activity
20162023
most citedLearning to Prune Filters in Convolutional Neural Networks

27 citations · 133 across the 25 of their papers we have counts for

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Showing 2021Show all

8 papers · 1 filter

cs.CV2021★ 5 cited

Unsupervised Lightweight Single Object Tracking with UHP-SOT++

Zhiruo Zhou, Hongyu Fu, Suya You +1

An unsupervised, lightweight and high-performance single object tracker, called UHP-SOT, was proposed by Zhou et al. recently. As an extension, we present an enhanced version and n…

cs.CV2021★ 11 cited

Geo-DefakeHop: High-Performance Geographic Fake Image Detection

Hong-Shuo Chen, Kaitai Zhang, Shuowen Hu +2

A robust fake satellite image detection method, called Geo-DefakeHop, is proposed in this work. Geo-DefakeHop is developed based on the parallel subspace learning (PSL) methodology…

cs.CV2021★ 1 cited

Meta-UDA: Unsupervised Domain Adaptive Thermal Object Detection using Meta-Learning

Vibashan VS, Domenick Poster, Suya You +2

Object detectors trained on large-scale RGB datasets are being extensively employed in real-world applications. However, these RGB-trained models suffer a performance drop under ad…

cs.CV2021★ 1 cited

UHP-SOT: An Unsupervised High-Performance Single Object Tracker

Zhiruo Zhou, Hongyu Fu, Suya You +2

An unsupervised online object tracking method that exploits both foreground and background correlations is proposed and named UHP-SOT (Unsupervised High-Performance Single Object T…

cs.CV2021★ 4 cited

GTNet:Guided Transformer Network for Detecting Human-Object Interactions

A S M Iftekhar, Satish Kumar, R. Austin McEver +2

The human-object interaction (HOI) detection task refers to localizing humans, localizing objects, and predicting the interactions between each human-object pair. HOI is considered…

cs.CV2021

Evaluation of Multimodal Semantic Segmentation using RGB-D Data

Jiesi Hu, Ganning Zhao, Suya You +1

Our goal is to develop stable, accurate, and robust semantic scene understanding methods for wide-area scene perception and understanding, especially in challenging outdoor environ…