activity
20122020
most citedContrastive Adaptation Network for Unsupervised Domain Adaptation

101 citations · 246 across the 14 of their papers we have counts for

collaborators

35 papers

cs.CV20203 cited

Spatial-Temporal Alignment Network for Action Recognition and Detection

Junwei Liang, Liangliang Cao, Xuehan Xiong +2

This paper studies how to introduce viewpoint-invariant feature representations that can help action recognition and detection. Although we have witnessed great progress of action…

cs.CL20201 cited

Event-Related Bias Removal for Real-time Disaster Events

Evangelia Spiliopoulou, Salvador Medina Maza, Eduard Hovy +1

Social media has become an important tool to share information about crisis events such as natural disasters and mass attacks. Detecting actionable posts that contain useful inform…

cs.CV2020

Support-set bottlenecks for video-text representation learning

Mandela Patrick, Po-Yao Huang, Yuki Asano +4

The dominant paradigm for learning video-text representations -- noise contrastive learning -- increases the similarity of the representations of pairs of samples that are known to…

cs.CV2020

Robust Long-Term Object Tracking via Improved Discriminative Model Prediction

Seokeon Choi, Junhyun Lee, Yunsung Lee +1

We propose an improved discriminative model prediction method for robust long-term tracking based on a pre-trained short-term tracker. The baseline pre-trained short-term tracker i…

cs.CV20204 cited

From A Glance to "Gotcha": Interactive Facial Image Retrieval with Progressive Relevance Feedback

Xinru Yang, Haozhi Qi, Mingyang Li +1

Facial image retrieval plays a significant role in forensic investigations where an untrained witness tries to identify a suspect from a massive pool of images. However, due to the…

cs.CV2020

MSNet: A Multilevel Instance Segmentation Network for Natural Disaster Damage Assessment in Aerial Videos

Xiaoyu Zhu, Junwei Liang, Alexander Hauptmann

In this paper, we study the problem of efficiently assessing building damage after natural disasters like hurricanes, floods or fires, through aerial video analysis. We make two ma…