1.2k citations
- Chinese University of Hong KongHK59 papers
- Sun Yat-sen UniversityCN14 papers
- Tsinghua UniversityCN12 papers
- Chinese Academy of SciencesCN11 papers
- The University of SydneyAU9 papers
- University of Hong KongHK9 papers
- Beihang UniversityCN8 papers
- Nanyang Technological UniversitySG8 papers
- Shanghai Jiao Tong UniversityCN7 papers
- Shenzhen Institutes of Advanced TechnologyCN7 papers
- Tencent (China)CN7 papers
- Group Sense (China)CN6 papers
49 papers · 2 filters
Joint Face Hallucination and Deblurring via Structure Generation and Detail Enhancement
Yibing Song, Jiawei Zhang, Lijun Gong +5
We address the problem of restoring a high-resolution face image from a blurry low-resolution input. This problem is difficult as super-resolution and deblurring need to be tackled…
Do Normalization Layers in a Deep ConvNet Really Need to Be Distinct?
Ping Luo, Zhanglin Peng, Jiamin Ren +1
Yes, they do. This work investigates a perspective for deep learning: whether different normalization layers in a ConvNet require different normalizers. This is the first step towa…
Learning to Steer by Mimicking Features from Heterogeneous Auxiliary Networks
Yuenan Hou, Zheng Ma, Chunxiao Liu +1
The training of many existing end-to-end steering angle prediction models heavily relies on steering angles as the supervisory signal. Without learning from much richer contexts, t…
Sequential Context Encoding for Duplicate Removal
Lu Qi, Shu Liu, Jianping Shi +1
Duplicate removal is a critical step to accomplish a reasonable amount of predictions in prevalent proposal-based object detection frameworks. Albeit simple and effective, most pre…
Unsupervised Image Super-Resolution using Cycle-in-Cycle Generative Adversarial Networks
Yuan Yuan, Siyuan Liu, Jiawei Zhang +3
We consider the single image super-resolution problem in a more general case that the low-/high-resolution pairs and the down-sampling process are unavailable. Different from tradi…
Multi-Label Image Classification via Knowledge Distillation from Weakly-Supervised Detection
Yongcheng Liu, Lu Sheng, Jing Shao +3
Multi-label image classification is a fundamental but challenging task towards general visual understanding. Existing methods found the region-level cues (e.g., features from RoIs)…