I$^2$MD: 3D Action Representation Learning with Inter- and Intra-modal Mutual Distillation

• URL: http://arxiv.org/abs/2310.15568v1
• Date: Tue, 24 Oct 2023 07:22:17 GMT
• Title: I$^2$MD: 3D Action Representation Learning with Inter- and Intra-modal Mutual Distillation
• Authors: Yunyao Mao, Jiajun Deng, Wengang Zhou, Zhenbo Lu, Wanli Ouyang, Houqiang Li
• Abstract summary: We introduce a general Inter- and Intra-modal Mutual Distillation (I$2$MD) framework. In I$2$MD, we first re-formulate the cross-modal interaction as a Cross-modal Mutual Distillation (CMD) process. To alleviate the interference of similar samples and exploit their underlying contexts, we further design the Intra-modal Mutual Distillation (IMD) strategy.
• Score: 147.2183428328396
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent progresses on self-supervised 3D human action representation learning are largely attributed to contrastive learning. However, in conventional contrastive frameworks, the rich complementarity between different skeleton modalities remains under-explored. Moreover, optimized with distinguishing self-augmented samples, models struggle with numerous similar positive instances in the case of limited action categories. In this work, we tackle the aforementioned problems by introducing a general Inter- and Intra-modal Mutual Distillation (I$^2$MD) framework. In I$^2$MD, we first re-formulate the cross-modal interaction as a Cross-modal Mutual Distillation (CMD) process. Different from existing distillation solutions that transfer the knowledge of a pre-trained and fixed teacher to the student, in CMD, the knowledge is continuously updated and bidirectionally distilled between modalities during pre-training. To alleviate the interference of similar samples and exploit their underlying contexts, we further design the Intra-modal Mutual Distillation (IMD) strategy, In IMD, the Dynamic Neighbors Aggregation (DNA) mechanism is first introduced, where an additional cluster-level discrimination branch is instantiated in each modality. It adaptively aggregates highly-correlated neighboring features, forming local cluster-level contrasting. Mutual distillation is then performed between the two branches for cross-level knowledge exchange. Extensive experiments on three datasets show that our approach sets a series of new records.

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