83 citations · 252 across the 13 of their papers we have counts for
8 papers · 1 filter
Distilling Object Detectors with Feature Richness
Zhixing Du, Rui Zhang, Ming Chang +4
In recent years, large-scale deep models have achieved great success, but the huge computational complexity and massive storage requirements make it a great challenge to deploy the…
Domain-Specific Suppression for Adaptive Object Detection
Yu Wang, Rui Zhang, Shuo Zhang +4
Domain adaptation methods face performance degradation in object detection, as the complexity of tasks require more about the transferability of the model. We propose a new perspec…
Asymmetric GAN for Unpaired Image-to-image Translation
Yu Li, Sheng Tang, Rui Zhang +3
Unpaired image-to-image translation problem aims to model the mapping from one domain to another with unpaired training data. Current works like the well-acknowledged Cycle GAN pro…
Consensus Feature Network for Scene Parsing
Tianyi Wu, Sheng Tang, Rui Zhang +2
Scene parsing is challenging as it aims to assign one of the semantic categories to each pixel in scene images. Thus, pixel-level features are desired for scene parsing. However, c…
Harmonic Unpaired Image-to-image Translation
Rui Zhang, Tomas Pfister, Jia Li
The recent direction of unpaired image-to-image translation is on one hand very exciting as it alleviates the big burden in obtaining label-intensive pixel-to-pixel supervision, bu…
Tree-structured Kronecker Convolutional Network for Semantic Segmentation
Tianyi Wu, Sheng Tang, Rui Zhang +2
Most existing semantic segmentation methods employ atrous convolution to enlarge the receptive field of filters, but neglect partial information. To tackle this issue, we firstly p…
Style Separation and Synthesis via Generative Adversarial Networks
Rui Zhang, Sheng Tang, Yu Li +4
Style synthesis attracts great interests recently, while few works focus on its dual problem "style separation". In this paper, we propose the Style Separation and Synthesis Genera…
CGNet: A Light-weight Context Guided Network for Semantic Segmentation
Tianyi Wu, Sheng Tang, Rui Zhang +1
The demand of applying semantic segmentation model on mobile devices has been increasing rapidly. Current state-of-the-art networks have enormous amount of parameters hence unsuita…