40 citations · 62 across the 17 of their papers we have counts for
17 papers · 1 filter
Reducing Annotation Effort by Identifying and Labeling Contextually Diverse Classes for Semantic Segmentation Under Domain Shift
Sharat Agarwal, Saket Anand, Chetan Arora
In Active Domain Adaptation (ADA), one uses Active Learning (AL) to select a subset of images from the target domain, which are then annotated and used for supervised domain adapta…
Learning Hierarchy Aware Features for Reducing Mistake Severity
Ashima Garg, Depanshu Sani, Saket Anand
Label hierarchies are often available apriori as part of biological taxonomy or language datasets WordNet. Several works exploit these to learn hierarchy aware features in order to…
HIERMATCH: Leveraging Label Hierarchies for Improving Semi-Supervised Learning
Ashima Garg, Shaurya Bagga, Yashvardhan Singh +1
Semi-supervised learning approaches have emerged as an active area of research to combat the challenge of obtaining large amounts of annotated data. Towards the goal of improving t…
Does Data Repair Lead to Fair Models? Curating Contextually Fair Data To Reduce Model Bias
Sharat Agarwal, Sumanyu Muku, Saket Anand +1
Contextual information is a valuable cue for Deep Neural Networks (DNNs) to learn better representations and improve accuracy. However, co-occurrence bias in the training dataset m…
Contextual Diversity for Active Learning
Sharat Agarwal, Himanshu Arora, Saket Anand +1
Requirement of large annotated datasets restrict the use of deep convolutional neural networks (CNNs) for many practical applications. The problem can be mitigated by using active…
DGSAC: Density Guided Sampling and Consensus
Lokender Tiwari, Saket Anand
Robust multiple model fitting plays a crucial role in many computer vision applications. Unlike single model fitting problems, the multi-model fitting has additional challenges. Th…
REGroup: Rank-aggregating Ensemble of Generative Classifiers for Robust Predictions
Lokender Tiwari, Anish Madan, Saket Anand +1
Deep Neural Networks (DNNs) are often criticized for being susceptible to adversarial attacks. Most successful defense strategies adopt adversarial training or random input transfo…
GraCIAS: Grassmannian of Corrupted Images for Adversarial Security
Ankita Shukla, Pavan Turaga, Saket Anand
Input transformation based defense strategies fall short in defending against strong adversarial attacks. Some successful defenses adopt approaches that either increase the randomn…
Automatic Detection and Recognition of Individuals in Patterned Species
Gullal Singh Cheema, Saket Anand
Visual animal biometrics is rapidly gaining popularity as it enables a non-invasive and cost-effective approach for wildlife monitoring applications. Widespread usage of camera tra…
Intelligent Querying for Target Tracking in Camera Networks using Deep Q-Learning with n-Step Bootstrapping
Anil Sharma, Saket Anand, Sanjit K. Kaul
Surveillance camera networks are a useful infrastructure for various visual analytics applications, where high-level inferences and predictions could be made based on target tracki…
Pseudo RGB-D for Self-Improving Monocular SLAM and Depth Prediction
Lokender Tiwari, Pan Ji, Quoc-Huy Tran +3
Classical monocular Simultaneous Localization And Mapping (SLAM) and the recently emerging convolutional neural networks (CNNs) for monocular depth prediction represent two largely…
Product of Orthogonal Spheres Parameterization for Disentangled Representation Learning
Ankita Shukla, Sarthak Bhagat, Shagun Uppal +2
Learning representations that can disentangle explanatory attributes underlying the data improves interpretabilty as well as provides control on data generation. Various learning f…
Primate Face Identification in the Wild
Ankita Shukla, Gullal Singh Cheema, Saket Anand +2
Ecological imbalance owing to rapid urbanization and deforestation has adversely affected the population of several wild animals. This loss of habitat has skewed the population of…
Geometry of Deep Generative Models for Disentangled Representations
Ankita Shukla, Shagun Uppal, Sarthak Bhagat +2
Deep generative models like variational autoencoders approximate the intrinsic geometry of high dimensional data manifolds by learning low-dimensional latent-space variables and an…
Unique Identification of Macaques for Population Monitoring and Control
Ankita Shukla, Gullal Singh Cheema, Saket Anand +2
Despite loss of natural habitat due to development and urbanization, certain species like the Rhesus macaque have adapted well to the urban environment. With abundant food and no p…
A Reinforcement Learning Approach to Target Tracking in a Camera Network
Anil Sharma, Prabhat Kumar, Saket Anand +1
Target tracking in a camera network is an important task for surveillance and scene understanding. The task is challenging due to disjoint views and illumination variation in diffe…
Disentangling Factors of Variation with Cycle-Consistent Variational Auto-Encoders
Ananya Harsh Jha, Saket Anand, Maneesh Singh +1
Generative models that learn disentangled representations for different factors of variation in an image can be very useful for targeted data augmentation. By sampling from the dis…