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Self-Supervised Learning for Videos
Overview of Self-Supervised Learning for Videos which deal with temporal redundancy and information leakage leading to more generalized models with fewer compute requirements for training video-based models significantly.
This random sequence order empirically yields significantly stronger results, suggesting that effectively capturing the inherent multidimensionality of video data is crucial for autoregressive modeling. Figure: ARVideo architectureThey extend the Generative Pretrained Transformer (GPT) framework which autoregressively predicts the next element given all preceding ones by minimizing the negative log-likelihood wrt model parameters. From the foundational work of VideoMAE to innovative approaches like VideoMAEv2, MGMAE, and ARVideo, researchers have tackled issues such as temporal redundancy, information leakage, and the need for more efficient and effective representation learning.
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