etornam45/vl-jepa
This VL-JEPA implimentation takes direct insperation from the original VL-JEPA paper
This project helps machine learning researchers working with video and text data. It combines existing models like DINOv3 for video and Gemma for text to create a system that can learn relationships between them. The input is video and associated text, and the output is a trained predictor capable of understanding these multimodal relationships, without needing to retrain the core video and text models themselves.
Use this if you are a machine learning researcher focused on multimodal learning, specifically aiming to build models that predict relationships between video and text without extensive new model training.
Not ideal if you are looking for an off-the-shelf solution for video analysis or natural language processing without deep involvement in model architecture and training.
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Language
Python
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Last pushed
Jan 18, 2026
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