activity
20142021
most citedAttentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks

119 citations · 422 across the 18 of their papers we have counts for

collaborators

18 papers

cs.CV2021★ 25 cited

Relational Graph Learning for Grounded Video Description Generation

Wenqiao Zhang, Xin Eric Wang, Siliang Tang +5

Grounded video description (GVD) encourages captioning models to attend to appropriate video regions (e.g., objects) dynamically and generate a description. Such a setting can help…

cs.CV2021★ 2 cited

Consensus Graph Representation Learning for Better Grounded Image Captioning

Wenqiao Zhang, Haochen Shi, Siliang Tang +3

The contemporary visual captioning models frequently hallucinate objects that are not actually in a scene, due to the visual misclassification or over-reliance on priors that resul…

cs.CV2021★ 1 cited

Natural Language Video Localization with Learnable Moment Proposals

Shaoning Xiao, Long Chen, Jian Shao +2

Given an untrimmed video and a natural language query, Natural Language Video Localization (NLVL) aims to identify the video moment described by the query. To address this task, ex…

cs.LG2020★ 38 cited

Federated Unsupervised Representation Learning

Fengda Zhang, Kun Kuang, Zhaoyang You +6

To leverage enormous unlabeled data on distributed edge devices, we formulate a new problem in federated learning called Federated Unsupervised Representation Learning (FURL) to le…

cs.CL2020★ 9 cited

Topic Adaptation and Prototype Encoding for Few-Shot Visual Storytelling

Jiacheng Li, Siliang Tang, Juncheng Li +4

Visual Storytelling~(VIST) is a task to tell a narrative story about a certain topic according to the given photo stream. The existing studies focus on designing complex models, wh…

cs.IR2020★ 12 cited

Hierarchical Fashion Graph Network for Personalized Outfit Recommendation

Xingchen Li, Xiang Wang, Xiangnan He +3

Fashion outfit recommendation has attracted increasing attentions from online shopping services and fashion communities.Distinct from other scenarios (e.g., social networking or co…