Search Results for author:
Found 8 papers, 4 papers with code
Learning label-label correlations in Extreme Multi-label Classification via Label Features
Extreme Multi-label Text Classification (XMC) involves learning a classifier that can assign an input with a subset of most relevant labels from millions of label choices.
Extreme Multi-Label Classification
MUlTI-LABEL-ClASSIFICATION
+4
TriSAM: Tri-Plane SAM for zero-shot cortical blood vessel segmentation in VEM images
While imaging techniques at macro and mesoscales have garnered substantial attention and resources, microscale Volume Electron Microscopy (vEM) imaging, capable of revealing intricate vascular details, has lacked the necessary benchmarking infrastructure.
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-ClASSIFICATION
+3
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.
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.
