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Compressed Video Action Recognition
The main features of chaoyuaw/pytorch-coviar are: Computer Vision Models, Computer Vision Research.
Projects with overlapping indexed features include: lewandofskee/mobilemamba — [CVPR25] Official implementation of `MobileMamba: Lightweight Multi-Receptive Visual Mamba Network.'. kenshohara/3d-resnets-pytorch — This project is a PyTorch implementation of 3D residual networks designed for video action recognition. It provides a… dmitryulyanov/deep-image-prior — This project is an unsupervised image restoration tool that uses a convolutional neural network as a structural prior… davidmascharka/tbd-nets — PyTorch implementation of "Transparency by Design: Closing the Gap Between Performance and Interpretability in Visual… jnhwkim/ban-vqa — ⚠️ Regrettably, I cannot perform maintenance due to the loss of the materials. I'm archiving this repository for… mkocabas/coordconv-pytorch — Pytorch implementation of CoordConv introduced in An intriguing failing of convolutional neural networks and the…
⚠️ Regrettably, I cannot perform maintenance due to the loss of the materials. I'm archiving this repository for reference
This project is an unsupervised image restoration tool that uses a convolutional neural network as a structural prior to reconstruct images from noisy or incomplete data. It functions as a neural network image prior, utilizing the inherent biases of the network architecture to restore pixels without the need for a pre-trained dataset or external learning. The system performs zero-shot image restoration by treating the network architecture itself as a regularization term. It uses a randomly initialized encoder-decoder structure and iterative gradient descent to minimize pixel-wise loss, recove
PyTorch implementation of "Transparency by Design: Closing the Gap Between Performance and Interpretability in Visual Reasoning"
This project is a PyTorch implementation of 3D residual networks designed for video action recognition. It provides a spatiotemporal architecture that analyzes both spatial frames and temporal motion to classify human activities within video clips. The system includes a distributed model training framework to accelerate learning across multiple compute nodes. It supports the deployment and fine-tuning of pre-trained model weights, allowing the adaptation of existing networks to specific new datasets. The codebase covers the full pipeline for spatiotemporal learning, including video dataset p