25 citations · 59 across the 12 of their papers we have counts for
12 papers
Exploring Attribute Variations in Style-based GANs using Diffusion Models
Rishubh Parihar, Prasanna Balaji, Raghav Magazine +4
Existing attribute editing methods treat semantic attributes as binary, resulting in a single edit per attribute. However, attributes such as eyeglasses, smiles, or hairstyles exhi…
NoisyTwins: Class-Consistent and Diverse Image Generation through StyleGANs
Harsh Rangwani, Lavish Bansal, Kartik Sharma +3
StyleGANs are at the forefront of controllable image generation as they produce a latent space that is semantically disentangled, making it suitable for image editing and manipulat…
Subsidiary Prototype Alignment for Universal Domain Adaptation
Jogendra Nath Kundu, Suvaansh Bhambri, Akshay Kulkarni +3
Universal Domain Adaptation (UniDA) deals with the problem of knowledge transfer between two datasets with domain-shift as well as category-shift. The goal is to categorize unlabel…
Improving GANs for Long-Tailed Data through Group Spectral Regularization
Harsh Rangwani, Naman Jaswani, Tejan Karmali +2
Deep long-tailed learning aims to train useful deep networks on practical, real-world imbalanced distributions, wherein most labels of the tail classes are associated with a few sa…
Hierarchical Semantic Regularization of Latent Spaces in StyleGANs
Tejan Karmali, Rishubh Parihar, Susmit Agrawal +4
Progress in GANs has enabled the generation of high-resolution photorealistic images of astonishing quality. StyleGANs allow for compelling attribute modification on such images vi…
Concurrent Subsidiary Supervision for Unsupervised Source-Free Domain Adaptation
Jogendra Nath Kundu, Suvaansh Bhambri, Akshay Kulkarni +3
The prime challenge in unsupervised domain adaptation (DA) is to mitigate the domain shift between the source and target domains. Prior DA works show that pretext tasks could be us…
Balancing Discriminability and Transferability for Source-Free Domain Adaptation
Jogendra Nath Kundu, Akshay Kulkarni, Suvaansh Bhambri +4
Conventional domain adaptation (DA) techniques aim to improve domain transferability by learning domain-invariant representations; while concurrently preserving the task-discrimina…
LEAD: Self-Supervised Landmark Estimation by Aligning Distributions of Feature Similarity
Tejan Karmali, Abhinav Atrishi, Sai Sree Harsha +3
In this work, we introduce LEAD, an approach to discover landmarks from an unannotated collection of category-specific images. Existing works in self-supervised landmark detection…
Non-Local Latent Relation Distillation for Self-Adaptive 3D Human Pose Estimation
Jogendra Nath Kundu, Siddharth Seth, Anirudh Jamkhandi +4
Available 3D human pose estimation approaches leverage different forms of strong (2D/3D pose) or weak (multi-view or depth) paired supervision. Barring synthetic or in-studio domai…
Aligning Silhouette Topology for Self-Adaptive 3D Human Pose Recovery
Mugalodi Rakesh, Jogendra Nath Kundu, Varun Jampani +1
Articulation-centric 2D/3D pose supervision forms the core training objective in most existing 3D human pose estimation techniques. Except for synthetic source environments, acquir…
Uncertainty-Aware Adaptation for Self-Supervised 3D Human Pose Estimation
Jogendra Nath Kundu, Siddharth Seth, Pradyumna YM +3
The advances in monocular 3D human pose estimation are dominated by supervised techniques that require large-scale 2D/3D pose annotations. Such methods often behave erratically in…
Amplitude Spectrum Transformation for Open Compound Domain Adaptive Semantic Segmentation
Jogendra Nath Kundu, Akshay Kulkarni, Suvaansh Bhambri +2
Open compound domain adaptation (OCDA) has emerged as a practical adaptation setting which considers a single labeled source domain against a compound of multi-modal unlabeled targ…