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20132022
most citedLearning Spatio-Temporal Representation with Pseudo-3D Residual Networks

251 citations · 2.2k across the 123 of their papers we have counts for

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Showing 2017Show all

6 papers · 1 filter

cs.SI2017★ 14 cited

Time Matters: Multi-scale Temporalization of Social Media Popularity

Bo Wu, Wen-Huang Cheng, Yongdong Zhang +1

The evolution of social media popularity exhibits rich temporality, i.e., popularities change over time at various levels of temporal granularity. This is influenced by temporal va…

cs.SI2017★ 22 cited

Sequential Prediction of Social Media Popularity with Deep Temporal Context Networks

Bo Wu, Wen-Huang Cheng, Yongdong Zhang +3

Prediction of popularity has profound impact for social media, since it offers opportunities to reveal individual preference and public attention from evolutionary social systems.…

cs.CV2017★ 251 cited

Learning Spatio-Temporal Representation with Pseudo-3D Residual Networks

Zhaofan Qiu, Ting Yao, Tao Mei

Convolutional Neural Networks (CNN) have been regarded as a powerful class of models for image recognition problems. Nevertheless, it is not trivial when utilizing a CNN for learni…

cs.MM2017★ 19 cited

Learning Social Image Embedding with Deep Multimodal Attention Networks

Feiran Huang, Xiaoming Zhang, Zhoujun Li +3

Learning social media data embedding by deep models has attracted extensive research interest as well as boomed a lot of applications, such as link prediction, classification, and…

cs.CV2017

Joint Detection and Recounting of Abnormal Events by Learning Deep Generic Knowledge

Ryota Hinami, Tao Mei, Shin'ichi Satoh

This paper addresses the problem of joint detection and recounting of abnormal events in videos. Recounting of abnormal events, i.e., explaining why they are judged to be abnormal,…

cs.CV2017★ 33 cited

Incorporating Copying Mechanism in Image Captioning for Learning Novel Objects

Ting Yao, Yingwei Pan, Yehao Li +1

Image captioning often requires a large set of training image-sentence pairs. In practice, however, acquiring sufficient training pairs is always expensive, making the recent capti…