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

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.

• The paper demonstrates a spatio-temporal tuning strategy that integrates visual and textual features from video data to boost pedestrian attribute recognition.
• It employs lightweight spatial and temporal side networks to fine-tune only essential layers, reducing computational costs significantly.
• Experimental results on MARS-Attribute and DukeMTMC-VID-Attribute datasets validate the method’s robust performance in challenging scenarios like occlusion and motion blur.

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

The paper "Spatio-Temporal Side Tuning Pre-trained Foundation Models for Video-based Pedestrian Attribute Recognition" presents an innovative approach to enhancing pedestrian attribute recognition (PAR) by leveraging pre-trained multi-modal foundation models, specifically CLIP, in conjunction with a novel tuning strategy. The authors address the limitations of previous image-based PAR approaches by integrating temporal information from video frames to improve recognition accuracy in challenging conditions like occlusion and motion blur.

Methodology and Framework

The authors propose formulating the video-based PAR task as a vision-language fusion problem. This is achieved by using CLIP to extract visual features and by introducing a spatiotemporal side-tuning strategy for fine-tuning. The side-tuning strategy suggests fixing the large number of parameters in the CLIP model and using a lightweight network to optimize only a few critical layers efficiently. This approach is both parameter-efficient and computationally less demanding.

Key Components of the Framework:

1. Input Encoding: The video frames and attributes are encoded using the CLIP model. Attributes are transformed into natural language descriptions, which are then processed by the CLIP text encoder.
2. Spatio-Temporal Side Tuning: The paper introduces spatial and temporal side networks that interact with different layers of the CLIP model. This dual-side tuning allows for efficient parameter optimization by aggregating spatial and temporal features from video inputs.
3. Video-Text Fusion Transformer: Following the encoding, both visual and textual tokens are fused using a Fusion Transformer, enhancing the representation of pedestrian attributes.
4. Attribute Prediction Head: The Transformer outputs are processed through a classification head designed for final attribute recognition, demonstrating robust performance across several evaluation metrics.

Experimental Results

The paper benchmarks the proposed framework on two large-scale video-based pedestrian attribute recognition datasets, MARS-Attribute and DukeMTMC-VID-Attribute. The results show superior accuracy, precision, recall, and F1 scores compared to prior methods, including CNNs and RNNs, as well as more advanced models like VTB and the authors' prior work, VTF.

Key Findings:

• The framework achieves notable improvements in F1 scores, signifying effective attribute recognition even in complex scenarios.
• By utilizing the spatiotemporal side network, the method reduces the number of parameters needing fine-tuning, enhancing computational efficiency without sacrificing accuracy.
• The comprehensive use of video data, rather than single frames, and the integration of LLMs lead to performance boosts, particularly in dynamically changing environments.

Implications and Future Directions

This research offers a valuable contribution to pedestrian attribute recognition by demonstrating how large pre-trained models can be effectively adapted for specialized tasks like video-based PAR. The introduction of the spatiotemporal side-tuning strategy showcases a method for maintaining the generalization benefits of large foundation models while reducing computational burdens.

Theoretical Implications:

The work underscores the potential of multi-modal learning frameworks in computer vision, especially those leveraging pre-trained models to align visual and semantic information.

Practical Implications:

Incorporating temporal dynamics into attribute recognition systems could enhance applications such as surveillance, security, and automated crowd monitoring.

Future Prospectus:

Future research may focus on exploring other types of side networks, integrating newer model architectures (such as state space models), or expanding the framework's applicability to other video-based recognition tasks. Furthermore, addressing the challenges identified by the authors, including enhancing framework efficiency and refining recognition in multi-human or noisy environments, remains a pertinent area of exploration.