Self-Supervised Contrastive Learning for Videos using Differentiable Local Alignment

• URL: http://arxiv.org/abs/2409.04607v1
• Date: Fri, 6 Sep 2024 20:32:53 GMT
• Title: Self-Supervised Contrastive Learning for Videos using Differentiable Local Alignment
• Authors: Keyne Oei, Amr Gomaa, Anna Maria Feit, João Belo,
• Abstract summary: We present a self-supervised method for representation learning based on aligning temporal video sequences. We introduce the novel Local-Alignment Contrastive (LAC) loss, which combines a differentiable local alignment loss to capture local temporal dependencies. We show that our learned representations outperform existing state-of-the-art approaches on action recognition tasks.
• Score: 3.2873782624127834
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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