Spatiotemporal Contrastive Video Representation Learning (2008.03800v4)

Abstract: We present a self-supervised Contrastive Video Representation Learning (CVRL) method to learn spatiotemporal visual representations from unlabeled videos. Our representations are learned using a contrastive loss, where two augmented clips from the same short video are pulled together in the embedding space, while clips from different videos are pushed away. We study what makes for good data augmentations for video self-supervised learning and find that both spatial and temporal information are crucial. We carefully design data augmentations involving spatial and temporal cues. Concretely, we propose a temporally consistent spatial augmentation method to impose strong spatial augmentations on each frame of the video while maintaining the temporal consistency across frames. We also propose a sampling-based temporal augmentation method to avoid overly enforcing invariance on clips that are distant in time. On Kinetics-600, a linear classifier trained on the representations learned by CVRL achieves 70.4% top-1 accuracy with a 3D-ResNet-50 (R3D-50) backbone, outperforming ImageNet supervised pre-training by 15.7% and SimCLR unsupervised pre-training by 18.8% using the same inflated R3D-50. The performance of CVRL can be further improved to 72.9% with a larger R3D-152 (2x filters) backbone, significantly closing the gap between unsupervised and supervised video representation learning. Our code and models will be available at https://github.com/tensorflow/models/tree/master/official/.

Spatiotemporal Contrastive Video Representation Learning

The paper "Spatiotemporal Contrastive Video Representation Learning" addresses the critical task of learning video representations through the development of a novel self-supervised method named Contrastive Video Representation Learning (CVRL). The methodology is particularly aimed at leveraging spatial and temporal information from unlabeled videos to obtain robust spatiotemporal visual representations. This approach is distinct from prior works as it focuses on effectively combining spatial augmentations with temporal cues, a necessary blend to comprehend video data effectively.

Methodology

The proposed CVRL framework employs a contrastive loss, wherein two augmented clips derived from the same segment of a video are forced to align closely in the embedding space, while those from disparate videos are distanced from each other. This strategy instantiates positive and negative pairing that is crucial for contrastive learning.

In particular, the paper introduces two novel data augmentation strategies. Firstly, the authors propose a temporally consistent spatial augmentation method. This technique applies substantial spatial augmentations consistently across each frame of a video, ensuring the temporal integrity and motion dynamics of the video are maintained. Secondly, they introduce a sampling-based temporal augmentation mechanism to bypass potential pitfalls in enforcing invariance on temporally distant clips, which might differ significantly in content and motion.

Experimental Results

CVRL's efficacy is rigorously evaluated across various datasets, with a notable focus on the Kinetics-400 and Kinetics-600 video datasets. Significantly, a linear classifier trained on representations gleaned from CVRL achieved a top-1 accuracy of 70.4% on the Kinetics-600 dataset utilizing a 3D-ResNet-50 backbone architecture. This performance notably surpasses the ImageNet supervised pre-training by 15.7% and SimCLR unsupervised pre-training by 18.8%.

Further experimental validations demonstrate that CVRL continues to perform robustly when scaling to larger networks and datasets. The performance climbs to 72.9% with an R3D-152 backbone with 2x increased filter size, narrowing the gap between unsupervised and supervised video representation learning.

Implications and Future Directions

The implications of this work are multifaceted. Practically, CVRL provides a pathway to harness and utilize the vast amounts of unlabeled video data available, enhancing tasks such as video classification, action detection, and possibly transcending into augmenting other modalities of data representation with self-supervised learning. Theoretically, this work encourages further exploration into the architecture of contrastive learning frameworks, highlighting the balance between spatial and temporal attributes in video data.

For future research, the trajectory could explore scaling CVRL across various network architectures and integrating it with multimodal data to further enhance representation robustness. Additionally, there is potential in extending these methodologies to tackle complex, real-world scenarios where video data is abundant but sparsely labeled.

Overall, the CVRL framework presented in this paper marks a significant advancement in the domain of self-supervised video representation learning, with promising outcomes that substantially bridge the gap between supervised and unsupervised learning methodologies.