172 citations · 1.2k across the 80 of their papers we have counts for
16 papers · 1 filter
Guided Adversarial Attack for Evaluating and Enhancing Adversarial Defenses
Gaurang Sriramanan, Sravanti Addepalli, Arya Baburaj +1
Advances in the development of adversarial attacks have been fundamental to the progress of adversarial defense research. Efficient and effective attacks are crucial for reliable e…
Completely Self-Supervised Crowd Counting via Distribution Matching
Deepak Babu Sam, Abhinav Agarwalla, Jimmy Joseph +3
Dense crowd counting is a challenging task that demands millions of head annotations for training models. Though existing self-supervised approaches could learn good representation…
Class-Incremental Domain Adaptation
Jogendra Nath Kundu, Rahul Mysore Venkatesh, Naveen Venkat +2
We introduce a practical Domain Adaptation (DA) paradigm called Class-Incremental Domain Adaptation (CIDA). Existing DA methods tackle domain-shift but are unsuitable for learning…
Unsupervised Cross-Modal Alignment for Multi-Person 3D Pose Estimation
Jogendra Nath Kundu, Ambareesh Revanur, Govind Vitthal Waghmare +2
We present a deployment friendly, fast bottom-up framework for multi-person 3D human pose estimation. We adopt a novel neural representation of multi-person 3D pose which unifies t…
Appearance Consensus Driven Self-Supervised Human Mesh Recovery
Jogendra Nath Kundu, Mugalodi Rakesh, Varun Jampani +2
We present a self-supervised human mesh recovery framework to infer human pose and shape from monocular images in the absence of any paired supervision. Recent advances have shifte…
Fusion of Deep and Non-Deep Methods for Fast Super-Resolution of Satellite Images
Gaurav Kumar Nayak, Saksham Jain, R Venkatesh Babu +1
In the emerging commercial space industry there is a drastic increase in access to low cost satellite imagery. The price for satellite images depends on the sensor quality and revi…
Saliency-driven Class Impressions for Feature Visualization of Deep Neural Networks
Sravanti Addepalli, Dipesh Tamboli, R. Venkatesh Babu +1
In this paper, we propose a data-free method of extracting Impressions of each class from the classifier's memory. The Deep Learning regime empowers classifiers to extract distinct…
Learning to Count in the Crowd from Limited Labeled Data
Vishwanath A. Sindagi, Rajeev Yasarla, Deepak Sam Babu +2
Recent crowd counting approaches have achieved excellent performance. However, they are essentially based on fully supervised paradigm and require large number of annotated samples…
From Image Collections to Point Clouds with Self-supervised Shape and Pose Networks
K L Navaneet, Ansu Mathew, Shashank Kashyap +3
Reconstructing 3D models from 2D images is one of the fundamental problems in computer vision. In this work, we propose a deep learning technique for 3D object reconstruction from…
Adversarial Fooling Beyond "Flipping the Label"
Konda Reddy Mopuri, Vaisakh Shaj, R. Venkatesh Babu
Recent advancements in CNNs have shown remarkable achievements in various CV/AI applications. Though CNNs show near human or better than human performance in many critical tasks, t…
Single-step Adversarial training with Dropout Scheduling
Vivek B. S., R. Venkatesh Babu
Deep learning models have shown impressive performance across a spectrum of computer vision applications including medical diagnosis and autonomous driving. One of the major concer…
Self-Supervised 3D Human Pose Estimation via Part Guided Novel Image Synthesis
Jogendra Nath Kundu, Siddharth Seth, Varun Jampani +3
Camera captured human pose is an outcome of several sources of variation. Performance of supervised 3D pose estimation approaches comes at the cost of dispensing with variations, s…
Universal Source-Free Domain Adaptation
Jogendra Nath Kundu, Naveen Venkat, Rahul M +1
There is a strong incentive to develop versatile learning techniques that can transfer the knowledge of class-separability from a labeled source domain to an unlabeled target domai…
Towards Inheritable Models for Open-Set Domain Adaptation
Jogendra Nath Kundu, Naveen Venkat, Ambareesh Revanur +2
There has been a tremendous progress in Domain Adaptation (DA) for visual recognition tasks. Particularly, open-set DA has gained considerable attention wherein the target domain c…
Towards Achieving Adversarial Robustness by Enforcing Feature Consistency Across Bit Planes
Sravanti Addepalli, Vivek B. S., Arya Baburaj +2
As humans, we inherently perceive images based on their predominant features, and ignore noise embedded within lower bit planes. On the contrary, Deep Neural Networks are known to…
Regularizers for Single-step Adversarial Training
B. S. Vivek, R. Venkatesh Babu
The progress in the last decade has enabled machine learning models to achieve impressive performance across a wide range of tasks in Computer Vision. However, a plethora of works…