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Neural Pooling for Graph Neural Networks
Our proposed methods have the ability to handle variable number of nodes in different graphs, and are also invariant to the isomorphic structures of graphs.
LEAD: Self-Supervised Landmark Estimation by Aligning Distributions of Feature Similarity
Existing works in self-supervised landmark detection are based on learning dense (pixel-level) feature representations from an image, which are further used to learn landmarks in a semi-supervised manner.
GenVideo: One-shot Target-image and Shape Aware Video Editing using T2I Diffusion Models
Our approach handles edits with target objects of varying shapes and sizes while maintaining the temporal consistency of the edit using our novel target and shape aware InvEdit masks.
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