QCLR: A Plug-in Robustification Module via Quantization for Contrastive Learning in Self-Supervised Skeleton-Based Human Action Recognition

Summary

This paper proposes QCLR, a plug-in robustification module for contrastive learning in self-supervised skeleton-based human action recognition. It applies a dual-level quantization scheme across the encoding pipeline, which breaks gradient propagation and absorbs small adversarial perturbations within quantization bins. Experiments on benchmark datasets show improved robustness against gradient-based white-box and gradient-free black-box attacks while keeping classification accuracy highly competitive.

Mitigation

The proposed mitigation is the QCLR module itself, which integrates a dual-level quantization scheme and an attention-guided multi-faceted quantization strategy into contrastive learning models to improve adversarial robustness.

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