Search Results for author:
Found 7 papers, 4 papers with code
TriSAM: Tri-Plane SAM for zero-shot cortical blood vessel segmentation in VEM images
Furthermore, we developed a zero-shot cortical blood vessel segmentation method named TriSAM, which leverages the powerful segmentation model SAM for 3D segmentation.
Reconstructing the Mind's Eye: fMRI-to-Image with Contrastive Learning and Diffusion Priors
This allows us to accurately retrieve images even from large-scale databases like LAION-5B.
CascadeXML: Rethinking Transformers for End-to-end Multi-resolution Training in Extreme Multi-label Classification
We thus propose CascadeXML, an end-to-end multi-resolution learning pipeline, which can harness the multi-layered architecture of a transformer model for attending to different label resolutions with separate feature representations.
Extreme Multi-Label Classification
Multi Label Text Classification
+2
Revisiting RCAN: Improved Training for Image Super-Resolution
Image super-resolution (SR) is a fast-moving field with novel architectures attracting the spotlight.
InceptionXML: A Lightweight Framework with Synchronized Negative Sampling for Short Text Extreme Classification
Automatic annotation of short-text data to a large number of target labels, referred to as Short Text Extreme Classification, has found numerous applications including prediction of related searches and product recommendation tasks.
Meta-DRN: Meta-Learning for 1-Shot Image Segmentation
In this paper, we propose a novel lightweight CNN architecture for 1-shot image segmentation.
MXR-U-Nets for Real Time Hyperspectral Reconstruction
In recent times, CNNs have made significant contributions to applications in image generation, super-resolution and style transfer.
