activity
20172022
most citedMotion-Aware Feature for Improved Video Anomaly Detection

116 citations · 140 across the 10 of their papers we have counts for

collaborators

19 papers

cs.CV20222 cited

DistPro: Searching A Fast Knowledge Distillation Process via Meta Optimization

Xueqing Deng, Dawei Sun, Shawn Newsam +1

Recent Knowledge distillation (KD) studies show that different manually designed schemes impact the learned results significantly. Yet, in KD, automatically searching an optimal di…

cs.CV20223 cited

NightLab: A Dual-level Architecture with Hardness Detection for Segmentation at Night

Xueqing Deng, Peng Wang, Xiaochen Lian +1

The semantic segmentation of nighttime scenes is a challenging problem that is key to impactful applications like self-driving cars. Yet, it has received little attention compared…

cs.CV20225 cited

Image Search with Text Feedback by Additive Attention Compositional Learning

Yuxin Tian, Shawn Newsam, Kofi Boakye

Effective image retrieval with text feedback stands to impact a range of real-world applications, such as e-commerce. Given a source image and text feedback that describes the desi…

cs.CV20213 cited

AutoAdapt: Automated Segmentation Network Search for Unsupervised Domain Adaptation

Xueqing Deng, Yi Zhu, Yuxin Tian +1

Neural network-based semantic segmentation has achieved remarkable results when large amounts of annotated data are available, that is, in the supervised case. However, such data i…

cs.CV20201 cited

Scale Aware Adaptation for Land-Cover Classification in Remote Sensing Imagery

Xueqing Deng, Yi Zhu, Yuxin Tian +1

Land-cover classification using remote sensing imagery is an important Earth observation task. Recently, land cover classification has benefited from the development of fully conne…

cs.CV2019116 cited

Motion-Aware Feature for Improved Video Anomaly Detection

Yi Zhu, Shawn Newsam

Motivated by our observation that motion information is the key to good anomaly detection performance in video, we propose a temporal augmented network to learn a motion-aware feat…