most citedUnified Anomaly Detection methods on Edge Device using Knowledge Distillation and Quantization

2 citations · 2 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CV2025

Towards Blind and Low-Vision Accessibility of Lightweight VLMs and Custom LLM-Evals

Shruti Singh Baghel, Yash Pratap Singh Rathore, Sushovan Jena +4

Large Vision-Language Models (VLMs) excel at understanding and generating video descriptions but their high memory, computation, and deployment demands hinder practical use particu…

cs.CV20242 cited

Unified Anomaly Detection methods on Edge Device using Knowledge Distillation and Quantization

Sushovan Jena, Arya Pulkit, Kajal Singh +5

With the rapid advances in deep learning and smart manufacturing in Industry 4.0, there is an imperative for high-throughput, high-performance, and fully integrated visual inspecti…

cs.CV2024

Attend, Distill, Detect: Attention-aware Entropy Distillation for Anomaly Detection

Sushovan Jena, Vishwas Saini, Ujjwal Shaw +6

Unsupervised anomaly detection encompasses diverse applications in industrial settings where a high-throughput and precision is imperative. Early works were centered around one-cla…

cs.HC2024

Evaluating the efficacy of haptic feedback, 360° treadmill-integrated Virtual Reality framework and longitudinal training on decision-making performance in a complex search-and-shoot simulation

Akash K Rao, Arnav Bhavsar, Shubhajit Roy Chowdhury +4

Virtual Reality (VR) has made significant strides, offering users a multitude of ways to interact with virtual environments. Each sensory modality in VR provides distinct inputs an…

q-bio.NC2024

Classification of attention performance post-longitudinal tDCS via functional connectivity and machine learning methods

Akash K Rao, Vishnu K Menon, Arnav Bhavsar +3

Attention is the brain's mechanism for selectively processing specific stimuli while filtering out irrelevant information. Characterizing changes in attention following long-term i…

cs.HC2024

Prediction of multitasking performance post-longitudinal tDCS via EEG-based functional connectivity and machine learning methods

Akash K Rao, Shashank Uttrani, Vishnu K Menon +4

Predicting and understanding the changes in cognitive performance, especially after a longitudinal intervention, is a fundamental goal in neuroscience. Longitudinal brain stimulati…