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Found 6 papers, 1 papers with code
LD-ZNet: A Latent Diffusion Approach for Text-Based Image Segmentation
First, we show that the latent space of LDMs (z-space) is a better input representation compared to other feature representations like RGB images or CLIP encodings for text-based image segmentation.
Learning Graphs for Knowledge Transfer With Limited Labels
The standard paradigm is to utilize relationships in the input graph to transfer information using GCNs from training to testing nodes in the graph; for example, the semi-supervised, zero-shot, and few-shot learning setups.
All About Knowledge Graphs for Actions
Current action recognition systems require large amounts of training data for recognizing an action.
Ranked #17 on
Zero-Shot Action Recognition
on Kinetics
Depth Completion Using a View-constrained Deep Prior
Given color images and noisy and incomplete target depth maps, we optimize a randomly-initialized CNN model to reconstruct a depth map restored by virtue of using the CNN network structure as a prior combined with a view-constrained photo-consistency loss.
Stacked Spatio-Temporal Graph Convolutional Networks for Action Segmentation
We show results on CAD120 (which provides pre-computed node features and edge weights for fair performance comparison across algorithms) as well as a more complex real-world activity dataset, Charades.
Stacked U-Nets: A No-Frills Approach to Natural Image Segmentation
Many imaging tasks require global information about all pixels in an image.
