183 citations · 370 across the 18 of their papers we have counts for
18 papers
Spatial Transform Decoupling for Oriented Object Detection
Hongtian Yu, Yunjie Tian, Qixiang Ye +1
Vision Transformers (ViTs) have achieved remarkable success in computer vision tasks. However, their potential in rotation-sensitive scenarios has not been fully explored, and this…
Multi-Object Manipulation via Object-Centric Neural Scattering Functions
Stephen Tian, Yancheng Cai, Hong-Xing Yu +5
Learned visual dynamics models have proven effective for robotic manipulation tasks. Yet, it remains unclear how best to represent scenes involving multi-object interactions. Curre…
Learning Object-Centric Neural Scattering Functions for Free-Viewpoint Relighting and Scene Composition
Hong-Xing Yu, Michelle Guo, Alireza Fathi +5
Photorealistic object appearance modeling from 2D images is a constant topic in vision and graphics. While neural implicit methods (such as Neural Radiance Fields) have shown high-…
Learning Vortex Dynamics for Fluid Inference and Prediction
Yitong Deng, Hong-Xing Yu, Jiajun Wu +1
We propose a novel differentiable vortex particle (DVP) method to infer and predict fluid dynamics from a single video. Lying at its core is a particle-based latent space to encaps…
Accidental Light Probes
Hong-Xing Yu, Samir Agarwala, Charles Herrmann +4
Recovering lighting in a scene from a single image is a fundamental problem in computer vision. While a mirror ball light probe can capture omnidirectional lighting, light probes a…
Unsupervised Discovery and Composition of Object Light Fields
Cameron Smith, Hong-Xing Yu, Sergey Zakharov +4
Neural scene representations, both continuous and discrete, have recently emerged as a powerful new paradigm for 3D scene understanding. Recent efforts have tackled unsupervised di…
Rotationally Equivariant 3D Object Detection
Hong-Xing Yu, Jiajun Wu, Li Yi
Rotation equivariance has recently become a strongly desired property in the 3D deep learning community. Yet most existing methods focus on equivariance regarding a global input ro…
Letter-level Online Writer Identification
Zelin Chen, Hong-Xing Yu, Ancong Wu +1
Writer identification (writer-id), an important field in biometrics, aims to identify a writer by their handwriting. Identification in existing writer-id studies requires a complet…
Unsupervised Discovery of Object Radiance Fields
Hong-Xing Yu, Leonidas J. Guibas, Jiajun Wu
We study the problem of inferring an object-centric scene representation from a single image, aiming to derive a representation that explains the image formation process, captures…
Neural Radiance Flow for 4D View Synthesis and Video Processing
Yilun Du, Yinan Zhang, Hong-Xing Yu +2
We present a method, Neural Radiance Flow (NeRFlow),to learn a 4D spatial-temporal representation of a dynamic scene from a set of RGB images. Key to our approach is the use of a n…
OpenRooms: An End-to-End Open Framework for Photorealistic Indoor Scene Datasets
Zhengqin Li, Ting-Wei Yu, Shen Sang +14
We propose a novel framework for creating large-scale photorealistic datasets of indoor scenes, with ground truth geometry, material, lighting and semantics. Our goal is to make th…
Weakly supervised discriminative feature learning with state information for person identification
Hong-Xing Yu, Wei-Shi Zheng
Unsupervised learning of identity-discriminative visual feature is appealing in real-world tasks where manual labelling is costly. However, the images of an identity can be visuall…
DSRGAN: Explicitly Learning Disentangled Representation of Underlying Structure and Rendering for Image Generation without Tuple Supervision
Guang-Yuan Hao, Hong-Xing Yu, Wei-Shi Zheng
We focus on explicitly learning disentangled representation for natural image generation, where the underlying spatial structure and the rendering on the structure can be independe…
Unsupervised Person Re-identification by Soft Multilabel Learning
Hong-Xing Yu, Wei-Shi Zheng, Ancong Wu +3
Although unsupervised person re-identification (RE-ID) has drawn increasing research attentions due to its potential to address the scalability problem of supervised RE-ID models,…
Unsupervised Person Re-identification by Deep Asymmetric Metric Embedding
Hong-Xing Yu, Ancong Wu, Wei-Shi Zheng
Person re-identification (Re-ID) aims to match identities across non-overlapping camera views. Researchers have proposed many supervised Re-ID models which require quantities of cr…
MIXGAN: Learning Concepts from Different Domains for Mixture Generation
Guang-Yuan Hao, Hong-Xing Yu, Wei-Shi Zheng
In this work, we present an interesting attempt on mixture generation: absorbing different image concepts (e.g., content and style) from different domains and thus generating a new…
Adversarial Attribute-Image Person Re-identification
Zhou Yin, Wei-Shi Zheng, Ancong Wu +5
While attributes have been widely used for person re-identification (Re-ID) which aims at matching the same person images across disjoint camera views, they are used either as extr…
Cross-view Asymmetric Metric Learning for Unsupervised Person Re-identification
Hong-Xing Yu, Ancong Wu, Wei-Shi Zheng
While metric learning is important for Person re-identification (RE-ID), a significant problem in visual surveillance for cross-view pedestrian matching, existing metric models for…