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Don't Judge by the Look: Towards Motion Coherent Video Representation

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arxiv 2403.09506 v2 pith:PZYQBCIJ submitted 2024-03-14 cs.CV cs.AIcs.LG

Yitian Zhang , Yue Bai , Huan Wang , Yizhou Wang , Yun Fu This is my paper
classification cs.CVcs.AIcs.LG
keywords appearanceaugmentationmotionvideoappearancescoherentdatamodel
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Current training pipelines in object recognition neglect Hue Jittering when doing data augmentation as it not only brings appearance changes that are detrimental to classification, but also the implementation is inefficient in practice. In this study, we investigate the effect of hue variance in the context of video understanding and find this variance to be beneficial since static appearances are less important in videos that contain motion information. Based on this observation, we propose a data augmentation method for video understanding, named Motion Coherent Augmentation (MCA), that introduces appearance variation in videos and implicitly encourages the model to prioritize motion patterns, rather than static appearances. Concretely, we propose an operation SwapMix to efficiently modify the appearance of video samples, and introduce Variation Alignment (VA) to resolve the distribution shift caused by SwapMix, enforcing the model to learn appearance invariant representations. Comprehensive empirical evaluation across various architectures and different datasets solidly validates the effectiveness and generalization ability of MCA, and the application of VA in other augmentation methods. Code is available at https://github.com/BeSpontaneous/MCA-pytorch.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. MUG: Pseudo Labeling Augmented Audio-Visual Mamba Network for Audio-Visual Video Parsing

    cs.CV 2025-07 conditional novelty 6.0 of 10

    MUG combines manually corrected pseudo-labels, cross-modal random track recombination, and a Mamba-Transformer network to reach new state-of-the-art F1 scores on the LLP audio-visual video parsing benchmark.

  2. Group Relative Augmentation for Data Efficient Action Detection

    cs.CV 2025-07 conditional novelty 5.0 of 10

    A LoRA plus FiLM feature-augmentation method with a group-weighted loss reports modest few-shot action detection gains on AVA and MOMA, but the evidence for the weighting component is weak.

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