584 citations · 1.9k across the 94 of their papers we have counts for
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An End-to-End Audio Classification System based on Raw Waveforms and Mix-Training Strategy
Jiaxu Chen, Jing Hao, Kai Chen +3
Audio classification can distinguish different kinds of sounds, which is helpful for intelligent applications in daily life. However, it remains a challenging task since the sound…
Adversarial Seeded Sequence Growing for Weakly-Supervised Temporal Action Localization
Chengwei Zhang, Yunlu Xu, Zhanzhan Cheng +4
Temporal action localization is an important yet challenging research topic due to its various applications. Since the frame-level or segment-level annotations of untrimmed videos…
Learned Quality Enhancement via Multi-Frame Priors for HEVC Compliant Low-Delay Applications
Ming Lu, Ming Cheng, Yiling Xu +3
Networked video applications, e.g., video conferencing, often suffer from poor visual quality due to unexpected network fluctuation and limited bandwidth. In this paper, we have de…
Posterior-regularized REINFORCE for Instance Selection in Distant Supervision
Qi Zhang, Siliang Tang, Xiang Ren +3
This paper provides a new way to improve the efficiency of the REINFORCE training process. We apply it to the task of instance selection in distant supervision. Modeling the instan…
UG Track 2: A Collective Benchmark Effort for Evaluating and Advancing Image Understanding in Poor Visibility Environments
Ye Yuan, Wenhan Yang, Wenqi Ren +3
The UG challenge in IEEE CVPR 2019 aims to evoke a comprehensive discussion and exploration about how low-level vision techniques can benefit the high-level automatic visual…
All You Need is a Few Shifts: Designing Efficient Convolutional Neural Networks for Image Classification
Weijie Chen, Di Xie, Yuan Zhang +1
Shift operation is an efficient alternative over depthwise separable convolution. However, it is still bottlenecked by its implementation manner, namely memory movement. To put thi…
Collaborative Spatio-temporal Feature Learning for Video Action Recognition
Chao Li, Qiaoyong Zhong, Di Xie +1
Spatio-temporal feature learning is of central importance for action recognition in videos. Existing deep neural network models either learn spatial and temporal features independe…
You Only Recognize Once: Towards Fast Video Text Spotting
Zhanzhan Cheng, Jing Lu, Yi Niu +3
Video text spotting is still an important research topic due to its various real-applications. Previous approaches usually fall into the four-staged pipeline: text detection in ind…