Search Results for author:
Found 9 papers, 4 papers with code
IPCL: Iterative Pseudo-Supervised Contrastive Learning to Improve Self-Supervised Feature Representation
In this paper, we propose a novel self-supervised learning approach named Iterative Pseudo-supervised Contrastive Learning (IPCL), which utilizes a balanced combination of image augmentations and pseudo-class information to improve the visual representation iteratively.
Segmentation of tibiofemoral joint tissues from knee MRI using MtRA-Unet and incorporating shape information: Data from the Osteoarthritis Initiative
Knee Osteoarthritis (KOA) is the third most prevalent Musculoskeletal Disorder (MSD) after neck and back pain.
DatUS^2: Data-driven Unsupervised Semantic Segmentation with Pre-trained Self-supervised Vision Transformer
Also, the best version of DatUS^2 outperforms the existing state-of-the-art method for the unsupervised dense semantic segmentation task with 15. 02% MiOU and 21. 47% Pixel accuracy on the SUIM dataset.
Ranked #1 on
Unsupervised Semantic Segmentation
on SUIM
DiRaC-I: Identifying Diverse and Rare Training Classes for Zero-Shot Learning
Inspired by strategies like Active Learning, it is intuitive that intelligently selecting the training classes from a dataset for Zero-Shot Learning (ZSL) can improve the performance of existing ZSL methods.
Resolving Semantic Confusions for Improved Zero-Shot Detection
Zero-shot detection (ZSD) is a challenging task where we aim to recognize and localize objects simultaneously, even when our model has not been trained with visual samples of a few target ("unseen") classes.
Ranked #2 on
Generalized Zero-Shot Object Detection
on MS-COCO
DRMNet: Difference Image Reconstruction Enhanced Multiresolution Network for Optical Change Detection
In another parallel path, the output of the BN is downsampled and passed to the proposed deconvolution with a subpixel convolution module to generate image difference.
Change Detection
Change detection for remote sensing images
+1
Knee Osteoarthritis Severity Prediction using an Attentive Multi-Scale Deep Convolutional Neural Network
Knee Osteoarthritis (OA) is a destructive joint disease identified by joint stiffness, pain, and functional disability concerning millions of lives across the globe.
Multi-Contextual Design of Convolutional Neural Network for Steganalysis
Most deep steganalysis classifiers usually extract noise residuals using high-pass filters as preprocessing steps and feed them to their deep model for classification.
Wavelength-based Attributed Deep Neural Network for Underwater Image Restoration
Background: Underwater images, in general, suffer from low contrast and high color distortions due to the non-uniform attenuation of the light as it propagates through the water.
