4 citations · 16 across the 9 of their papers we have counts for
12 papers
Pred&Guide: Labeled Target Class Prediction for Guiding Semi-Supervised Domain Adaptation
Megh Manoj Bhalerao, Anurag Singh, Soma Biswas
Semi-supervised domain adaptation aims to classify data belonging to a target domain by utilizing a related label-rich source domain and very few labeled examples of the target dom…
Spacing Loss for Discovering Novel Categories
K J Joseph, Sujoy Paul, Gaurav Aggarwal +4
Novel Class Discovery (NCD) is a learning paradigm, where a machine learning model is tasked to semantically group instances from unlabeled data, by utilizing labeled instances fro…
Universal Cross-Domain Retrieval: Generalizing Across Classes and Domains
Soumava Paul, Titir Dutta, Soma Biswas
In this work, for the first time, we address the problem of universal cross-domain retrieval, where the test data can belong to classes or domains which are unseen during training.…
SML: Semantic Meta-learning for Few-shot Semantic Segmentation
Ayyappa Kumar Pambala, Titir Dutta, Soma Biswas
The significant amount of training data required for training Convolutional Neural Networks has become a bottleneck for applications like semantic segmentation. Few-shot semantic s…
A Novel Incremental Cross-Modal Hashing Approach
Devraj Mandal, Soma Biswas
Cross-modal retrieval deals with retrieving relevant items from one modality, when provided with a search query from another modality. Hashing techniques, where the data is represe…
A Novel Self-Supervised Re-labeling Approach for Training with Noisy Labels
Devraj Mandal, Shrisha Bharadwaj, Soma Biswas
The major driving force behind the immense success of deep learning models is the availability of large datasets along with their clean labels. Unfortunately, this is very difficul…