GEP transfers semantic knowledge from image foundation models to event data via alignment and generative pretraining on mixed sequences to create transferable event-based visual models.
Arvideo: Autoregressive pretrain- ing for self-supervised video representation learning.arXiv preprint arXiv:2405.15160, 2024
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Motion-aware contrastive learning on mask tubes improves temporal panoptic scene graph generation over pooling-based methods on video and 4D datasets.
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Generative Event Pretraining with Foundation Model Alignment
GEP transfers semantic knowledge from image foundation models to event data via alignment and generative pretraining on mixed sequences to create transferable event-based visual models.
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Motion-aware Contrastive Learning for Temporal Panoptic Scene Graph Generation
Motion-aware contrastive learning on mask tubes improves temporal panoptic scene graph generation over pooling-based methods on video and 4D datasets.