Knowledge Distillation for Multimodal Egocentric Action Recognition Robust to Missing Modalities

• URL: http://arxiv.org/abs/2504.08578v1
• Date: Fri, 11 Apr 2025 14:30:42 GMT
• Title: Knowledge Distillation for Multimodal Egocentric Action Recognition Robust to Missing Modalities
• Authors: Maria Santos-Villafranca, Dustin Carrión-Ojeda, Alejandro Perez-Yus, Jesus Bermudez-Cameo, Jose J. Guerrero, Simone Schaub-Meyer,
• Abstract summary: We introduce an efficient multimodal knowledge distillation approach for egocentric action recognition.<n>Our method focuses on resource-efficient development by leveraging pre-trained models as unimodal feature extractors in our teacher model.
• Score: 43.15852057358654
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Action recognition is an essential task in egocentric vision due to its wide range of applications across many fields. While deep learning methods have been proposed to address this task, most rely on a single modality, typically video. However, including additional modalities may improve the robustness of the approaches to common issues in egocentric videos, such as blurriness and occlusions. Recent efforts in multimodal egocentric action recognition often assume the availability of all modalities, leading to failures or performance drops when any modality is missing. To address this, we introduce an efficient multimodal knowledge distillation approach for egocentric action recognition that is robust to missing modalities (KARMMA) while still benefiting when multiple modalities are available. Our method focuses on resource-efficient development by leveraging pre-trained models as unimodal feature extractors in our teacher model, which distills knowledge into a much smaller and faster student model. Experiments on the Epic-Kitchens and Something-Something datasets demonstrate that our student model effectively handles missing modalities while reducing its accuracy drop in this scenario.

Related papers

• Test-Time Adaptation for Combating Missing Modalities in Egocentric Videos [92.38662956154256]
  Real-world applications often face challenges with incomplete modalities due to privacy concerns, efficiency needs, or hardware issues.<n>We propose a novel approach to address this issue at test time without requiring retraining.<n>MiDl represents the first self-supervised, online solution for handling missing modalities exclusively at test time.
  arXiv  Detail & Related papers  (2024-04-23T16:01:33Z)
• Dynamic Self-adaptive Multiscale Distillation from Pre-trained Multimodal Large Model for Efficient Cross-modal Representation Learning [12.00246872965739]
  We propose a novel dynamic self-adaptive multiscale distillation from pre-trained multimodal large model. Our strategy employs a multiscale perspective, enabling the extraction structural knowledge across from the pre-trained multimodal large model. Our methodology streamlines pre-trained multimodal large models using only their output features and original image-level information.
  arXiv  Detail & Related papers  (2024-04-16T18:22:49Z)
• Exploring Missing Modality in Multimodal Egocentric Datasets [89.76463983679058]
  We introduce a novel concept -Missing Modality Token (MMT)-to maintain performance even when modalities are absent. Our method mitigates the performance loss, reducing it from its original $sim 30%$ drop to only $sim 10%$ when half of the test set is modal-incomplete.
  arXiv  Detail & Related papers  (2024-01-21T11:55:42Z)
• Unified Multi-modal Unsupervised Representation Learning for Skeleton-based Action Understanding [62.70450216120704]
  Unsupervised pre-training has shown great success in skeleton-based action understanding. We propose a Unified Multimodal Unsupervised Representation Learning framework, called UmURL. UmURL exploits an efficient early-fusion strategy to jointly encode the multi-modal features in a single-stream manner.
  arXiv  Detail & Related papers  (2023-11-06T13:56:57Z)
• Elevating Skeleton-Based Action Recognition with Efficient Multi-Modality Self-Supervision [40.16465314639641]
  Self-supervised representation learning for human action recognition has developed rapidly in recent years. Most of the existing works are based on skeleton data while using a multi-modality setup. We first propose an Implicit Knowledge Exchange Module which alleviates the propagation of erroneous knowledge between low-performance modalities.
  arXiv  Detail & Related papers  (2023-09-21T12:27:43Z)
• Multimodal Distillation for Egocentric Action Recognition [41.821485757189656]
  egocentric video understanding involves modelling hand-object interactions. Standard models, e.g. CNNs or Vision Transformers, which receive RGB frames as input perform well. But their performance improves further by employing additional input modalities that provide complementary cues. The goal of this work is to retain the performance of such a multimodal approach, while using only the RGB frames as input at inference time.
  arXiv  Detail & Related papers  (2023-07-14T17:07:32Z)
• Learning Unseen Modality Interaction [54.23533023883659]
  Multimodal learning assumes all modality combinations of interest are available during training to learn cross-modal correspondences. We pose the problem of unseen modality interaction and introduce a first solution. It exploits a module that projects the multidimensional features of different modalities into a common space with rich information preserved.
  arXiv  Detail & Related papers  (2023-06-22T10:53:10Z)
• Contrastive Learning with Cross-Modal Knowledge Mining for Multimodal Human Activity Recognition [1.869225486385596]
  We explore the hypothesis that leveraging multiple modalities can lead to better recognition. We extend a number of recent contrastive self-supervised approaches for the task of Human Activity Recognition. We propose a flexible, general-purpose framework for performing multimodal self-supervised learning.
  arXiv  Detail & Related papers  (2022-05-20T10:39:16Z)
• On Modality Bias Recognition and Reduction [70.69194431713825]
  We study the modality bias problem in the context of multi-modal classification. We propose a plug-and-play loss function method, whereby the feature space for each label is adaptively learned. Our method yields remarkable performance improvements compared with the baselines.
  arXiv  Detail & Related papers  (2022-02-25T13:47:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.