2 citations · 4 across the 5 of their papers we have counts for
6 papers
FAVE: Foveated Adaptive Visual Encoding for Efficient Fine-Grained Visual Understanding
Amitangshu Mukherjee, Kaushik Roy
Fine-grained visual understanding depends on local detail, yet visual encoders face a trade-off between costly full-image high-resolution processing and compact global encoding tha…
Towards Two-Stream Foveation-based Active Vision Learning
Timur Ibrayev, Amitangshu Mukherjee, Sai Aparna Aketi +1
Deep neural network (DNN) based machine perception frameworks process the entire input in a one-shot manner to provide answers to both "what object is being observed" and "where it…
On Inherent Adversarial Robustness of Active Vision Systems
Amitangshu Mukherjee, Timur Ibrayev, Kaushik Roy
Current Deep Neural Networks are vulnerable to adversarial examples, which alter their predictions by adding carefully crafted noise. Since human eyes are robust to such inputs, it…
HCiM: ADC-Less Hybrid Analog-Digital Compute in Memory Accelerator for Deep Learning Workloads
Shubham Negi, Utkarsh Saxena, Deepika Sharma +1
Analog Compute-in-Memory (CiM) accelerators are increasingly recognized for their efficiency in accelerating Deep Neural Networks (DNN). However, their dependence on Analog-to-Digi…
Pruning for Improved ADC Efficiency in Crossbar-based Analog In-memory Accelerators
Timur Ibrayev, Isha Garg, Indranil Chakraborty +1
Deep learning has proved successful in many applications but suffers from high computational demands and requires custom accelerators for deployment. Crossbar-based analog in-memor…
Semantic-Syntactic Discrepancy in Images (SSDI): Learning Meaning and Order of Features from Natural Images
Chun Tao, Timur Ibrayev, Kaushik Roy
Despite considerable progress in image classification tasks, classification models seem unaffected by the images that significantly deviate from those that appear natural to human…