XKD: Cross-modal Knowledge Distillation with Domain Alignment for Video Representation Learning

We present XKD, a novel self-supervised framework to learn meaningful representations from unlabelled videos. XKD is trained with two pseudo objectives.

First, masked data reconstruction is performed to learn modality-specific representations from audio and visual streams. Next, self-supervised cross-modal knowledge distillation is performed between the two modalities through a teacher-student setup to learn complementary information.

We introduce a novel domain alignment strategy to tackle domain discrepancy between audio and visual modalities enabling effective cross-modal knowledge distillation. Additionally, to develop a general-purpose network capable of handling both audio and visual streams, modality-agnostic variants of XKD are introduced, which use the same pretrained backbone for different audio and visual tasks.

Our proposed cross-modal knowledge distillation improves video action classification by $8%$ to $14%$ on UCF101, HMDB51, and Kinetics400. Additionally, XKD improves multimodal action classification by $5.5%$ on Kinetics-Sound.

XKD shows state-of-the-art performance in sound classification on ESC50, achieving top-1 accuracy of $96.5%$.