activity
20192026
most citedPerson Re-identification in Aerial Imagery

122 citations · 311 across the 25 of their papers we have counts for

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Showing 2023Show all

9 papers · 1 filter

cs.CV2023★ 3 cited

Open-Vocabulary Video Anomaly Detection

Peng Wu, Xuerong Zhou, Guansong Pang +4

Video anomaly detection (VAD) with weak supervision has achieved remarkable performance in utilizing video-level labels to discriminate whether a video frame is normal or abnormal.…

cs.CV2023

Human-centric Behavior Description in Videos: New Benchmark and Model

Lingru Zhou, Yiqi Gao, Manqing Zhang +3

In the domain of video surveillance, describing the behavior of each individual within the video is becoming increasingly essential, especially in complex scenarios with multiple i…

cs.CV2023

Zero-Shot Object Goal Visual Navigation With Class-Independent Relationship Network

Xinting Li, Shiguang Zhang, Yue LU +2

This paper investigates the zero-shot object goal visual navigation problem. In the object goal visual navigation task, the agent needs to locate navigation targets from its egocen…

cs.CV2023★ 16 cited

Ground-to-Aerial Person Search: Benchmark Dataset and Approach

Shizhou Zhang, Qingchun Yang, De Cheng +4

In this work, we construct a large-scale dataset for Ground-to-Aerial Person Search, named G2APS, which contains 31,770 images of 260,559 annotated bounding boxes for 2,644 identit…

cs.CV2023★ 2 cited

Pre-train, Adapt and Detect: Multi-Task Adapter Tuning for Camouflaged Object Detection

Yinghui Xing, Dexuan Kong, Shizhou Zhang +4

Camouflaged object detection (COD), aiming to segment camouflaged objects which exhibit similar patterns with the background, is a challenging task. Most existing works are dedicat…

cs.CV2023★ 2 cited

Towards Video Anomaly Retrieval from Video Anomaly Detection: New Benchmarks and Model

Peng Wu, Jing Liu, Xiangteng He +3

Video anomaly detection (VAD) has been paid increasing attention due to its potential applications, its current dominant tasks focus on online detecting anomalies% at the frame lev…