Self-Supervised Contrastive Learning for Videos using Differentiable Local Alignment
This work addresses the need for improved video understanding in computer vision, offering a novel approach to temporal alignment that enhances representation learning for tasks like action recognition.
The paper tackles the problem of learning robust frame-wise embeddings for video analysis by introducing a self-supervised method that aligns temporal video sequences, resulting in representations that outperform state-of-the-art approaches on action recognition tasks.
Robust frame-wise embeddings are essential to perform video analysis and understanding tasks. We present a self-supervised method for representation learning based on aligning temporal video sequences. Our framework uses a transformer-based encoder to extract frame-level features and leverages them to find the optimal alignment path between video sequences. We introduce the novel Local-Alignment Contrastive (LAC) loss, which combines a differentiable local alignment loss to capture local temporal dependencies with a contrastive loss to enhance discriminative learning. Prior works on video alignment have focused on using global temporal ordering across sequence pairs, whereas our loss encourages identifying the best-scoring subsequence alignment. LAC uses the differentiable Smith-Waterman (SW) affine method, which features a flexible parameterization learned through the training phase, enabling the model to adjust the temporal gap penalty length dynamically. Evaluations show that our learned representations outperform existing state-of-the-art approaches on action recognition tasks.