Efficient Video-based Pedestrian Attribute Recognition using Spatiotemporal Side Tuning of Pre-trained Foundation Models

Core Concepts

A novel video-based pedestrian attribute recognition framework that efficiently fine-tunes a pre-trained multi-modal foundation model using a spatiotemporal side tuning strategy to capture global visual features and align vision-language information.

Abstract

The proposed framework, termed VTFPAR++, formulates video-based pedestrian attribute recognition as a vision-language fusion problem. It adopts the pre-trained CLIP model as the backbone to extract visual and text features. To efficiently fine-tune the large pre-trained model, a novel spatiotemporal side tuning strategy is introduced. Specifically, the framework first encodes the input pedestrian video frames using the CLIP vision encoder. Lightweight spatial and temporal side networks are introduced to aggregate multi-scale visual features from different Transformer layers and model temporal relationships across frames, respectively. These spatiotemporal features are then fused with the text features of the attribute descriptions using a multi-modal Transformer. Finally, the enhanced features are fed into an attribute prediction head for the final recognition. The spatiotemporal side tuning strategy allows the framework to adapt the pre-trained CLIP model to the video-based pedestrian attribute recognition task efficiently, with only a small number of parameters being fine-tuned. Extensive experiments on two large-scale video-based pedestrian attribute recognition datasets demonstrate that the proposed VTFPAR++ outperforms state-of-the-art methods in terms of accuracy, while requiring lower GPU memory consumption and fewer parameter adjustments.

Stats

The video frames can provide more comprehensive visual information for pedestrian attribute recognition compared to a single RGB frame. Existing video-based pedestrian attribute recognition methods often fail to capture the global relations in the pixel-level space and align the vision-language information well.

Quotes

"The video frames can provide more comprehensive visual information for the specific attribute, but the static image fails to." "How to design a novel video-based pedestrian attribute recognition framework that simultaneously captures the global features of vision data, and aligns the vision and semantic attribute labels well?"

Key Insights Distilled From

Spatio-Temporal Side Tuning Pre-trained Foundation Models for Video-based Pedestrian Attribute Recognition

by Xiao Wang,Qi... at arxiv.org 04-30-2024

https://arxiv.org/pdf/2404.17929.pdf

Deeper Inquiries

How can the proposed spatiotemporal side tuning strategy be extended to other video-based vision-language tasks beyond pedestrian attribute recognition

The proposed spatiotemporal side tuning strategy can be extended to other video-based vision-language tasks beyond pedestrian attribute recognition by adapting the framework to different datasets and tasks. For instance, in action recognition tasks, the spatial side network can focus on extracting spatial features from different frames to capture the motion patterns effectively. The temporal side network can then model the temporal relationships between frames to enhance the understanding of action sequences. By fine-tuning the pre-trained vision-language model with spatiotemporal side networks, the model can learn to extract relevant features and interactions for specific tasks, such as activity recognition, event detection, or video captioning. This approach can improve the model's performance in understanding complex visual scenes and linguistic descriptions in various video-based applications.

What are the potential limitations of the current approach in handling highly occluded or blurred pedestrian videos, and how can it be further improved

One potential limitation of the current approach in handling highly occluded or blurred pedestrian videos is the reliance on pre-trained models, which may not be robust to such challenging scenarios. The model's performance may degrade when faced with significant occlusions or motion blur, as the pre-trained features may not adequately capture the relevant information in these conditions. To address this limitation, the framework can be further improved by incorporating data augmentation techniques specifically designed to simulate occlusions and blur in training data. By training the model on a more diverse set of data that includes various levels of occlusions and blur, the model can learn to adapt to these challenging conditions and improve its robustness in real-world scenarios. Additionally, integrating attention mechanisms or spatial-temporal modeling techniques that are more resilient to occlusions and blur can enhance the model's ability to extract meaningful features from obscured or distorted video frames.

What insights can be drawn from the proposed framework to guide the development of more efficient and generalizable multi-modal foundation models for various computer vision applications

The proposed framework provides valuable insights for developing more efficient and generalizable multi-modal foundation models for various computer vision applications. By leveraging pre-trained vision-language models and incorporating spatiotemporal side tuning strategies, the framework demonstrates the effectiveness of parameter-efficient optimization and feature extraction in video-based tasks. These insights can guide the development of future multi-modal models by emphasizing the importance of incorporating both spatial and temporal information for comprehensive understanding of visual data. Additionally, the framework highlights the significance of fusion Transformer networks for interactive learning between visual and textual modalities, paving the way for more effective integration of different data sources in multi-modal tasks. By building upon these insights, researchers can design more versatile and adaptable models that can handle a wide range of vision-language tasks with improved efficiency and performance.