244 citations · 1.5k across the 71 of their papers we have counts for
12 papers · 2 filters
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…