The focal point of egocentric video understanding is modelling hand-object interactions. Standard models, e.g. CNNs or Vision Transformers, which receive RGB frames as input perform well. However, their performance improves further by employing additional input modalities that provide complementary cues, such as object detections, optical flow, audio, etc. The added complexity of the modality-specific modules, on the other hand, makes these models impractical for deployment. 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. We demonstrate that for egocentric action recognition on the Epic-Kitchens and the Something-Something datasets, students which are taught by multimodal teachers tend to be more accurate and better calibrated than architecturally equivalent models trained on ground truth labels in a unimodal or multimodal fashion. We further adopt a principled multimodal knowledge distillation framework, allowing us to deal with issues which occur when applying multimodal knowledge distillation in a naive manner. Lastly, we demonstrate the achieved reduction in computational complexity, and show that our approach maintains higher performance with the reduction of the number of input views.

翻译：自我中心视频理解的核心在于建模手部-物体交互。标准模型（如CNN或视觉Transformer）以RGB帧为输入时表现良好。然而，通过引入提供补充线索的额外输入模态（例如物体检测、光流、音频等），其性能可进一步提升。但另一方面，模态专用模块增加的复杂性使得这些模型难以实际部署。本工作的目标是在推理阶段仅使用RGB帧作为输入的情况下，保留此类多模态方法的性能。我们证明，在Epic-Kitchens和Something-Something数据集的自我中心动作识别任务中，由多模态教师模型教导的学生模型，比使用真实标签以单模态或多模态方式训练的架构等价模型具有更高的准确性和更好的校准性。我们进一步采用有原则的多模态知识蒸馏框架，从而能够处理以朴素方式应用多模态知识蒸馏时出现的问题。最后，我们展示了计算复杂度的降低效果，并证明我们的方法在减少输入视角数量时仍能保持更高性能。