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|a 10.1109/TIP.2024.3374070
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|a eng
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| 100 |
1 |
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|a Wu, Peng
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Toward Video Anomaly Retrieval From Video Anomaly Detection
|b New Benchmarks and Model
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|c 2024
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|a Date Revised 26.03.2024
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Video anomaly detection (VAD) has been paid increasing attention due to its potential applications, its current dominant tasks focus on online detecting anomalies, which can be roughly interpreted as the binary or multiple event classification. However, such a setup that builds relationships between complicated anomalous events and single labels, e.g., "vandalism", is superficial, since single labels are deficient to characterize anomalous events. In reality, users tend to search a specific video rather than a series of approximate videos. Therefore, retrieving anomalous events using detailed descriptions is practical and positive but few researches focus on this. In this context, we propose a novel task called Video Anomaly Retrieval (VAR), which aims to pragmatically retrieve relevant anomalous videos by cross-modalities, e.g., language descriptions and synchronous audios. Unlike the current video retrieval where videos are assumed to be temporally well-trimmed with short duration, VAR is devised to retrieve long untrimmed videos which may be partially relevant to the given query. To achieve this, we present two large-scale VAR benchmarks and design a model called Anomaly-Led Alignment Network (ALAN) for VAR. In ALAN, we propose an anomaly-led sampling to focus on key segments in long untrimmed videos. Then, we introduce an efficient pretext task to enhance semantic associations between video-text fine-grained representations. Besides, we leverage two complementary alignments to further match cross-modal contents. Experimental results on two benchmarks reveal the challenges of VAR task and also demonstrate the advantages of our tailored method. Captions are publicly released at https://github.com/Roc-Ng/VAR
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|a Journal Article
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1 |
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|a Liu, Jing
|e verfasserin
|4 aut
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| 700 |
1 |
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|a He, Xiangteng
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Peng, Yuxin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wang, Peng
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhang, Yanning
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 33(2024) vom: 20., Seite 2213-2225
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|x 1941-0042
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|g volume:33
|g year:2024
|g day:20
|g pages:2213-2225
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|u http://dx.doi.org/10.1109/TIP.2024.3374070
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