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In AI, the dominant approach to develop neural nets is to first pretrain a large model on a big general dataset and then fine-tune it on specific tasks.

Large language models (LLMs) are all different in some way.

Training large neural networks is famously slow and expensive.

PyTorch Implementation of “Image Classification with Hierarchical Multigraph Networks” from BMVC 2019 The number of pixels in the top row is 11, 7 and 1000 times larger (from left to right) than the number of “superpixels” in the bottom row.

As part of the “Tutorial on Graph Neural Networks for Computer Vision and Beyond” The Fourier basis (DFT matrix) on the left, in which each column or row is a basis vector, reshaped to 28×28 (on the right), i.e. 20 basis vectors are shown on the right.

As part of the “Tutorial on Graph Neural Networks for Computer Vision and Beyond” I’m presenting an overview of important Graph Neural Network works, by distilling key ideas and explaining simple intuition behind milestone methods using Python and PyTorch.

Tutorial on Graph Neural Networks for Computer Vision and Beyond I’m answering questions that AI/ML/CV people not familiar with graphs or graph neural networks typically ask.