✨InternVideo-Next: Towards General Video Foundation Models without Video-Text Supervision
📝 Summary:
InternVideo-Next proposes a two-stage Encoder-Predictor-Decoder framework for general video representation learning without text supervision. It uses a conditional diffusion decoder to bridge pixel fidelity with semantics in Stage 1, then a latent world model in Stage 2 to learn world knowledge a...
🔹 Publication Date: Published on Dec 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.01342
• PDF: https://arxiv.org/pdf/2512.01342
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#VideoFoundationModels #VideoAI #DeepLearning #UnsupervisedLearning #DiffusionModels
📝 Summary:
InternVideo-Next proposes a two-stage Encoder-Predictor-Decoder framework for general video representation learning without text supervision. It uses a conditional diffusion decoder to bridge pixel fidelity with semantics in Stage 1, then a latent world model in Stage 2 to learn world knowledge a...
🔹 Publication Date: Published on Dec 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.01342
• PDF: https://arxiv.org/pdf/2512.01342
==================================
For more data science resources:
✓ https://t.me/DataScienceT
#VideoFoundationModels #VideoAI #DeepLearning #UnsupervisedLearning #DiffusionModels
✨Learning Eigenstructures of Unstructured Data Manifolds
📝 Summary:
This deep learning framework learns spectral bases directly from unstructured data, eliminating traditional operator selection and eigendecomposition. It provides a data-driven alternative for geometry processing, recovering spectral bases and eigenvalues unsupervised without explicit operator co...
🔹 Publication Date: Published on Nov 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.01103
• PDF: https://arxiv.org/pdf/2512.01103
==================================
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✓ https://t.me/DataScienceT
#DeepLearning #DataScience #ManifoldLearning #GeometryProcessing #UnsupervisedLearning
📝 Summary:
This deep learning framework learns spectral bases directly from unstructured data, eliminating traditional operator selection and eigendecomposition. It provides a data-driven alternative for geometry processing, recovering spectral bases and eigenvalues unsupervised without explicit operator co...
🔹 Publication Date: Published on Nov 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.01103
• PDF: https://arxiv.org/pdf/2512.01103
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#DeepLearning #DataScience #ManifoldLearning #GeometryProcessing #UnsupervisedLearning
✨Boosting Unsupervised Video Instance Segmentation with Automatic Quality-Guided Self-Training
📝 Summary:
AutoQ-VIS is an unsupervised Video Instance Segmentation framework that bridges the synthetic-to-real domain gap. It uses quality-guided self-training with automatic quality assessment for progressive adaptation. This method achieves state-of-the-art results without requiring human annotations.
🔹 Publication Date: Published on Dec 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.06864
• PDF: https://arxiv.org/pdf/2512.06864
• Github: https://github.com/wcbup/AutoQ-VIS/
==================================
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#VideoInstanceSegmentation #UnsupervisedLearning #ComputerVision #MachineLearning #DeepLearning
📝 Summary:
AutoQ-VIS is an unsupervised Video Instance Segmentation framework that bridges the synthetic-to-real domain gap. It uses quality-guided self-training with automatic quality assessment for progressive adaptation. This method achieves state-of-the-art results without requiring human annotations.
🔹 Publication Date: Published on Dec 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.06864
• PDF: https://arxiv.org/pdf/2512.06864
• Github: https://github.com/wcbup/AutoQ-VIS/
==================================
For more data science resources:
✓ https://t.me/DataScienceT
#VideoInstanceSegmentation #UnsupervisedLearning #ComputerVision #MachineLearning #DeepLearning