Proceedings of the 35th International BCS Human Computer Interaction Conference (HCI 2022) - Index
(2022)
Conference Proceeding
de Quincey, E., Woolley, S. I., Ortolani, M., Misirli, G., Mandal, B., Kanwal, N., …Rooney, J. (2022). Proceedings of the 35th International BCS Human Computer Interaction Conference (HCI 2022) - Index. . https://doi.org/10.14236/ewic/HCI2022.0
All Outputs (4)
Kernelized dynamic convolution routing in spatial and channel interaction for attentive concrete defect recognition (2022)
Journal Article
Mandal, B. (2022). Kernelized dynamic convolution routing in spatial and channel interaction for attentive concrete defect recognition. Signal Processing: Image Communication, 116818 - 116818. https://doi.org/10.1016/j.image.2022.116818Image/video based defect recognition is a crucial task in automating visual inspection of concrete structures. Although some progress has been made to automatically recognize defects in concrete structural images, significant challenges still exist.... Read More about Kernelized dynamic convolution routing in spatial and channel interaction for attentive concrete defect recognition.
MacularNet: Towards Fully Automated Attention-Based Deep CNN for Macular Disease Classification (2022)
Journal Article
Mandal, B. (2022). MacularNet: Towards Fully Automated Attention-Based Deep CNN for Macular Disease Classification. https://doi.org/10.1007/s42979-022-01024-0<jats:title>Abstract</jats:title><jats:p>In this work, we propose an attention-based deep convolutional neural network (CNN) model as an assistive computer-aided tool to classify common types of macular diseases: age-related macular degeneration, dia... Read More about MacularNet: Towards Fully Automated Attention-Based Deep CNN for Macular Disease Classification.
StructureNet: Deep Context Attention Learning for Structural Component Recognition (2022)
Conference Proceeding
Kaothalkar, A., Mandal, B., & Puhan, N. (2022). StructureNet: Deep Context Attention Learning for Structural Component Recognition. . https://doi.org/10.5220/0010872800003124Structural component recognition using images is a very challenging task due to the appearance of large components and their long continuation, existing jointly with very small components, the latter are often outcasted/missed by the existing methodo... Read More about StructureNet: Deep Context Attention Learning for Structural Component Recognition.