Papers › VadCLIP: Adapting Vision-Language Models for Weakly Supervised Video Anomaly Detection

VadCLIP: Adapting Vision-Language Models for Weakly Supervised Video Anomaly Detection

22 Aug 2023arXiv:2308.11681archive 2025-07-28

Peng Wu, Xuerong Zhou, Guansong Pang, Lingru Zhou, Qingsen Yan, Peng Wang, Yanning Zhang

The recent contrastive language-image pre-training (CLIP) model has shown great success in a wide range of image-level tasks, revealing remarkable ability for learning powerful visual representations with rich semantics. An open and worthwhile problem is efficiently adapting such a strong model to the video domain and designing a robust video anomaly detector. In this work, we propose VadCLIP, a new paradigm for weakly supervised video anomaly detection (WSVAD) by leveraging the frozen CLIP model directly without any pre-training and fine-tuning process. Unlike current works that directly feed extracted features into the weakly supervised classifier for frame-level binary classification, VadCLIP makes full use of fine-grained associations between vision and language on the strength of CLIP and involves dual branch. One branch simply utilizes visual features for coarse-grained binary classification, while the other fully leverages the fine-grained language-image alignment. With the benefit of dual branch, VadCLIP achieves both coarse-grained and fine-grained video anomaly detection by transferring pre-trained knowledge from CLIP to WSVAD task. We conduct extensive experiments on two commonly-used benchmarks, demonstrating that VadCLIP achieves the best performance on both coarse-grained and fine-grained WSVAD, surpassing the state-of-the-art methods by a large margin. Specifically, VadCLIP achieves 84.51% AP and 88.02% AUC on XD-Violence and UCF-Crime, respectively. Code and features are released at https://github.com/nwpu-zxr/VadCLIP.

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CLAS2 nwpu-zxr/vadclip/src/ucf_train.py official repository ran Apache-2.0 (permissive) · 722deffccfe046dc · report
CLAS2 nwpu-zxr/vadclip/src/xd_train.py official repository ran Apache-2.0 (permissive) · 39a9a85ba0b976d9 · report
image_crop nwpu-zxr/vadclip/src/crop.py official repository ran Apache-2.0 (permissive) · 8126e661e2da6d6e · report
video_crop nwpu-zxr/vadclip/src/crop.py official repository ran Apache-2.0 (permissive) · 2b5eff3ede9ce667 · report
CLASM nwpu-zxr/vadclip/src/ucf_train.py official repository unverified Apache-2.0 (permissive) · be63c876347199d0 · report

Tasks

Anomaly DetectionBinary ClassificationVideo Anomaly DetectionWeakly-supervised Video Anomaly Detection

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Weakly-supervised Video Anomaly Detection ShanghaiTech Weakly Supervised VadCLIP AUC-ROC 97.49 #7 of 16 Archive leaderboard report
Weakly-supervised Video Anomaly Detection UBnormal VadCLIP AUC-ROC 62.32 #8 of 11 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

CLIP

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