22 citations · 57 across the 17 of their papers we have counts for
17 papers
PE-Field 4D: Video Generation Models as Canvas
Yunpeng Bai, Haoxiang Li, Qixing Huang
Diffusion Transformers have recently achieved strong performance in video generation, yet controlling scene geometry under viewpoint changes and camera motion remains challenging.…
UniLayDiff: A Unified Diffusion Transformer for Content-Aware Layout Generation
Zeyang Liu, Le Wang, Sanping Zhou +4
Content-aware layout generation is a critical task in graphic design automation, focused on creating visually appealing arrangements of elements that seamlessly blend with a given…
LayoutRAG: Retrieval-Augmented Model for Content-agnostic Conditional Layout Generation
Yuxuan Wu, Le Wang, Sanping Zhou +3
Controllable layout generation aims to create plausible visual arrangements of element bounding boxes within a graphic design according to certain optional constraints, such as the…
Glissando-Net: Deep sinGLe vIew category level poSe eStimation ANd 3D recOnstruction
Bo Sun, Hao Kang, Li Guan +3
We present a deep learning model, dubbed Glissando-Net, to simultaneously estimate the pose and reconstruct the 3D shape of objects at the category level from a single RGB image. P…
Deployment Prior Injection for Run-time Calibratable Object Detection
Mo Zhou, Yiding Yang, Haoxiang Li +2
With a strong alignment between the training and test distributions, object relation as a context prior facilitates object detection. Yet, it turns into a harmful but inevitable tr…
UGG: Unified Generative Grasping
Jiaxin Lu, Hao Kang, Haoxiang Li +4
Dexterous grasping aims to produce diverse grasping postures with a high grasping success rate. Regression-based methods that directly predict grasping parameters given the object…
Flexible Visual Recognition by Evidential Modeling of Confusion and Ignorance
Lei Fan, Bo Liu, Haoxiang Li +2
In real-world scenarios, typical visual recognition systems could fail under two major causes, i.e., the misclassification between known classes and the excusable misbehavior on un…
DDM-NET: End-to-end learning of keypoint feature Detection, Description and Matching for 3D localization
Xiangyu Xu, Li Guan, Enrique Dunn +2
In this paper, we propose an end-to-end framework that jointly learns keypoint detection, descriptor representation and cross-frame matching for the task of image-based 3D localiza…
Boosted Dynamic Neural Networks
Haichao Yu, Haoxiang Li, Gang Hua +2
Early-exiting dynamic neural networks (EDNN), as one type of dynamic neural networks, has been widely studied recently. A typical EDNN has multiple prediction heads at different la…
Implicit Autoencoder for Point-Cloud Self-Supervised Representation Learning
Siming Yan, Zhenpei Yang, Haoxiang Li +5
This paper advocates the use of implicit surface representation in autoencoder-based self-supervised 3D representation learning. The most popular and accessible 3D representation,…
Learning Dynamics via Graph Neural Networks for Human Pose Estimation and Tracking
Yiding Yang, Zhou Ren, Haoxiang Li +3
Multi-person pose estimation and tracking serve as crucial steps for video understanding. Most state-of-the-art approaches rely on first estimating poses in each frame and only the…
Semi-supervised Long-tailed Recognition using Alternate Sampling
Bo Liu, Haoxiang Li, Hao Kang +2
Main challenges in long-tailed recognition come from the imbalanced data distribution and sample scarcity in its tail classes. While techniques have been proposed to achieve a more…
GistNet: a Geometric Structure Transfer Network for Long-Tailed Recognition
Bo Liu, Haoxiang Li, Hao Kang +2
The problem of long-tailed recognition, where the number of examples per class is highly unbalanced, is considered. It is hypothesized that the well known tendency of standard clas…
Breadcrumbs: Adversarial Class-Balanced Sampling for Long-tailed Recognition
Bo Liu, Haoxiang Li, Hao Kang +2
The problem of long-tailed recognition, where the number of examples per class is highly unbalanced, is considered. While training with class-balanced sampling has been shown effec…
Beyond Visual Attractiveness: Physically Plausible Single Image HDR Reconstruction for Spherical Panoramas
Wei Wei, Li Guan, Yue Liu +4
HDR reconstruction is an important task in computer vision with many industrial needs. The traditional approaches merge multiple exposure shots to generate HDRs that correspond to…
Few-Shot Open-Set Recognition using Meta-Learning
Bo Liu, Hao Kang, Haoxiang Li +2
The problem of open-set recognition is considered. While previous approaches only consider this problem in the context of large-scale classifier training, we seek a unified solutio…
Any-Precision Deep Neural Networks
Haichao Yu, Haoxiang Li, Honghui Shi +2
We present any-precision deep neural networks (DNNs), which are trained with a new method that allows the learned DNNs to be flexible in numerical precision during inference. The s…