3.4k citations · 3.5k across the 11 of their papers we have counts for
11 papers
CorrTalk: Correlation Between Hierarchical Speech and Facial Activity Variances for 3D Animation
Zhaojie Chu, Kailing Guo, Xiaofen Xing +3
Speech-driven 3D facial animation is a challenging cross-modal task that has attracted growing research interest. During speaking activities, the mouth displays strong motions, whi…
Center Contrastive Loss for Metric Learning
Bolun Cai, Pengfei Xiong, Shangxuan Tian
Contrastive learning is a major studied topic in metric learning. However, sampling effective contrastive pairs remains a challenge due to factors such as limited batch size, imbal…
UniMoCo: Unsupervised, Semi-Supervised and Full-Supervised Visual Representation Learning
Zhigang Dai, Bolun Cai, Yugeng Lin +1
Momentum Contrast (MoCo) achieves great success for unsupervised visual representation. However, there are a lot of supervised and semi-supervised datasets, which are already label…
UP-DETR: Unsupervised Pre-training for Object Detection with Transformers
Zhigang Dai, Bolun Cai, Yugeng Lin +1
DEtection TRansformer (DETR) for object detection reaches competitive performance compared with Faster R-CNN via a transformer encoder-decoder architecture. However, trained with s…
Listwise View Ranking for Image Cropping
Weirui Lu, Xiaofen Xing, Bolun Cai +1
Rank-based Learning with deep neural network has been widely used for image cropping. However, the performance of ranking-based methods is often poor and this is mainly due to two…
BIT: Biologically Inspired Tracker
Bolun Cai, Xiangmin Xu, Xiaofen Xing +3
Visual tracking is challenging due to image variations caused by various factors, such as object deformation, scale change, illumination change and occlusion. Given the superior tr…
Deep Sampling Networks
Bolun Cai, Xiangmin Xu, Kailing Guo +2
Deep convolutional neural networks achieve excellent image up-sampling performance. However, CNN-based methods tend to restore high-resolution results highly depending on tradition…
FReLU: Flexible Rectified Linear Units for Improving Convolutional Neural Networks
Suo Qiu, Xiangmin Xu, Bolun Cai
Rectified linear unit (ReLU) is a widely used activation function for deep convolutional neural networks. However, because of the zero-hard rectification, ReLU networks miss the be…
Single Image Super-Resolution Using Multi-Scale Convolutional Neural Network
Xiaoyi Jia, Xiangmin Xu, Bolun Cai +1
Methods based on convolutional neural network (CNN) have demonstrated tremendous improvements on single image super-resolution. However, the previous methods mainly restore images…
Multi-scale Convolutional Neural Networks for Crowd Counting
Lingke Zeng, Xiangmin Xu, Bolun Cai +2
Crowd counting on static images is a challenging problem due to scale variations. Recently deep neural networks have been shown to be effective in this task. However, existing neur…
DehazeNet: An End-to-End System for Single Image Haze Removal
Bolun Cai, Xiangmin Xu, Kui Jia +2
Single image haze removal is a challenging ill-posed problem. Existing methods use various constraints/priors to get plausible dehazing solutions. The key to achieve haze removal i…