activity
20202022
most citedMEDIC: A Multi-Task Learning Dataset for Disaster Image Classification

86 citations · 107 across the 6 of their papers we have counts for

collaborators

6 papers

eess.IV2022★ 10 cited

Brain Tumor Synthetic Data Generation with Adaptive StyleGANs

Usama Tariq, Rizwan Qureshi, Anas Zafar +5

Generative models have been very successful over the years and have received significant attention for synthetic data generation. As deep learning models are getting more and more…

cs.CV2021★ 86 cited

MEDIC: A Multi-Task Learning Dataset for Disaster Image Classification

Firoj Alam, Tanvirul Alam, Md. Arid Hasan +3

Recent research in disaster informatics demonstrates a practical and important use case of artificial intelligence to save human lives and suffering during natural disasters based…

cs.CL2021★ 1 cited

A Review of Bangla Natural Language Processing Tasks and the Utility of Transformer Models

Firoj Alam, Arid Hasan, Tanvirul Alam +4

Bangla -- ranked as the 6th most widely spoken language across the world (https://www.ethnologue.com/guides/ethnologue200), with 230 million native speakers -- is still considered…

cs.CV2021★ 5 cited

Robust Training of Social Media Image Classification Models for Rapid Disaster Response

Firoj Alam, Tanvirul Alam, Muhammad Imran +1

Images shared on social media help crisis managers gain situational awareness and assess incurred damages, among other response tasks. As the volume and velocity of such content ar…

cs.CV2020★ 3 cited

Deep Learning Benchmarks and Datasets for Social Media Image Classification for Disaster Response

Firoj Alam, Ferda Ofli, Muhammad Imran +2

During a disaster event, images shared on social media helps crisis managers gain situational awareness and assess incurred damages, among other response tasks. Recent advances in…

cs.CL2020★ 2 cited

Bangla Text Classification using Transformers

Tanvirul Alam, Akib Khan, Firoj Alam

Text classification has been one of the earliest problems in NLP. Over time the scope of application areas has broadened and the difficulty of dealing with new areas (e.g., noisy s…