167 citations · 457 across the 38 of their papers we have counts for
21 papers · 1 filter
Progressive Energy-Based Cooperative Learning for Multi-Domain Image-to-Image Translation
Weinan Song, Yaxuan Zhu, Lei He +2
This paper studies a novel energy-based cooperative learning framework for multi-domain image-to-image translation. The framework consists of four components: descriptor, translato…
Likelihood-Based Generative Radiance Field with Latent Space Energy-Based Model for 3D-Aware Disentangled Image Representation
Yaxuan Zhu, Jianwen Xie, Ping Li
We propose the NeRF-LEBM, a likelihood-based top-down 3D-aware 2D image generative model that incorporates 3D representation via Neural Radiance Fields (NeRF) and 2D imaging proces…
CoopInit: Initializing Generative Adversarial Networks via Cooperative Learning
Yang Zhao, Jianwen Xie, Ping Li
Numerous research efforts have been made to stabilize the training of the Generative Adversarial Networks (GANs), such as through regularization and architecture design. However, w…
CoopHash: Cooperative Learning of Multipurpose Descriptor and Contrastive Pair Generator via Variational MCMC Teaching for Supervised Image Hashing
Khoa D. Doan, Jianwen Xie, Yaxuan Zhu +2
Leveraging supervised information can lead to superior retrieval performance in the image hashing domain but the performance degrades significantly without enough labeled data. One…
An Energy-Based Prior for Generative Saliency
Jing Zhang, Jianwen Xie, Nick Barnes +1
We propose a novel generative saliency prediction framework that adopts an informative energy-based model as a prior distribution. The energy-based prior model is defined on the la…
Learning Generative Vision Transformer with Energy-Based Latent Space for Saliency Prediction
Jing Zhang, Jianwen Xie, Nick Barnes +1
Vision transformer networks have shown superiority in many computer vision tasks. In this paper, we take a step further by proposing a novel generative vision transformer with late…
Blind Image Super-resolution with Elaborate Degradation Modeling on Noise and Kernel
Zongsheng Yue, Qian Zhao, Jianwen Xie +3
While researches on model-based blind single image super-resolution (SISR) have achieved tremendous successes recently, most of them do not consider the image degradation sufficien…
Energy-Based Generative Cooperative Saliency Prediction
Jing Zhang, Jianwen Xie, Zilong Zheng +1
Conventional saliency prediction models typically learn a deterministic mapping from an image to its saliency map, and thus fail to explain the subjective nature of human attention…
Semi-Supervised Video Deraining with Dynamical Rain Generator
Zongsheng Yue, Jianwen Xie, Qian Zhao +1
While deep learning (DL)-based video deraining methods have achieved significant success recently, they still exist two major drawbacks. Firstly, most of them do not sufficiently m…
Learning Cycle-Consistent Cooperative Networks via Alternating MCMC Teaching for Unsupervised Cross-Domain Translation
Jianwen Xie, Zilong Zheng, Xiaolin Fang +2
This paper studies the unsupervised cross-domain translation problem by proposing a generative framework, in which the probability distribution of each domain is represented by a g…
Generative VoxelNet: Learning Energy-Based Models for 3D Shape Synthesis and Analysis
Jianwen Xie, Zilong Zheng, Ruiqi Gao +3
3D data that contains rich geometry information of objects and scenes is valuable for understanding 3D physical world. With the recent emergence of large-scale 3D datasets, it beco…
Learning Noise-Aware Encoder-Decoder from Noisy Labels by Alternating Back-Propagation for Saliency Detection
Jing Zhang, Jianwen Xie, Nick Barnes
In this paper, we propose a noise-aware encoder-decoder framework to disentangle a clean saliency predictor from noisy training examples, where the noisy labels are generated by un…
Generative PointNet: Deep Energy-Based Learning on Unordered Point Sets for 3D Generation, Reconstruction and Classification
Jianwen Xie, Yifei Xu, Zilong Zheng +2
We propose a generative model of unordered point sets, such as point clouds, in the form of an energy-based model, where the energy function is parameterized by an input-permutatio…
Motion-Based Generator Model: Unsupervised Disentanglement of Appearance, Trackable and Intrackable Motions in Dynamic Patterns
Jianwen Xie, Ruiqi Gao, Zilong Zheng +2
Dynamic patterns are characterized by complex spatial and motion patterns. Understanding dynamic patterns requires a disentangled representational model that separates the factoria…
Learning Energy-based Spatial-Temporal Generative ConvNets for Dynamic Patterns
Jianwen Xie, Song-Chun Zhu, Ying Nian Wu
Video sequences contain rich dynamic patterns, such as dynamic texture patterns that exhibit stationarity in the temporal domain, and action patterns that are non-stationary in eit…
Learning Feature-to-Feature Translator by Alternating Back-Propagation for Generative Zero-Shot Learning
Yizhe Zhu, Jianwen Xie, Bingchen Liu +1
We investigate learning feature-to-feature translator networks by alternating back-propagation as a general-purpose solution to zero-shot learning (ZSL) problems. It is a generativ…
Semantic-Guided Multi-Attention Localization for Zero-Shot Learning
Yizhe Zhu, Jianwen Xie, Zhiqiang Tang +2
Zero-shot learning extends the conventional object classification to the unseen class recognition by introducing semantic representations of classes. Existing approaches predominan…
Segregated Temporal Assembly Recurrent Networks for Weakly Supervised Multiple Action Detection
Yunlu Xu, Chengwei Zhang, Zhanzhan Cheng +4
This paper proposes a segregated temporal assembly recurrent (STAR) network for weakly-supervised multiple action detection. The model learns from untrimmed videos with only superv…
Weakly and Semi Supervised Human Body Part Parsing via Pose-Guided Knowledge Transfer
Hao-Shu Fang, Guansong Lu, Xiaolin Fang +3
Human body part parsing, or human semantic part segmentation, is fundamental to many computer vision tasks. In conventional semantic segmentation methods, the ground truth segmenta…
Learning Descriptor Networks for 3D Shape Synthesis and Analysis
Jianwen Xie, Zilong Zheng, Ruiqi Gao +3
This paper proposes a 3D shape descriptor network, which is a deep convolutional energy-based model, for modeling volumetric shape patterns. The maximum likelihood training of the…
Super-Trajectory for Video Segmentation
Wenguan Wang, Jianbing Shen, Jianwen Xie +1
We introduce a novel semi-supervised video segmentation approach based on an efficient video representation, called as "super-trajectory". Each super-trajectory corresponds to a gr…