15 citations · 55 across the 12 of their papers we have counts for
12 papers
Universal Model for Multi-Domain Medical Image Retrieval
Yang Feng, Yubao Liu, Jiebo Luo
Medical Image Retrieval (MIR) helps doctors quickly find similar patients' data, which can considerably aid the diagnosis process. MIR is becoming increasingly helpful due to the w…
Video-based Person Re-Identification using Gated Convolutional Recurrent Neural Networks
Yang Feng, Yu Wang, Jiebo Luo
Deep neural networks have been successfully applied to solving the video-based person re-identification problem with impressive results reported. The existing networks for person r…
Unifying Specialist Image Embedding into Universal Image Embedding
Yang Feng, Futang Peng, Xu Zhang +7
Deep image embedding provides a way to measure the semantic similarity of two images. It plays a central role in many applications such as image search, face verification, and zero…
Spatio-temporal Video Re-localization by Warp LSTM
Yang Feng, Lin Ma, Wei Liu +1
The need for efficiently finding the video content a user wants is increasing because of the erupting of user-generated videos on the Web. Existing keyword-based or content-based v…
Unsupervised Image Captioning
Yang Feng, Lin Ma, Wei Liu +1
Deep neural networks have achieved great successes on the image captioning task. However, most of the existing models depend heavily on paired image-sentence datasets, which are ve…
Video Re-localization
Yang Feng, Lin Ma, Wei Liu +2
Many methods have been developed to help people find the video contents they want efficiently. However, there are still some unsolved problems in this area. For example, given a qu…
How Polarized Have We Become? A Multimodal Classification of Trump Followers and Clinton Followers
Yu Wang, Yang Feng, Zhe Hong +2
Polarization in American politics has been extensively documented and analyzed for decades, and the phenomenon became all the more apparent during the 2016 presidential election, w…
Tactics and Tallies: Inferring Voter Preferences in the 2016 U.S. Presidential Primaries Using Sparse Learning
Yu Wang, Yang Feng, Xiyang Zhang +1
In this paper, we propose a web-centered framework to infer voter preferences for the 2016 U.S. presidential primaries. Using Twitter data collected from Sept. 2015 to March 2016,…
Gender Politics in the 2016 U.S. Presidential Election: A Computer Vision Approach
Yu Wang, Yang Feng, Xiyang Zhang +1
Gender is playing an important role in the 2016 U.S. presidential election, especially with Hillary Clinton becoming the first female presidential nominee and Donald Trump being fr…
Do They All Look the Same? Deciphering Chinese, Japanese and Koreans by Fine-Grained Deep Learning
Yu Wang, Haofu Liao, Yang Feng +2
We study to what extend Chinese, Japanese and Korean faces can be classified and which facial attributes offer the most important cues. First, we propose a novel way of obtaining l…
Will Sanders Supporters Jump Ship for Trump? Fine-grained Analysis of Twitter Followers
Yu Wang, Yang Feng, Xiyang Zhang +2
In this paper, we study the likelihood of Bernie Sanders supporters voting for Donald Trump instead of Hillary Clinton. Building from a unique time-series dataset of the three cand…
Voting with Feet: Who are Leaving Hillary Clinton and Donald Trump?
Yu Wang, Yuncheng Li, Quanzeng You +3
From a crowded field with 17 candidates, Hillary Clinton and Donald Trump have emerged as the two front-runners in the 2016 U.S. presidential campaign. The two candidates each boas…