Supervised Contrastive Frame Aggregation for Video Representation Learning

TL;DR

This paper introduces a supervised contrastive learning framework for video representation that uses a novel frame aggregation strategy to leverage global context, improve accuracy, and reduce computational costs.

Contribution

It presents a new video-to-image aggregation method combined with contrastive learning, enabling effective video representations with pre-trained CNNs and less computational overhead.

Findings

Outperforms existing methods in classification accuracy

Requires fewer computational resources

Achieves 76% accuracy on Penn Action and 48% on HMDB51

Abstract

We propose a supervised contrastive learning framework for video representation learning that leverages temporally global context. We introduce a video to image aggregation strategy that spatially arranges multiple frames from each video into a single input image. This design enables the use of pre trained convolutional neural network backbones such as ResNet50 and avoids the computational overhead of complex video transformer models. We then design a contrastive learning objective that directly compares pairwise projections generated by the model. Positive pairs are defined as projections from videos sharing the same label while all other projections are treated as negatives. Multiple natural views of the same video are created using different temporal frame samplings from the same underlying video. Rather than relying on data augmentation these frame level variations produce diverse positive samples with global context and reduce overfitting. Experiments on the Penn Action and HMDB51 datasets demonstrate that the proposed method outperforms existing approaches in classification accuracy while requiring fewer computational resources. The proposed Supervised Contrastive Frame Aggregation method learns effective video representations in both supervised and self supervised settings and supports video based tasks such as classification and captioning. The method achieves seventy six percent classification accuracy on Penn Action compared to forty three percent achieved by ViVIT and forty eight percent accuracy on HMDB51 compared to thirty seven percent achieved by ViVIT.