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Dr Bappaditya Mandal's Outputs (67)

Interpretative Attention Networks for Structural Component Recognition (2024)
Presentation / Conference Contribution
Uniyal, A., Mandal, B., Puhan, N. B., & Bera, P. Interpretative Attention Networks for Structural Component Recognition. Presented at 27th International Conference on Pattern Recognition, Kolkata, India

Bridges are essential for enabling movement during environmental disasters and serve as crucial links for rescue and aid delivery. Effective bridge inspection and maintenance are more critical than ever due to increasing severity and frequency of env... Read More about Interpretative Attention Networks for Structural Component Recognition.

Unified Deep Ensemble Architecture for Multiple Classification Tasks (2024)
Presentation / Conference Contribution
Mistry, K. A. J., & Mandal, B. (2024, August). Unified Deep Ensemble Architecture for Multiple Classification Tasks. Presented at 2024 Intelligent Systems Conference (IntelliSys), Amsterdam, The Netherlands

Banks face regular challenges in making decisions for ever increasing need for bank loans. Most banks use applicant’s financial situations, their past history, affordability checks, credit score and risk assessment, which are time consuming, challeng... Read More about Unified Deep Ensemble Architecture for Multiple Classification Tasks.

Grid LSTM based Attention Modelling for Traffic Flow Prediction (2024)
Presentation / Conference Contribution
Biju, R., Goparaju, S. U., Gangadharan, D., & Mandal, B. (2024, June). Grid LSTM based Attention Modelling for Traffic Flow Prediction. Presented at 2024 IEEE 99th Vehicular Technology Conference (VTC2024-Spring), Singapore

Traffic flow prediction is an important task that can directly impact the control of traffic flow positively and improve the overall traffic throughput. Although a large number of studies have been performed to improve traffic flow prediction, there... Read More about Grid LSTM based Attention Modelling for Traffic Flow Prediction.

Visual Attention Assisted Games (2023)
Presentation / Conference Contribution
Mandal, B., Puhan, N. B., & Homi Anil, V. (2023, August). Visual Attention Assisted Games. Presented at IEEE Symposium on Computational Intelligence and Games, CIG, Boston, MA, USA

In this work, we propose a committee of attention models developed for improving the deep reinforcement learning frequently used for games. The game environment is manifested with spatial and temporal attention mechanisms so as to focus on important... Read More about Visual Attention Assisted Games.

Optimization and Performance Evaluation of Hybrid Deep Learning Models for Traffic Flow Prediction (2023)
Presentation / Conference Contribution
Goparaju, S. U., Biju, R., M, P., MC, B., Gangadharan, D., Mandal, B., & C, P. (2023, June). Optimization and Performance Evaluation of Hybrid Deep Learning Models for Traffic Flow Prediction. Presented at 2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring), Florence, Italy

Traffic flow prediction has been regarded as a critical problem in intelligent transportation systems. An accurate prediction can help mitigate congestion and other societal problems while facilitating safer, cost and time-efficient travel. However,... Read More about Optimization and Performance Evaluation of Hybrid Deep Learning Models for Traffic Flow Prediction.

Deep Neural Network Based Attention Model for Structural Component Recognition (2023)
Presentation / Conference Contribution
Sarangi, S., & Mandal, B. (2023, February). Deep Neural Network Based Attention Model for Structural Component Recognition. Presented at 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications VISIGRAPP, Lisbon, Portugal

The recognition of structural components from images/videos is a highly complex task because of the appearance of huge components and their extended existence alongside, which are relatively small components. The latter is frequently overestimated or... Read More about Deep Neural Network Based Attention Model for Structural Component Recognition.

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.2022.116818

Image/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)
Presentation / Conference Contribution
Kaothalkar, A., Mandal, B., & Puhan, N. (2022, February). StructureNet: Deep Context Attention Learning for Structural Component Recognition. Presented at 17th International Conference on Computer Vision Theory and Applications, Virtual

Structural 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.

Perturbed Composite Attention Model for Macular Optical Coherence Tomography Image Classification (2021)
Journal Article
Mishra, S. S., Mandal, B., & Puhan, N. B. (2022). Perturbed Composite Attention Model for Macular Optical Coherence Tomography Image Classification. IEEE Transactions on Artificial Intelligence, 3(4), 625-635. https://doi.org/10.1109/tai.2021.3135797

In this article, we propose a deep architecture stemming from a perturbed composite attention mechanism with the following two novel attention modules: Multilevel perturbed spatial attention (MPSA) and multidimension attention (MDA) for macular optic... Read More about Perturbed Composite Attention Model for Macular Optical Coherence Tomography Image Classification.

Interleaved Deep Artifacts-Aware Attention Mechanism for Concrete Structural Defect Classification. (2021)
Journal Article
Mandal, B. (2021). Interleaved Deep Artifacts-Aware Attention Mechanism for Concrete Structural Defect Classification. IEEE Transactions on Image Processing, 6957 - 6969. https://doi.org/10.1109/TIP.2021.3100556

Automatic machine classification of concrete structural defects in images poses significant challenges because of multitude of problems arising from the surface texture, such as presence of stains, holes, colors, poster remains, graffiti, marking and... Read More about Interleaved Deep Artifacts-Aware Attention Mechanism for Concrete Structural Defect Classification..

GlaucoNet: Patch-Based Residual Deep Learning Network for Optic Disc and Cup Segmentation Towards Glaucoma Assessment (2021)
Journal Article
Mandal, B. (2021). GlaucoNet: Patch-Based Residual Deep Learning Network for Optic Disc and Cup Segmentation Towards Glaucoma Assessment. https://doi.org/10.1007/s42979-021-00491-1

Glaucoma is a chronic eye condition causing irreversible vision damage and presently stands as the second leading cause of blindness worldwide. Damaged optic disc and optic cup assessment in color fundus image has been shown to be a promising method... Read More about GlaucoNet: Patch-Based Residual Deep Learning Network for Optic Disc and Cup Segmentation Towards Glaucoma Assessment.

Multi-Deformation Aware Attention Learning for Concrete Structural Defect Classification (2020)
Journal Article
Bhattacharya, G., Mandal, B., & Puhan, N. B. (2020). Multi-Deformation Aware Attention Learning for Concrete Structural Defect Classification. IEEE Transactions on Circuits and Systems for Video Technology, 31(9), 3707-3713. https://doi.org/10.1109/TCSVT.2020.3028008

In this work, we propose a deep multi-deformation aware attention learning (MDAL) architecture comprising of multi-scale committee of attention (MSCA) and fine-grained feature induced attention (FGIA) modules to classify multi-target multi-class defe... Read More about Multi-Deformation Aware Attention Learning for Concrete Structural Defect Classification.