652 citations · 1.5k across the 9 of their papers we have counts for
9 papers
Joint Unsupervised Learning of Optical Flow and Depth by Watching Stereo Videos
Yang Wang, Zhenheng Yang, Peng Wang +3
Learning depth and optical flow via deep neural networks by watching videos has made significant progress recently. In this paper, we jointly solve the two tasks by exploiting the…
Convolutional Neural Networks with Recurrent Neural Filters
Yi Yang
We introduce a class of convolutional neural networks (CNNs) that utilize recurrent neural networks (RNNs) as convolution filters. A convolution filter is typically implemented as…
Unsupervised Learning Layers for Video Analysis
Liang Zhao, Yang Wang, Yi Yang +1
This paper presents two unsupervised learning layers (UL layers) for label-free video analysis: one for fully connected layers, and the other for convolutional ones. The proposed U…
CNN-RNN: A Unified Framework for Multi-label Image Classification
Jiang Wang, Yi Yang, Junhua Mao +3
While deep convolutional neural networks (CNNs) have shown a great success in single-label image classification, it is important to note that real world images generally contain mu…
Attention to Scale: Scale-aware Semantic Image Segmentation
Liang-Chieh Chen, Yi Yang, Jiang Wang +2
Incorporating multi-scale features in fully convolutional neural networks (FCNs) has been a key element to achieving state-of-the-art performance on semantic image segmentation. On…
Video Paragraph Captioning Using Hierarchical Recurrent Neural Networks
Haonan Yu, Jiang Wang, Zhiheng Huang +2
We present an approach that exploits hierarchical Recurrent Neural Networks (RNNs) to tackle the video captioning problem, i.e., generating one or multiple sentences to describe a…