Leveraging Text-Image Alignment and Temporal Adaptivity for Weakly Supervised Video Anomaly Detection

Core Concepts

A novel pseudo-label generation and self-training framework that utilizes text-image alignment capabilities of CLIP and adaptive temporal modeling to achieve state-of-the-art performance on video anomaly detection tasks.

Abstract

The paper proposes a novel framework called TPWNG for weakly supervised video anomaly detection (WSVAD). The key ideas are:

Utilizing the text-image alignment capabilities of the CLIP model to generate more accurate pseudo-labels for video frames. This is done by:

Fine-tuning the CLIP text encoder with ranking losses and a distributional inconsistency loss for domain adaptation.
Employing learnable text prompts and a normality visual prompt mechanism to further improve the text-image alignment.
Designing a pseudo-label generation (PLG) module that incorporates normality guidance to reduce interference from normal frames in anomalous videos.

Introducing a temporal context self-adaptive learning (TCSAL) module to adaptively learn the temporal dependencies of different video events, enabling more flexible and accurate modeling of temporal information.

The proposed TPWNG framework is evaluated on two benchmark datasets, UCF-Crime and XD-Violence, where it achieves state-of-the-art performance, outperforming previous methods by a significant margin. The ablation studies demonstrate the effectiveness of the key components, including the normality visual prompt, normality guidance, and the TCSAL module.

Stats

The UCF-Crime dataset contains 1900 surveillance videos covering 13 anomaly event categories, with 1610 training videos and 290 test videos.
The XD-Violence dataset contains 4754 videos covering 6 anomaly event categories, with 3954 training videos and 800 test videos.

Quotes

"Inspired by the manual labeling process based on the event description, in this paper, we propose a novel pseudo-label generation and self-training framework based on Text Prompt with Normality Guidance (TPWNG) for WSVAD."
"To further exploit the potential of pseudo-label-based self-training on WSVAD, we dedicate to investigating the two problems mentioned above in this paper."

Key Insights Distilled From

Text Prompt with Normality Guidance for Weakly Supervised Video Anomaly Detection

by Zhiwei Yang,... at arxiv.org 04-15-2024

https://arxiv.org/pdf/2404.08531.pdf

Deeper Inquiries

How can the proposed TPWNG framework be extended to other video understanding tasks beyond anomaly detection, such as action recognition or video summarization

The TPWNG framework proposed in this work can be extended to other video understanding tasks beyond anomaly detection by adapting the text prompt with normality guidance approach to suit the specific requirements of different tasks. For action recognition, the framework can be modified to align action descriptions with video frames to generate pseudo-labels for training action recognition models. By fine-tuning the CLIP model with action-related text prompts and incorporating normality visual prompts, the framework can effectively learn the associations between action descriptions and corresponding video frames. Similarly, for video summarization, the framework can be adjusted to align key event descriptions with video segments to identify important moments for summarization. By leveraging the rich language-visual knowledge of the CLIP model and incorporating normality guidance mechanisms, the framework can generate accurate pseudo-labels for identifying significant video segments for summarization.

What are the potential limitations of the text-image alignment approach used in this work, and how could it be further improved to handle more complex or ambiguous video-text relationships

The text-image alignment approach used in this work may have potential limitations when handling more complex or ambiguous video-text relationships. One limitation could be the reliance on predefined text prompts, which may not capture all nuances or variations in the video content. To address this limitation, the approach could be further improved by incorporating a more dynamic and adaptive text prompt generation mechanism. This mechanism could involve leveraging reinforcement learning or attention mechanisms to dynamically adjust the text prompts based on the video content, allowing for more flexible and accurate alignment between text descriptions and video frames. Additionally, exploring multimodal fusion techniques to combine information from both text and visual modalities more effectively could enhance the alignment process and improve the accuracy of pseudo-label generation.

Given the adaptive temporal modeling capabilities of the TCSAL module, could it be applied to other video processing tasks that require flexible handling of temporal information, such as video prediction or video editing

The adaptive temporal modeling capabilities of the TCSAL module can be applied to other video processing tasks that require flexible handling of temporal information, such as video prediction or video editing. For video prediction tasks, the TCSAL module can adaptively adjust the attention span range based on the input video frames, allowing the model to capture long-range dependencies and predict future frames more accurately. By dynamically adjusting the attention span, the model can focus on relevant temporal information and improve the prediction quality. In video editing tasks, the TCSAL module can help in identifying and aligning key temporal dependencies in the video sequence, enabling more precise editing decisions and enhancing the overall editing process. By incorporating the TCSAL module into these tasks, the models can better understand and utilize temporal relationships in videos, leading to improved performance and efficiency.