activity
20212025
most citedA Novel Disaster Image Dataset and Characteristics Analysis using Attention Model

15 citations · 17 across the 9 of their papers we have counts for

collaborators

6 papers

cs.LG2025

Towards Source-Free Machine Unlearning

Sk Miraj Ahmed, Umit Yigit Basaran, Dripta S. Raychaudhuri +5

As machine learning becomes more pervasive and data privacy regulations evolve, the ability to remove private or copyrighted information from trained models is becoming an increasi…

cs.LG2025

HEAL: An Empirical Study on Hallucinations in Embodied Agents Driven by Large Language Models

Trishna Chakraborty, Udita Ghosh, Xiaopan Zhang +5

Large language models (LLMs) are increasingly being adopted as the cognitive core of embodied agents. However, inherited hallucinations, which stem from failures to ground user ins…

cs.LG2024

SSMT: Few-Shot Traffic Forecasting with Single Source Meta-Transfer

Kishor Kumar Bhaumik, Minha Kim, Fahim Faisal Niloy +2

Traffic forecasting in Intelligent Transportation Systems (ITS) is vital for intelligent traffic prediction. Yet, ITS often relies on data from traffic sensors or vehicle devices,…

cs.CV20222 cited

CFL-Net: Image Forgery Localization Using Contrastive Learning

Fahim Faisal Niloy, Kishor Kumar Bhaumik, Simon S. Woo

Conventional forgery localizing methods usually rely on different forgery footprints such as JPEG artifacts, edge inconsistency, camera noise, etc., with cross-entropy loss to loca…

cs.CV202115 cited

A Novel Disaster Image Dataset and Characteristics Analysis using Attention Model

Fahim Faisal Niloy, Arif, Abu Bakar Siddik Nayem +6

The advancement of deep learning technology has enabled us to develop systems that outperform any other classification technique. However, success of any empirical system depends o…

cs.CV2021

Attention Toward Neighbors: A Context Aware Framework for High Resolution Image Segmentation

Fahim Faisal Niloy, M. Ashraful Amin, Amin Ahsan Ali +1

High-resolution image segmentation remains challenging and error-prone due to the enormous size of intermediate feature maps. Conventional methods avoid this problem by using patch…