BigGait: Learning Gait Representation with Large Vision Models

Core Concepts

BigGait introduces a novel approach to gait representation using Large Vision Models, outperforming existing methods in self-domain and cross-domain tasks. The core thesis is the shift from task-specific priors to all-purpose knowledge for gait recognition.

Abstract

BigGait explores gait representation using Large Vision Models, showing superior performance in various tasks. The methodology involves a Gait Representation Extractor (GRE) that transforms all-purpose knowledge into effective gait features. Experimental results on multiple datasets demonstrate the effectiveness of BigGait in learning next-generation gait representations. The study highlights challenges in LVMs-based gait recognition and emphasizes the need for interpretability and purity in learned representations.

Stats

Experimental results on CCPG, CAISA-B* and SUSTech1K indicate significant performance improvements by BigGait.
BigGait achieves rank-1 accuracy of 83.1% across different scenarios.
Training on CCPG shows strong adaptability to unseen datasets.
Cross-domain evaluations reveal varying performances based on training data biases.

Quotes

"BigGait significantly outperforms previous methods in both self-domain and cross-domain tasks."
"Results show that BigGait is a more practical paradigm for learning general gait representation."
"The study provides inspiration for employing all-purpose knowledge produced by LVMs for other vision tasks."

Key Insights Distilled From

BigGait

by Dingqiang Ye... at arxiv.org 03-01-2024

https://arxiv.org/pdf/2402.19122.pdf

Deeper Inquiries

How can the interpretability of gait representations derived from soft constraints be improved?

Interpretability of gait representations derived from soft constraints can be enhanced by incorporating more explicit human gait priors into the learning process. This could involve developing methods to translate the learned representations into more intuitive physical meanings, making them easier to understand and interpret. Additionally, visualization techniques such as PCA projections or activation maps can help provide insights into how the model is processing and representing gait information. Exploring ways to combine these visualizations with domain-specific knowledge could further improve interpretability.

What are the implications of data distribution biases on the performance of LVMs-based gait recognition?

Data distribution biases can have significant implications on the performance of Large Vision Models (LVMs)-based gait recognition systems. In scenarios where training data lacks diversity in terms of clothing changes or other key factors that influence gait patterns, models may struggle to generalize well to unseen datasets with different distributions. This can lead to suboptimal performance in cross-domain tasks and challenges in filtering out irrelevant noise from input data. Addressing these biases through strategies like data augmentation, transfer learning, or dataset balancing techniques is crucial for improving model robustness and generalization capabilities.

How might advancements in LVMs impact other areas of computer vision research?

Advancements in Large Vision Models (LVMs) are likely to have a profound impact on various areas within computer vision research. These advancements could lead to improvements in image classification, object detection, semantic segmentation, depth estimation, and other fundamental tasks by providing more generalized features learned from large-scale datasets without task-specific supervision. LVMs may also enable breakthroughs in self-supervised learning approaches across different domains within computer vision applications. Furthermore, developments in LVMs could pave the way for novel applications such as video understanding, scene parsing, anomaly detection, and beyond by leveraging all-purpose knowledge acquired during pre-training stages.