2 citations · 7 across the 9 of their papers we have counts for
9 papers
pNNCLR: Stochastic Pseudo Neighborhoods for Contrastive Learning based Unsupervised Representation Learning Problems
Momojit Biswas, Himanshu Buckchash, Dilip K. Prasad
Nearest neighbor (NN) sampling provides more semantic variations than pre-defined transformations for self-supervised learning (SSL) based image recognition problems. However, its…
Latent Graph Attention for Enhanced Spatial Context
Ayush Singh, Yash Bhambhu, Himanshu Buckchash +2
Global contexts in images are quite valuable in image-to-image translation problems. Conventional attention-based and graph-based models capture the global context to a large exten…
Data-Efficient Training of CNNs and Transformers with Coresets: A Stability Perspective
Animesh Gupta, Irtiza Hasan, Dilip K. Prasad +1
Coreset selection is among the most effective ways to reduce the training time of CNNs, however, only limited is known on how the resultant models will behave under variations of t…
MABNet: Master Assistant Buddy Network with Hybrid Learning for Image Retrieval
Rohit Agarwal, Gyanendra Das, Saksham Aggarwal +2
Image retrieval has garnered growing interest in recent times. The current approaches are either supervised or self-supervised. These methods do not exploit the benefits of hybrid…
Aux-Drop: Handling Haphazard Inputs in Online Learning Using Auxiliary Dropouts
Rohit Agarwal, Deepak Gupta, Alexander Horsch +1
Many real-world applications based on online learning produce streaming data that is haphazard in nature, i.e., contains missing features, features becoming obsolete in time, the a…
Patch Gradient Descent: Training Neural Networks on Very Large Images
Deepak K. Gupta, Gowreesh Mago, Arnav Chavan +1
Traditional CNN models are trained and tested on relatively low resolution images (<300 px), and cannot be directly operated on large-scale images due to compute and memory constra…
On Designing Light-Weight Object Trackers through Network Pruning: Use CNNs or Transformers?
Saksham Aggarwal, Taneesh Gupta, Pawan Kumar Sahu +4
Object trackers deployed on low-power devices need to be light-weight, however, most of the current state-of-the-art (SOTA) methods rely on using compute-heavy backbones built usin…
UltraMNIST Classification: A Benchmark to Train CNNs for Very Large Images
Deepak K. Gupta, Udbhav Bamba, Abhishek Thakur +4
Convolutional neural network (CNN) approaches available in the current literature are designed to work primarily with low-resolution images. When applied on very large images, chal…
TMAV: Temporal Motionless Analysis of Video using CNN in MPSoC
Somdip Dey, Amit K. Singh, Dilip K. Prasad +1
Analyzing video for traffic categorization is an important pillar of Intelligent Transport Systems. However, it is difficult to analyze and predict traffic based on image frames be…