DEVIAS: Learning Disentangled Video Representations of Action and Scene for Holistic Video Understanding

When watching a video, humans can naturally extract human actions from the surrounding scene context, even when action-scene combinations are unusual. However, unlike humans, video action recognition models often learn scene-biased action representations from the spurious correlation in training data, leading to poor performance in out-of-context scenarios. While scene-debiased models achieve improved performance in out-of-context scenarios, they often overlook valuable scene information in the data. Addressing this challenge, we propose Disentangled VIdeo representations of Action and Scene (DEVIAS), which aims to achieve holistic video understanding. Disentangled action and scene representations with our method could provide flexibility to adjust the emphasis on action or scene information depending on downstream task and dataset characteristics. Disentangled action and scene representations could be beneficial for both in-context and out-of-context video understanding. To this end, we employ slot attention to learn disentangled action and scene representations with a single model, along with auxiliary tasks that further guide slot attention. We validate the proposed method on both in-context datasets: UCF-101 and Kinetics-400, and out-of-context datasets: SCUBA and HAT. Our proposed method shows favorable performance across different datasets compared to the baselines, demonstrating its effectiveness in diverse video understanding scenarios.

GLAD: Global-Local View Alignment and Background Debiasing for Unsupervised Video Domain Adaptation with Large Domain Gap

In this work, we tackle the challenging problem of unsupervised video domain adaptation (UVDA) for action recognition. We specifically focus on scenarios with a substantial domain gap, in contrast to existing works primarily deal with small domain gaps between labeled source domains and unlabeled target domains. To establish a more realistic setting, we introduce a novel UVDA scenario, denoted as Kinetics->BABEL, with a more considerable domain gap in terms of both temporal dynamics and background shifts. To tackle the temporal shift, i.e., action duration difference between the source and target domains, we propose a global-local view alignment approach. To mitigate the background shift, we propose to learn temporal order sensitive representations by temporal order learning and background invariant representations by background augmentation. We empirically validate that the proposed method shows significant improvement over the existing methods on the Kinetics->BABEL dataset with a large domain gap. The code is available at https://github.com/KHUVLL/GLAD.