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20212024
most citedLDC-Net: A Unified Framework for Localization, Detection and Counting in Dense Crowds

7 citations · 32 across the 9 of their papers we have counts for

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8 papers · 1 filter

cs.CV2024★ 4 cited

SamLP: A Customized Segment Anything Model for License Plate Detection

Haoxuan Ding, Junyu Gao, Yuan Yuan +1

With the emergence of foundation model, this novel paradigm of deep learning has encouraged many powerful achievements in natural language processing and computer vision. There are…

cs.CV2023

Imbalanced Aircraft Data Anomaly Detection

Hao Yang, Junyu Gao, Yuan Yuan +1

Anomaly detection in temporal data from sensors under aviation scenarios is a practical but challenging task: 1) long temporal data is difficult to extract contextual information w…

cs.CV2022★ 2 cited

Counting Like Human: Anthropoid Crowd Counting on Modeling the Similarity of Objects

Qi Wang, Juncheng Wang, Junyu Gao +2

The mainstream crowd counting methods regress density map and integrate it to obtain counting results. Since the density representation to one head accords to its adjacent distribu…

cs.CV2022★ 1 cited

DR.VIC: Decomposition and Reasoning for Video Individual Counting

Tao Han, Lei Bai, Junyu Gao +2

Pedestrian counting is a fundamental tool for understanding pedestrian patterns and crowd flow analysis. Existing works (e.g., image-level pedestrian counting, crossline crowd coun…

cs.CV2021★ 6 cited

Audio-visual Representation Learning for Anomaly Events Detection in Crowds

Junyu Gao, Maoguo Gong, Xuelong Li

In recent years, anomaly events detection in crowd scenes attracts many researchers' attention, because of its importance to public safety. Existing methods usually exploit visual…

cs.CV2021★ 2 cited

Unsupervised Domain Adaptive Learning via Synthetic Data for Person Re-identification

Qi Wang, Sikai Bai, Junyu Gao +2

Person re-identification (re-ID) has gained more and more attention due to its widespread applications in intelligent video surveillance. Unfortunately, the mainstream deep learnin…