5.3k citations · 12.2k across the 85 of their papers we have counts for
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Exploring Emotion Features and Fusion Strategies for Audio-Video Emotion Recognition
Hengshun Zhou, Debin Meng, Yuanyuan Zhang +4
The audio-video based emotion recognition aims to classify a given video into basic emotions. In this paper, we describe our approaches in EmotiW 2019, which mainly explores emotio…
Learning Geometry-Disentangled Representation for Complementary Understanding of 3D Object Point Cloud
Mutian Xu, Junhao Zhang, Zhipeng Zhou +3
In 2D image processing, some attempts decompose images into high and low frequency components for describing edge and smooth parts respectively. Similarly, the contour and flat are…
Exploring Multi-Scale Feature Propagation and Communication for Image Super Resolution
Ruicheng Feng, Weipeng Guan, Yu Qiao +1
Multi-scale techniques have achieved great success in a wide range of computer vision tasks. However, while this technique is incorporated in existing works, there still lacks a co…
Visual Compositional Learning for Human-Object Interaction Detection
Zhi Hou, Xiaojiang Peng, Yu Qiao +1
Human-Object interaction (HOI) detection aims to localize and infer relationships between human and objects in an image. It is challenging because an enormous number of possible co…
SmallBigNet: Integrating Core and Contextual Views for Video Classification
Xianhang Li, Yali Wang, Zhipeng Zhou +1
Temporal convolution has been widely used for video classification. However, it is performed on spatio-temporal contexts in a limited view, which often weakens its capacity of lear…
COCAS: A Large-Scale Clothes Changing Person Dataset for Re-identification
Shijie Yu, Shihua Li, Dapeng Chen +3
Recent years have witnessed great progress in person re-identification (re-id). Several academic benchmarks such as Market1501, CUHK03 and DukeMTMC play important roles to promote…
Learning to Predict Context-adaptive Convolution for Semantic Segmentation
Jianbo Liu, Junjun He, Jimmy S. Ren +2
Long-range contextual information is essential for achieving high-performance semantic segmentation. Previous feature re-weighting methods demonstrate that using global context for…
Context-Transformer: Tackling Object Confusion for Few-Shot Detection
Ze Yang, Yali Wang, Xianyu Chen +2
Few-shot object detection is a challenging but realistic scenario, where only a few annotated training images are available for training detectors. A popular approach to handle thi…
TTPP: Temporal Transformer with Progressive Prediction for Efficient Action Anticipation
Wen Wang, Xiaojiang Peng, Yanzhou Su +2
Video action anticipation aims to predict future action categories from observed frames. Current state-of-the-art approaches mainly resort to recurrent neural networks to encode hi…
Suppressing Uncertainties for Large-Scale Facial Expression Recognition
Kai Wang, Xiaojiang Peng, Jianfei Yang +2
Annotating a qualitative large-scale facial expression dataset is extremely difficult due to the uncertainties caused by ambiguous facial expressions, low-quality facial images, an…
Domain Adaptive Ensemble Learning
Kaiyang Zhou, Yongxin Yang, Yu Qiao +1
The problem of generalizing deep neural networks from multiple source domains to a target one is studied under two settings: When unlabeled target data is available, it is a multi-…
Learning Attentive Pairwise Interaction for Fine-Grained Classification
Peiqin Zhuang, Yali Wang, Yu Qiao
Fine-grained classification is a challenging problem, due to subtle differences among highly-confused categories. Most approaches address this difficulty by learning discriminative…
Progressive Object Transfer Detection
Hao Chen, Yali Wang, Guoyou Wang +2
Recent development of object detection mainly depends on deep learning with large-scale benchmarks. However, collecting such fully-annotated data is often difficult or expensive fo…
Refined Gate: A Simple and Effective Gating Mechanism for Recurrent Units
Zhanzhan Cheng, Yunlu Xu, Mingjian Cheng +4
Recurrent neural network (RNN) has been widely studied in sequence learning tasks, while the mainstream models (e.g., LSTM and GRU) rely on the gating mechanism (in control of how…
A Comprehensive Study on Temporal Modeling for Online Action Detection
Wen Wang, Xiaojiang Peng, Yu Qiao +1
Online action detection (OAD) is a practical yet challenging task, which has attracted increasing attention in recent years. A typical OAD system mainly consists of three modules:…
FD-GAN: Generative Adversarial Networks with Fusion-discriminator for Single Image Dehazing
Yu Dong, Yihao Liu, He Zhang +2
Recently, convolutional neural networks (CNNs) have achieved great improvements in single image dehazing and attained much attention in research. Most existing learning-based dehaz…