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EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition 2021: Team M3EM Technical Report

arXiv 2106.10026 ↗First posted 18 Jun 2021cs.CVcited by 4

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Abstract (arXiv)

In this report, we describe the technical details of our submission to the 2021 EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition. Leveraging multiple modalities has been proved to benefit the Unsupervised Domain Adaptation (UDA) task. In this work, we present Multi-Modal Mutual Enhancement Module (M3EM), a deep module for jointly considering information from multiple modalities to find the most transferable representations across domains. We achieve this by implementing two sub-modules for enhancing each modality using the context of other modalities. The first sub-module exchanges information across modalities through the semantic space, while the second sub-module finds the most transferable spatial region based on the consensus of all modalities.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 9 of its 11 references that are in the map. First posted on arXiv 18 Jun 2021.

Builds on

9 of 11 references.

References · 9

  1. 2016Feature Pyramid Networks for Object Detectioncited by 5,021
  2. 2017Squeeze-and-Excitation Networkscited by 4,763
  3. 2017Quo Vadis, Action Recognition? A New Model and the Kinetics Datasetcited by 3,259
  4. 2017Residual Attention Network for Image Classificationcited by 385
  5. 2020Understanding Human Hands in Contact at Internet Scalecited by 281
  6. 2019EPIC-Fusion: Audio-Visual Temporal Binding for Egocentric Action Recognitioncited by 226
  7. 2020Multi-Modal Domain Adaptation for Fine-Grained Action Recognitioncited by 115
  8. 2019Temporal Attentive Alignment for Large-Scale Video Domain Adaptationcited by 100
  9. 2020Modality Compensation Network: Cross-Modal Adaptation for Action Recognitioncited by 16

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Citing papers per year: 2022: 2; 2023: 0; 2024: 1; 2025: 1.

4 papers.