244 citations · 1.5k across the 71 of their papers we have counts for
35 papers · 1 filter
TeCH: Text-guided Reconstruction of Lifelike Clothed Humans
Yangyi Huang, Hongwei Yi, Yuliang Xiu +4
Despite recent research advancements in reconstructing clothed humans from a single image, accurately restoring the "unseen regions" with high-level details remains an unsolved cha…
Learning to Affiliate: Mutual Centralized Learning for Few-shot Classification
Yang Liu, Weifeng Zhang, Chao Xiang +3
Few-shot learning (FSL) aims to learn a classifier that can be easily adapted to accommodate new tasks not seen during training, given only a few examples. To handle the limited-da…
ES-Net: Erasing Salient Parts to Learn More in Re-Identification
Dong Shen, Shuai Zhao, Jinming Hu +3
As an instance-level recognition problem, re-identification (re-ID) requires models to capture diverse features. However, with continuous training, re-ID models pay more and more a…
Complementary Pseudo Labels For Unsupervised Domain Adaptation On Person Re-identification
Hao Feng, Minghao Chen, Jinming Hu +3
In recent years, supervised person re-identification (re-ID) models have received increasing studies. However, these models trained on the source domain always suffer dramatic perf…
Geometry-based Occlusion-Aware Unsupervised Stereo Matching for Autonomous Driving
Liang Peng, Dan Deng, Deng Cai
Recently, there are emerging many stereo matching methods for autonomous driving based on unsupervised learning. Most of them take advantage of reconstruction losses to remove depe…
Accelerate CNNs from Three Dimensions: A Comprehensive Pruning Framework
Wenxiao Wang, Minghao Chen, Shuai Zhao +6
Most neural network pruning methods, such as filter-level and layer-level prunings, prune the network model along one dimension (depth, width, or resolution) solely to meet a compu…
Apparel-invariant Feature Learning for Apparel-changed Person Re-identification
Zhengxu Yu, Yilun Zhao, Bin Hong +5
With the rise of deep learning methods, person Re-Identification (ReID) performance has been improved tremendously in many public datasets. However, most public ReID datasets are c…
Learning to Caricature via Semantic Shape Transform
Wenqing Chu, Wei-Chih Hung, Yi-Hsuan Tsai +4
Caricature is an artistic drawing created to abstract or exaggerate facial features of a person. Rendering visually pleasing caricatures is a difficult task that requires professio…
RESA: Recurrent Feature-Shift Aggregator for Lane Detection
Tu Zheng, Hao Fang, Yi Zhang +4
Lane detection is one of the most important tasks in self-driving. Due to various complex scenarios (e.g., severe occlusion, ambiguous lanes, etc.) and the sparse supervisory signa…
Boundary-Aware Dense Feature Indicator for Single-Stage 3D Object Detection from Point Clouds
Guodong Xu, Wenxiao Wang, Zili Liu +4
3D object detection based on point clouds has become more and more popular. Some methods propose localizing 3D objects directly from raw point clouds to avoid information loss. How…
Convolutional Hierarchical Attention Network for Query-Focused Video Summarization
Shuwen Xiao, Zhou Zhao, Zijian Zhang +2
Previous approaches for video summarization mainly concentrate on finding the most diverse and representative visual contents as video summary without considering the user's prefer…
Adversarial-Learned Loss for Domain Adaptation
Minghao Chen, Shuai Zhao, Haifeng Liu +1
Recently, remarkable progress has been made in learning transferable representation across domains. Previous works in domain adaptation are majorly based on two techniques: domain-…
DBP: Discrimination Based Block-Level Pruning for Deep Model Acceleration
Wenxiao Wang, Shuai Zhao, Minghao Chen +3
Neural network pruning is one of the most popular methods of accelerating the inference of deep convolutional neural networks (CNNs). The dominant pruning methods, filter-level pru…
PI-RCNN: An Efficient Multi-sensor 3D Object Detector with Point-based Attentive Cont-conv Fusion Module
Liang Xie, Chao Xiang, Zhengxu Yu +4
LIDAR point clouds and RGB-images are both extremely essential for 3D object detection. So many state-of-the-art 3D detection algorithms dedicate in fusing these two types of data…
Region Mutual Information Loss for Semantic Segmentation
Shuai Zhao, Yang Wang, Zheng Yang +1
Semantic segmentation is a fundamental problem in computer vision. It is considered as a pixel-wise classification problem in practice, and most segmentation models use a pixel-wis…
Correlation Maximized Structural Similarity Loss for Semantic Segmentation
Shuai Zhao, Boxi Wu, Wenqing Chu +2
Most semantic segmentation models treat semantic segmentation as a pixel-wise classification task and use a pixel-wise classification error as their optimization criterions. Howeve…
Domain Adaptation for Semantic Segmentation with Maximum Squares Loss
Minghao Chen, Hongyang Xue, Deng Cai
Deep neural networks for semantic segmentation always require a large number of samples with pixel-level labels, which becomes the major difficulty in their real-world applications…
A Better Way to Attend: Attention with Trees for Video Question Answering
Hongyang Xue, Wenqing Chu, Zhou Zhao +1
We propose a new attention model for video question answering. The main idea of the attention models is to locate on the most informative parts of the visual data. The attention me…
Training-Time-Friendly Network for Real-Time Object Detection
Zili Liu, Tu Zheng, Guodong Xu +3
Modern object detectors can rarely achieve short training time, fast inference speed, and high accuracy at the same time. To strike a balance among them, we propose the Training-Ti…
Progressive Transfer Learning
Zhengxu Yu, Dong Shen, Zhongming Jin +3
Model fine-tuning is a widely used transfer learning approach in person Re-identification (ReID) applications, which fine-tuning a pre-trained feature extraction model into the tar…
Improving Semantic Segmentation via Dilated Affinity
Boxi Wu, Shuai Zhao, Wenqing Chu +2
Introducing explicit constraints on the structural predictions has been an effective way to improve the performance of semantic segmentation models. Existing methods are mainly bas…
Localizing Unseen Activities in Video via Image Query
Zhu Zhang, Zhou Zhao, Zhijie Lin +2
Action localization in untrimmed videos is an important topic in the field of video understanding. However, existing action localization methods are restricted to a pre-defined set…
COP: Customized Deep Model Compression via Regularized Correlation-Based Filter-Level Pruning
Wenxiao Wang, Cong Fu, Jishun Guo +2
Neural network compression empowers the effective yet unwieldy deep convolutional neural networks (CNN) to be deployed in resource-constrained scenarios. Most state-of-the-art appr…
Weakly-supervised Caricature Face Parsing through Domain Adaptation
Wenqing Chu, Wei-Chih Hung, Yi-Hsuan Tsai +2
A caricature is an artistic form of a person's picture in which certain striking characteristics are abstracted or exaggerated in order to create a humor or sarcasm effect. For num…