TRIM is a self-supervised video summarization framework that uses Markov-driven losses to achieve state-of-the-art unsupervised results on SUMME and TVSUM while rivaling top supervised models.
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Similarity-constrained adversarial perturbations reduce drift signals in malware classifiers while achieving evasion, with l2 regularization performing best.
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TRIM: A Self-Supervised Video Summarization Framework Maximizing Temporal Relative Information and Representativeness
TRIM is a self-supervised video summarization framework that uses Markov-driven losses to achieve state-of-the-art unsupervised results on SUMME and TVSUM while rivaling top supervised models.
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Adversarial Evasion in Non-Stationary Malware Detection: Minimizing Drift Signals through Similarity-Constrained Perturbations
Similarity-constrained adversarial perturbations reduce drift signals in malware classifiers while achieving evasion, with l2 regularization performing best.