3.2k citations · 12.3k across the 227 of their papers we have counts for
18 papers · 1 filter
An Embarrassingly Simple Approach for Knowledge Distillation
Mengya Gao, Yujun Shen, Quanquan Li +5
Knowledge Distillation (KD) aims at improving the performance of a low-capacity student model by inheriting knowledge from a high-capacity teacher model. Previous KD methods typica…
Instance-level Facial Attributes Transfer with Geometry-Aware Flow
Weidong Yin, Ziwei Liu, Chen Change Loy
We address the problem of instance-level facial attribute transfer without paired training data, e.g. faithfully transferring the exact mustache from a source face to a target face…
Deep Network Interpolation for Continuous Imagery Effect Transition
Xintao Wang, Ke Yu, Chao Dong +2
Deep convolutional neural network has demonstrated its capability of learning a deterministic mapping for the desired imagery effect. However, the large variety of user flavors mot…
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…
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
Xintao Wang, Ke Yu, Shixiang Wu +6
The Super-Resolution Generative Adversarial Network (SRGAN) is a seminal work that is capable of generating realistic textures during single image super-resolution. However, the ha…
Improving On-policy Learning with Statistical Reward Accumulation
Yubin Deng, Ke Yu, Dahua Lin +2
Deep reinforcement learning has obtained significant breakthroughs in recent years. Most methods in deep-RL achieve good results via the maximization of the reward signal provided…
Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition
Xiaohang Zhan, Ziwei Liu, Junjie Yan +2
Face recognition has witnessed great progress in recent years, mainly attributed to the high-capacity model designed and the abundant labeled data collected. However, it becomes mo…
The Devil of Face Recognition is in the Noise
Fei Wang, Liren Chen, Cheng Li +4
The growing scale of face recognition datasets empowers us to train strong convolutional networks for face recognition. While a variety of architectures and loss functions have bee…
ReenactGAN: Learning to Reenact Faces via Boundary Transfer
Wayne Wu, Yunxuan Zhang, Cheng Li +2
We present a novel learning-based framework for face reenactment. The proposed method, known as ReenactGAN, is capable of transferring facial movements and expressions from monocul…
Zoom-Net: Mining Deep Feature Interactions for Visual Relationship Recognition
Guojun Yin, Lu Sheng, Bin Liu +4
Recognizing visual relationships <subject-predicate-object> among any pair of localized objects is pivotal for image understanding. Previous studies have shown remarkable progress…
Non-Local Recurrent Network for Image Restoration
Ding Liu, Bihan Wen, Yuchen Fan +2
Many classic methods have shown non-local self-similarity in natural images to be an effective prior for image restoration. However, it remains unclear and challenging to make use…
Deep Imbalanced Learning for Face Recognition and Attribute Prediction
Chen Huang, Yining Li, Chen Change Loy +1
Data for face analysis often exhibit highly-skewed class distribution, i.e., most data belong to a few majority classes, while the minority classes only contain a scarce amount of…
Optimizing Video Object Detection via a Scale-Time Lattice
Kai Chen, Jiaqi Wang, Shuo Yang +4
High-performance object detection relies on expensive convolutional networks to compute features, often leading to significant challenges in applications, e.g. those that require d…
Crafting a Toolchain for Image Restoration by Deep Reinforcement Learning
Ke Yu, Chao Dong, Liang Lin +1
We investigate a novel approach for image restoration by reinforcement learning. Unlike existing studies that mostly train a single large network for a specialized task, we prepare…
Recovering Realistic Texture in Image Super-resolution by Deep Spatial Feature Transform
Xintao Wang, Ke Yu, Chao Dong +1
Despite that convolutional neural networks (CNN) have recently demonstrated high-quality reconstruction for single-image super-resolution (SR), recovering natural and realistic tex…
Video Object Segmentation with Joint Re-identification and Attention-Aware Mask Propagation
Xiaoxiao Li, Chen Change Loy
The problem of video object segmentation can become extremely challenging when multiple instances co-exist. While each instance may exhibit large scale and pose variations, the pro…
Pose-Robust Face Recognition via Deep Residual Equivariant Mapping
Kaidi Cao, Yu Rong, Cheng Li +2
Face recognition achieves exceptional success thanks to the emergence of deep learning. However, many contemporary face recognition models still perform relatively poor in processi…
Mix-and-Match Tuning for Self-Supervised Semantic Segmentation
Xiaohang Zhan, Ziwei Liu, Ping Luo +2
Deep convolutional networks for semantic image segmentation typically require large-scale labeled data, e.g. ImageNet and MS COCO, for network pre-training. To reduce annotation ef…