11 citations · 12 across the 2 of their papers we have counts for
5 papers
CascadeXML: Rethinking Transformers for End-to-end Multi-resolution Training in Extreme Multi-label Classification
Siddhant Kharbanda, Atmadeep Banerjee, Erik Schultheis +1
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. Recent a…
Revisiting RCAN: Improved Training for Image Super-Resolution
Zudi Lin, Prateek Garg, Atmadeep Banerjee +6
Image super-resolution (SR) is a fast-moving field with novel architectures attracting the spotlight. However, most SR models were optimized with dated training strategies. In this…
Meta-DRN: Meta-Learning for 1-Shot Image Segmentation
Atmadeep Banerjee
Modern deep learning models have revolutionized the field of computer vision. But, a significant drawback of most of these models is that they require a large number of labelled ex…
NTIRE 2020 Challenge on Spectral Reconstruction from an RGB Image
Boaz Arad, Radu Timofte, Ohad Ben-Shahar +4
This paper reviews the second challenge on spectral reconstruction from RGB images, i.e., the recovery of whole-scene hyperspectral (HS) information from a 3-channel RGB image. As…
MXR-U-Nets for Real Time Hyperspectral Reconstruction
Atmadeep Banerjee, Akash Palrecha
In recent times, CNNs have made significant contributions to applications in image generation, super-resolution and style transfer. In this paper, we build upon the work of Howard…