Learning Suspected Anomalies from Event Prompts for Video Anomaly Detection

Core Concepts

The author proposes a novel framework to guide the learning of suspected anomalies from event prompts, enhancing weakly supervised video anomaly detection. By utilizing semantic anomaly similarity and multi-prompt learning, the model outperforms state-of-the-art methods in various datasets.

Abstract

The content introduces a novel approach for weakly supervised video anomaly detection by learning suspected anomalies from event prompts. The proposed LAP model leverages semantic features and a prompt dictionary to enhance anomaly detection performance across different datasets. Through comprehensive experiments and ablation studies, the effectiveness of the model is demonstrated, showcasing improvements in open-set and cross-dataset scenarios. The study highlights the importance of incorporating textual abnormal event prompts into video anomaly detection frameworks. By introducing a multi-prompt learning strategy and pseudo labels based on semantic similarity, the LAP model achieves superior performance compared to existing methods. The content emphasizes the significance of leveraging natural language processing techniques in enhancing video anomaly detection accuracy. Key points include: Introduction of LAP model for weakly supervised video anomaly detection. Utilization of semantic features and prompt dictionary for improved performance. Demonstrated effectiveness through experiments on multiple datasets. Importance of integrating textual prompts for enhanced anomaly detection accuracy.

Stats

Most state-of-the-art methods outperformed by proposed model with AP or AUC scores ranging from 82.6% to 97.4%. Prompt dictionary capacity set to 30 for UCF-Crime, XD-Violence, and TAD datasets. Batch size varied between 32 and 64 across different datasets. Hyperparameters α = 1, β = 0.1, γ = 0.001 used consistently. Adam optimizer with learning rate of 0.001 employed during training.

Quotes

"Most models for weakly supervised video anomaly detection rely on multiple instance learning." "A novel framework is proposed to guide the learning of suspected anomalies from event prompts." "The LAP model outperforms most state-of-the-art methods in terms of AP or AUC."

Key Insights Distilled From

Learn Suspected Anomalies from Event Prompts for Video Anomaly Detection

by Chenchen Tao... at arxiv.org 03-05-2024

https://arxiv.org/pdf/2403.01169.pdf

Deeper Inquiries

How can incorporating textual prompts improve the accuracy of video anomaly detection beyond traditional methods?

Incorporating textual prompts in video anomaly detection can significantly enhance accuracy by providing semantic information that guides the model to learn suspected anomalies. Traditional methods often rely solely on visual features, leading to high false alarm rates and low accuracy in detecting ambiguous abnormal events. By introducing a prompt dictionary listing potential anomaly events, the model can compare these prompts with captions generated from anomaly videos to identify suspected anomalous events for each snippet. This approach allows for a more nuanced understanding of anomalies, enabling the model to distinguish between normal and abnormal patterns more effectively. The use of textual prompts also facilitates multi-prompt learning, where visual-semantic features are constrained across different videos based on the identified anomalies. This not only improves overall feature representation but also enables self-training through pseudo labels generated from the semantic similarity between event prompts and video captions. By leveraging this additional information, models like LAP have shown promising results in open-set and cross-dataset scenarios, outperforming state-of-the-art methods in terms of precision-recall curves (AP) or area under the curve (AUC).

How might prompt-based learning impact other areas beyond video anomaly detection?

Prompt-based learning has broader implications beyond video anomaly detection and could potentially revolutionize various fields where multimodal tasks are involved. For instance: Natural Language Processing (NLP): Prompt-tuning techniques have been successful in adapting models to downstream NLP tasks by incorporating semantic guidance through text prompts. In NLP applications such as language modeling or sentiment analysis, prompt-based approaches could lead to improved performance by providing contextual cues for better understanding text data. Computer Vision: In image classification tasks, prompting techniques have demonstrated state-of-the-art performance by transferring semantic information into vision tasks using pretrained foundation multimodality models like CLIP or GPT. Action Recognition: Prompt-guided zero-shot action recognition has shown promise in recognizing actions based on skeleton features and text embeddings shared space refinement decision boundaries. Semi-supervised Learning: Prompt-based learning has been applied successfully in semi-supervised settings for various machine learning tasks where labeled data is scarce but prior knowledge can be leveraged effectively. Overall, integrating prompt-based learning methodologies across different domains could lead to advancements in multimodal AI systems' capabilities by enhancing feature representations and guiding models towards more accurate predictions based on contextual cues provided by textual prompts.