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

TL;DR

This paper introduces a novel spatio-temporal side-tuning method for pre-trained foundation models to improve video-based pedestrian attribute recognition by leveraging temporal information and multi-modal fusion.

Contribution

It proposes a parameter-efficient spatio-temporal side-tuning strategy for CLIP, integrating attribute semantics and video features for enhanced pedestrian attribute recognition.

Findings

Significant performance improvements on large-scale datasets.

Effective utilization of temporal information in video frames.

Parameter-efficient fine-tuning of pre-trained models.

Abstract

Existing pedestrian attribute recognition (PAR) algorithms are mainly developed based on a static image, however, the performance is unreliable in challenging scenarios, such as heavy occlusion, motion blur, etc. In this work, we propose to understand human attributes using video frames that can fully use temporal information by fine-tuning a pre-trained multi-modal foundation model efficiently. Specifically, we formulate the video-based PAR as a vision-language fusion problem and adopt a pre-trained foundation model CLIP to extract the visual features. More importantly, we propose a novel spatiotemporal side-tuning strategy to achieve parameter-efficient optimization of the pre-trained vision foundation model. To better utilize the semantic information, we take the full attribute list that needs to be recognized as another input and transform the attribute words/phrases into the corresponding sentence via split, expand, and prompt operations. Then, the text encoder of CLIP is utilized for embedding processed attribute descriptions. The averaged visual tokens and text tokens are concatenated and fed into a fusion Transformer for multi-modal interactive learning. The enhanced tokens will be fed into a classification head for pedestrian attribute prediction. Extensive experiments on two large-scale video-based PAR datasets fully validated the effectiveness of our proposed framework. The source code of this paper is available at https://github.com/Event-AHU/OpenPAR.