192 citations · 564 across the 21 of their papers we have counts for
21 papers
Geometry-Aware Spatio-Temporal Context Modeling for 4D Occupancy Forecasting
Sitao Chen, Zhuangwei Zhuang, Hui Luo +2
4D occupancy forecasting models the spatio-temporal evolution of 3D scenes and is crucial for autonomous driving, especially for corner-case simulation. Existing methods often rely…
Semantic Occupancy Prediction with Dual Range-Voxel Representation
Sitao Chen, Zhuangwei Zhuang, Hui Luo +3
LiDAR-based 3D semantic occupancy prediction, which aims to provide accurate and comprehensive scene representation, is crucial for autonomous driving systems. As point clouds suff…
Dual Progressive Transformations for Weakly Supervised Semantic Segmentation
Dongjian Huo, Yukun Su, Qingyao Wu
Weakly supervised semantic segmentation (WSSS), which aims to mine the object regions by merely using class-level labels, is a challenging task in computer vision. The current stat…
A Unified Transformer Framework for Group-based Segmentation: Co-Segmentation, Co-Saliency Detection and Video Salient Object Detection
Yukun Su, Jingliang Deng, Ruizhou Sun +2
Humans tend to mine objects by learning from a group of images or several frames of video since we live in a dynamic world. In the computer vision area, many researches focus on co…
Calibrating Class Activation Maps for Long-Tailed Visual Recognition
Chi Zhang, Guosheng Lin, Lvlong Lai +2
Real-world visual recognition problems often exhibit long-tailed distributions, where the amount of data for learning in different categories shows significant imbalance. Standard…
MV-TON: Memory-based Video Virtual Try-on network
Xiaojing Zhong, Zhonghua Wu, Taizhe Tan +2
With the development of Generative Adversarial Network, image-based virtual try-on methods have made great progress. However, limited work has explored the task of video-based virt…
Context Decoupling Augmentation for Weakly Supervised Semantic Segmentation
Yukun Su, Ruizhou Sun, Guosheng Lin +1
Data augmentation is vital for deep learning neural networks. By providing massive training samples, it helps to improve the generalization ability of the model. Weakly supervised…
StackRec: Efficient Training of Very Deep Sequential Recommender Models by Iterative Stacking
Jiachun Wang, Fajie Yuan, Jian Chen +4
Deep learning has brought great progress for the sequential recommendation (SR) tasks. With advanced network architectures, sequential recommender models can be stacked with many h…
Double Forward Propagation for Memorized Batch Normalization
Yong Guo, Qingyao Wu, Chaorui Deng +2
Batch Normalization (BN) has been a standard component in designing deep neural networks (DNNs). Although the standard BN can significantly accelerate the training of DNNs and impr…
Graph Edit Distance Reward: Learning to Edit Scene Graph
Lichang Chen, Guosheng Lin, Shijie Wang +1
Scene Graph, as a vital tool to bridge the gap between language domain and image domain, has been widely adopted in the cross-modality task like VQA. In this paper, we propose a ne…
Improving Generative Adversarial Networks with Local Coordinate Coding
Jiezhang Cao, Yong Guo, Qingyao Wu +3
Generative adversarial networks (GANs) have shown remarkable success in generating realistic data from some predefined prior distribution (e.g., Gaussian noises). However, such pri…
Collaborative Unsupervised Domain Adaptation for Medical Image Diagnosis
Yifan Zhang, Ying Wei, Qingyao Wu +4
Deep learning based medical image diagnosis has shown great potential in clinical medicine. However, it often suffers two major difficulties in real-world applications: 1) only lim…
COVID-DA: Deep Domain Adaptation from Typical Pneumonia to COVID-19
Yifan Zhang, Shuaicheng Niu, Zhen Qiu +6
The outbreak of novel coronavirus disease 2019 (COVID-19) has already infected millions of people and is still rapidly spreading all over the globe. Most COVID-19 patients suffer f…
Cost-Sensitive Portfolio Selection via Deep Reinforcement Learning
Yifan Zhang, Peilin Zhao, Qingyao Wu +3
Portfolio Selection is an important real-world financial task and has attracted extensive attention in artificial intelligence communities. This task, however, has two main difficu…
Online Adaptive Asymmetric Active Learning with Limited Budgets
Yifan Zhang, Peilin Zhao, Shuaicheng Niu +4
Online Active Learning (OAL) aims to manage unlabeled datastream by selectively querying the label of data. OAL is applicable to many real-world problems, such as anomaly detection…
Collaborative Unsupervised Domain Adaptation for Medical Image Diagnosis
Yifan Zhang, Ying Wei, Peilin Zhao +4
Deep learning based medical image diagnosis has shown great potential in clinical medicine. However, it often suffers two major difficulties in practice: 1) only limited labeled sa…
Attention Guided Network for Retinal Image Segmentation
Shihao Zhang, Huazhu Fu, Yuguang Yan +5
Learning structural information is critical for producing an ideal result in retinal image segmentation. Recently, convolutional neural networks have shown a powerful ability to ex…
Auto-Embedding Generative Adversarial Networks for High Resolution Image Synthesis
Yong Guo, Qi Chen, Jian Chen +3
Generating images via the generative adversarial network (GAN) has attracted much attention recently. However, most of the existing GAN-based methods can only produce low-resolutio…
Discrimination-aware Channel Pruning for Deep Neural Networks
Zhuangwei Zhuang, Mingkui Tan, Bohan Zhuang +5
Channel pruning is one of the predominant approaches for deep model compression. Existing pruning methods either train from scratch with sparsity constraints on channels, or minimi…
Adversarial Learning with Local Coordinate Coding
Jiezhang Cao, Yong Guo, Qingyao Wu +3
Generative adversarial networks (GANs) aim to generate realistic data from some prior distribution (e.g., Gaussian noises). However, such prior distribution is often independent of…
The Shallow End: Empowering Shallower Deep-Convolutional Networks through Auxiliary Outputs
Yong Guo, Jian Chen, Qing Du +3
Depth is one of the key factors behind the success of convolutional neural networks (CNNs). Since ResNet, we are able to train very deep CNNs as the gradient vanishing issue has be…