CrossVideoMAE: Self-Supervised Image-Video Representation Learning with Masked Autoencoders

Current video-based Masked Autoencoders (MAEs) primarily focus on learning effective spatiotemporal representations from a visual perspective, which may lead the model to prioritize general spatial-temporal patterns but often overlook nuanced semantic attributes like specific interactions or sequences that define actions - such as action-specific features that align more closely with human cognition for space-time correspondence. This can limit the model's ability to capture the essence of certain actions that are contextually rich and continuous. Humans are capable of mapping visual concepts, object view invariance, and semantic attributes available in static instances to comprehend natural dynamic scenes or videos. Existing MAEs for videos and static images rely on separate datasets for videos and images, which may lack the rich semantic attributes necessary for fully understanding the learned concepts, especially when compared to using video and corresponding sampled frame images together. To this end, we propose CrossVideoMAE an end-to-end self-supervised cross-modal contrastive learning MAE that effectively learns both video-level and frame-level rich spatiotemporal representations and semantic attributes. Our method integrates mutual spatiotemporal information from videos with spatial information from sampled frames within a feature-invariant space, while encouraging invariance to augmentations within the video domain. This objective is achieved through jointly embedding features of visible tokens and combining feature correspondence within and across modalities, which is critical for acquiring rich, label-free guiding signals from both video and frame image modalities in a self-supervised manner. Extensive experiments demonstrate that our approach surpasses previous state-of-the-art methods and ablation studies validate the effectiveness of our approach.

How Effective are Self-Supervised Models for Contact Identification in Videos

The exploration of video content via Self-Supervised Learning (SSL) models has unveiled a dynamic field of study, emphasizing both the complex challenges and unique opportunities inherent in this area. Despite the growing body of research, the ability of SSL models to detect physical contacts in videos remains largely unexplored, particularly the effectiveness of methods such as downstream supervision with linear probing or full fine-tuning. This work aims to bridge this gap by employing eight different convolutional neural networks (CNNs) based video SSL models to identify instances of physical contact within video sequences specifically. The Something-Something v2 (SSv2) and Epic-Kitchen (EK-100) datasets were chosen for evaluating these approaches due to the promising results on UCF101 and HMDB51, coupled with their limited prior assessment on SSv2 and EK-100. Additionally, these datasets feature diverse environments and scenarios, essential for testing the robustness and accuracy of video-based models. This approach not only examines the effectiveness of each model in recognizing physical contacts but also explores the performance in the action recognition downstream task. By doing so, valuable insights into the adaptability of SSL models in interpreting complex, dynamic visual information are contributed.