1 citations · 2 across the 4 of their papers we have counts for
4 papers
TinyVQA: Compact Multimodal Deep Neural Network for Visual Question Answering on Resource-Constrained Devices
Hasib-Al Rashid, Argho Sarkar, Aryya Gangopadhyay +2
Traditional machine learning models often require powerful hardware, making them unsuitable for deployment on resource-limited devices. Tiny Machine Learning (tinyML) has emerged a…
Polar-VQA: Visual Question Answering on Remote Sensed Ice sheet Imagery from Polar Region
Argho Sarkar, Maryam Rahnemoonfar
For glaciologists, studying ice sheets from the polar regions is critical. With the advancement of deep learning techniques, we can now extract high-level information from the ice…
VQA-Aid: Visual Question Answering for Post-Disaster Damage Assessment and Analysis
Argho Sarkar, Maryam Rahnemoonfar
Visual Question Answering system integrated with Unmanned Aerial Vehicle (UAV) has a lot of potentials to advance the post-disaster damage assessment purpose. Providing assistance…
FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding
Maryam Rahnemoonfar, Tashnim Chowdhury, Argho Sarkar +3
Visual scene understanding is the core task in making any crucial decision in any computer vision system. Although popular computer vision datasets like Cityscapes, MS-COCO, PASCAL…