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All Outputs (39)

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.

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.

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.

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.

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.

Towards Automatic Screening of Typical and Atypical Behaviors in Children With Autism (2019)
Presentation / Conference Contribution
Cook, A., Mandal, B., Berry, D., & Johnson, M. (2019, October). Towards Automatic Screening of Typical and Atypical Behaviors in Children With Autism. Paper presented at 2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA), Washington, DC, USA

Autism spectrum disorders (ASD) impact the cognitive, social, communicative and behavioral abilities of an individual. The development of new clinical decision support systems is of importance in reducing the delay between presentation of symptoms an... Read More about Towards Automatic Screening of Typical and Atypical Behaviors in Children With Autism.

Enhanced Deep Video Summarization Network (2019)
Presentation / Conference Contribution
Gonuguntla, N., Mandal, B., & Puhan, N. (2019, September). Enhanced Deep Video Summarization Network. Paper presented at 30th British Machine Vision Conference, Cardiff

Video summarization is understanding video which aims to get an abstract view of the original video sequence by the concatenation of keyframes representing the highlights of the video. In this work, we propose an enhanced deep summarization network (... Read More about Enhanced Deep Video Summarization Network.

Cross-spectral Periocular Recognition: a Survey (2019)
Presentation / Conference Contribution
Behera, S., Mandal, B., & Puhan, N. (2018, August). Cross-spectral Periocular Recognition: a Survey. Presented at 3rd International Conference on Emerging Research in Electronics, Computer Science and Technology - International Conference, ICERECT 2018

Among many biometrics such as face, iris, fingerprint and others, periocular region has the advantages over other biometrics because it is non-intrusive and serves as a balance between iris or eye region (very stringent, small area) and the whole fac... Read More about Cross-spectral Periocular Recognition: a Survey.

DeepPCA Based Objective Function for Melanoma Detection (2018)
Presentation / Conference Contribution
Sultana, N. N., Puhan, N. B., & Mandal, B. (2018, December). DeepPCA Based Objective Function for Melanoma Detection. Presented at 2018 International Conference on Information Technology (ICIT), Bhubaneswar, India

In this paper, we propose an objective function for the convolutional neural network to acquire the variation separability as opposed to the categorical cross entropy which maximizes according to the target labels. This approach is an unsupervised le... Read More about DeepPCA Based Objective Function for Melanoma Detection.

Deep Adaptive Temporal Pooling for Activity Recognition (2018)
Presentation / Conference Contribution
Song, S., Cheung, N.-M., Chandrasekhar, V., & Mandal, B. (2018, October). Deep Adaptive Temporal Pooling for Activity Recognition. Presented at MM '18: ACM Multimedia Conference, Seoul Republic of Korea

Deep neural networks have recently achieved competitive accuracy for human activity recognition. However, there is room for improvement, especially in modeling of long-term temporal importance and determining the activity relevance of different tempo... Read More about Deep Adaptive Temporal Pooling for Activity Recognition.

Deep Residual Network With Subclass Discriminant Analysis For Crowd Behavior Recognition (2018)
Presentation / Conference Contribution
Mandal, B., Fajtl, J., Argyriou, V., Monekosso, D., & Remagnino, P. (2018, October). Deep Residual Network With Subclass Discriminant Analysis For Crowd Behavior Recognition. Presented at 2018 IEEE International Conference on Image Processing, Athens

In this work, we extract rich representations of crowd behavior from video using a fine-tuned deep convolutional neural residual network. Using spatial partitioning trees we create subclasses within the feature maps from each of the crowd behavior a... Read More about Deep Residual Network With Subclass Discriminant Analysis For Crowd Behavior Recognition.

I2R VC @ ImageClef2017: Ensemble of Deep Learnt Features for Lifelog Video Summarization (2017)
Presentation / Conference Contribution
Molino, A., Mandal, B., Jie, L., Lim, J.-H., Subbaraju, V., & Chandrasekhar, V. I2R VC @ ImageClef2017: Ensemble of Deep Learnt Features for Lifelog Video Summarization

In this paper we describe our approach for the ImageCLEF-lifelog summarization task. A total of ten runs were submitted, which used only visual features, only metadata information, or both. In the first step, a set of relevant frames are drawn from t... Read More about I2R VC @ ImageClef2017: Ensemble of Deep Learnt Features for Lifelog Video Summarization.

An empirical approach for automatic face clustering on personal lifelogging images (2017)
Presentation / Conference Contribution
Subbaraju, V., Xu, Q., Mandal, B., Li, L., & Lim, J.-H. (2017, August). An empirical approach for automatic face clustering on personal lifelogging images. Presented at 2017 IEEE 2nd International Conference on Signal and Image Processing (ICSIP), Singapore

Life-logging applications generate a vast amount of personalized data that provides vital insights into the user's daily life. One such key insight is the people whom the user has come across/interacted with during regular life. This can be obtained... Read More about An empirical approach for automatic face clustering on personal lifelogging images.

Learning cognitive manifolds of faces (2017)
Presentation / Conference Contribution
Li, L., Mandal, B., Tan, C., & Lim, J.-H. (2017, August). Learning cognitive manifolds of faces. Presented at 2017 IEEE 2nd International Conference on Signal and Image Processing (ICSIP), Singapore

Inspired by the studies in psychology and neuroscience, we propose a computational model of cognitive face representation that mimics the mechanism of human face perception. We propose to learn two separate manifolds for facial identity and facial ex... Read More about Learning cognitive manifolds of faces.

Analysis of Human Attentions for Face Recognition on Natural Videos and Comparison with CV Algorithm on Performance (2017)
Presentation / Conference Contribution
Ragab Sayed, M., Yuting Lim, R., Mandal, B., Li, L., Hwee Lim, J., & Sim, T. (2017, March). Analysis of Human Attentions for Face Recognition on Natural Videos and Comparison with CV Algorithm on Performance. Presented at 2017 AAAI Spring Symposium, Stanford University, USA

Researchers have conducted many studies on human attentions and their eye gaze patterns for face recognition (FR), hoping to inspire new ideas to develop computer vision (CV) algorithms which perform like or even better than human. Yet, while these s... Read More about Analysis of Human Attentions for Face Recognition on Natural Videos and Comparison with CV Algorithm on Performance.