activity
20172022
most citedA Survey of Recent Advances in CNN-based Single Image Crowd Counting and Density Estimation

640 citations · 732 across the 11 of their papers we have counts for

collaborators

23 papers

cs.CV2022

Unsupervised Restoration of Weather-affected Images using Deep Gaussian Process-based CycleGAN

Rajeev Yasarla, Vishwanath A. Sindagi, Vishal M. Patel

Existing approaches for restoring weather-degraded images follow a fully-supervised paradigm and they require paired data for training. However, collecting paired data for weather…

cs.CV202120 cited

Uncertainty-aware Mean Teacher for Source-free Unsupervised Domain Adaptive 3D Object Detection

Deepti Hegde, Vishwanath Sindagi, Velat Kilic +3

Pseudo-label based self training approaches are a popular method for source-free unsupervised domain adaptation. However, their efficacy depends on the quality of the labels genera…

cs.CV202139 cited

Lidar Light Scattering Augmentation (LISA): Physics-based Simulation of Adverse Weather Conditions for 3D Object Detection

Velat Kilic, Deepti Hegde, Vishwanath Sindagi +3

Lidar-based object detectors are critical parts of the 3D perception pipeline in autonomous navigation systems such as self-driving cars. However, they are known to be sensitive to…

cs.CV2021

Unsupervised Domain Adaptation of Object Detectors: A Survey

Poojan Oza, Vishwanath A. Sindagi, Vibashan VS +1

Recent advances in deep learning have led to the development of accurate and efficient models for various computer vision applications such as classification, segmentation, and det…

cs.CV20215 cited

MeGA-CDA: Memory Guided Attention for Category-Aware Unsupervised Domain Adaptive Object Detection

Vibashan VS, Vikram Gupta, Poojan Oza +2

Existing approaches for unsupervised domain adaptive object detection perform feature alignment via adversarial training. While these methods achieve reasonable improvements in per…

eess.IV2020

KiU-Net: Overcomplete Convolutional Architectures for Biomedical Image and Volumetric Segmentation

Jeya Maria Jose Valanarasu, Vishwanath A. Sindagi, Ilker Hacihaliloglu +1

Most methods for medical image segmentation use U-Net or its variants as they have been successful in most of the applications. After a detailed analysis of these "traditional" enc…