219 citations · 232 across the 3 of their papers we have counts for
7 papers
Decoupling Representation and Classifier for Long-Tailed Recognition
Bingyi Kang, Saining Xie, Marcus Rohrbach +4
The long-tail distribution of the visual world poses great challenges for deep learning based classification models on how to handle the class imbalance problem. Existing solutions…
Only Time Can Tell: Discovering Temporal Data for Temporal Modeling
Laura Sevilla-Lara, Shengxin Zha, Zhicheng Yan +3
Understanding temporal information and how the visual world changes over time is a fundamental ability of intelligent systems. In video understanding, temporal information is at th…
Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution
Yunpeng Chen, Haoqi Fan, Bing Xu +5
In natural images, information is conveyed at different frequencies where higher frequencies are usually encoded with fine details and lower frequencies are usually encoded with gl…
DMC-Net: Generating Discriminative Motion Cues for Fast Compressed Video Action Recognition
Zheng Shou, Xudong Lin, Yannis Kalantidis +4
Motion has shown to be useful for video understanding, where motion is typically represented by optical flow. However, computing flow from video frames is very time-consuming. Rece…
Graph-Based Global Reasoning Networks
Yunpeng Chen, Marcus Rohrbach, Zhicheng Yan +3
Globally modeling and reasoning over relations between regions can be beneficial for many computer vision tasks on both images and videos. Convolutional Neural Networks (CNNs) exce…
Learning Concept Taxonomies from Multi-modal Data
Hao Zhang, Zhiting Hu, Yuntian Deng +3
We study the problem of automatically building hypernym taxonomies from textual and visual data. Previous works in taxonomy induction generally ignore the increasingly prominent vi…