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VideoChat3 achieves unprecedented generalization in video understanding while maintaining high efficiency, outperforming larger models with just 4 billion parameters.
Natural paper revisions can be harnessed to train AI agents for precise and context-aware editing of complex scientific diagrams.
A single unified model can outperform specialized systems across various computer vision tasks, all without the need for custom architectures.
Achieve SOTA joint audio-video generation with JavisDiT++ using just 1M public training examples, rivaling performance of models trained on proprietary datasets.
