Beyond high-fidelity image synthesis, diffusion models have recently exhibited promising results in dense visual perception tasks. However, most existing work treats diffusion mode…
ReferEverything: Towards Segmenting Everything We Can Speak of in Videos
Anurag Bagchi, Zhipeng Bao, Yu-Xiong Wang +2
We present REM, a framework for segmenting a wide range of concepts in video that can be described through natural language. Our method leverages the universal visual-language mapp…
Lexicon3D: Probing Visual Foundation Models for Complex 3D Scene Understanding
Yunze Man, Shuhong Zheng, Zhipeng Bao +3
Complex 3D scene understanding has gained increasing attention, with scene encoding strategies playing a crucial role in this success. However, the optimal scene encoding strategie…
Separate-and-Enhance: Compositional Finetuning for Text2Image Diffusion Models
Zhipeng Bao, Yijun Li, Krishna Kumar Singh +2
Despite recent significant strides achieved by diffusion-based Text-to-Image (T2I) models, current systems are still less capable of ensuring decent compositional generation aligne…
Multi-task View Synthesis with Neural Radiance Fields
Shuhong Zheng, Zhipeng Bao, Martial Hebert +1
Multi-task visual learning is a critical aspect of computer vision. Current research, however, predominantly concentrates on the multi-task dense prediction setting, which overlook…
Restoring Images Captured in Arbitrary Hybrid Adverse Weather Conditions in One Go
Ye-Cong Wan, Ming-Wen Shao, Yuan-Shuo Cheng +2
Adverse conditions typically suffer from stochastic hybrid weather degradations (e.g., rainy and hazy night), while existing image restoration algorithms envisage that weather degr…
Object Discovery from Motion-Guided Tokens
Zhipeng Bao, Pavel Tokmakov, Yu-Xiong Wang +2
Object discovery -- separating objects from the background without manual labels -- is a fundamental open challenge in computer vision. Previous methods struggle to go beyond clust…
Beyond RGB: Scene-Property Synthesis with Neural Radiance Fields
Mingtong Zhang, Shuhong Zheng, Zhipeng Bao +2
Comprehensive 3D scene understanding, both geometrically and semantically, is important for real-world applications such as robot perception. Most of the existing work has focused…
Discovering Objects that Can Move
Zhipeng Bao, Pavel Tokmakov, Allan Jabri +3
This paper studies the problem of object discovery -- separating objects from the background without manual labels. Existing approaches utilize appearance cues, such as color, text…
Generative Modeling for Multi-task Visual Learning
Zhipeng Bao, Martial Hebert, Yu-Xiong Wang
Generative modeling has recently shown great promise in computer vision, but it has mostly focused on synthesizing visually realistic images. In this paper, motivated by multi-task…
Bowtie Networks: Generative Modeling for Joint Few-Shot Recognition and Novel-View Synthesis
Zhipeng Bao, Yu-Xiong Wang, Martial Hebert
We propose a novel task of joint few-shot recognition and novel-view synthesis: given only one or few images of a novel object from arbitrary views with only category annotation, w…
Deep Learning-Based Strategy for Macromolecules Classification with Imbalanced Data from Cellular Electron Cryotomography
Ziqian Luo, Xiangrui Zeng, Zhipeng Bao +1
Deep learning model trained by imbalanced data may not work satisfactorily since it could be determined by major classes and thus may ignore the classes with small amount of data.…