445 citations · 545 across the 9 of their papers we have counts for
13 papers
GET: Generative Embedding Translation for Medical Image Segmentation
Md Maklachur Rahman, Md Hasan Al Banna, Saraf Anjum +2
Generative segmentation provides an alternative to direct pixel-wise prediction by operating on learned latent representations, but effective image-to-mask translation must preserv…
Context-Aware Zero-Shot Anomaly Detection in Surveillance Using Contrastive and Predictive Spatiotemporal Modeling
Md. Rashid Shahriar Khan, Md. Abrar Hasan, Mohammod Tareq Aziz Justice
Detecting anomalies in surveillance footage is inherently challenging due to their unpredictable and context-dependent nature. This work introduces a novel context-aware zero-shot…
Efficient Movie Scene Detection using State-Space Transformers
Md Mohaiminul Islam, Mahmudul Hasan, Kishan Shamsundar Athrey +2
The ability to distinguish between different movie scenes is critical for understanding the storyline of a movie. However, accurately detecting movie scenes is often challenging as…
Modelling Lips-State Detection Using CNN for Non-Verbal Communications
Abtahi Ishmam, Mahmudul Hasan, Md. Saif Hassan Onim +3
Vision-based deep learning models can be promising for speech-and-hearing-impaired and secret communications. While such non-verbal communications are primarily investigated with h…
The Case for High-Accuracy Classification: Think Small, Think Many!
Mohammad Hosseini, Mahmudul Hasan
To facilitate implementation of high-accuracy deep neural networks especially on resource-constrained devices, maintaining low computation requirements is crucial. Using very deep…
Improving Multimodal Accuracy Through Modality Pre-training and Attention
Aya Abdelsalam Ismail, Mahmudul Hasan, Faisal Ishtiaq
Training a multimodal network is challenging and it requires complex architectures to achieve reasonable performance. We show that one reason for this phenomena is the difference b…
Prediction and Description of Near-Future Activities in Video
Tahmida Mahmud, Mohammad Billah, Mahmudul Hasan +1
Most of the existing works on human activity analysis focus on recognition or early recognition of the activity labels from complete or partial observations. Similarly, almost all…
Context-Aware Query Selection for Active Learning in Event Recognition
Mahmudul Hasan, Sujoy Paul, Anastasios I. Mourikis +1
Activity recognition is a challenging problem with many practical applications. In addition to the visual features, recent approaches have benefited from the use of context, e.g.,…
Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation
Md Zahangir Alom, Mahmudul Hasan, Chris Yakopcic +2
Deep learning (DL) based semantic segmentation methods have been providing state-of-the-art performance in the last few years. More specifically, these techniques have been success…
Improved Inception-Residual Convolutional Neural Network for Object Recognition
Md Zahangir Alom, Mahmudul Hasan, Chris Yakopcic +2
Machine learning and computer vision have driven many of the greatest advances in the modeling of Deep Convolutional Neural Networks (DCNNs). Nowadays, most of the research has bee…
Handwritten Bangla Character Recognition Using The State-of-Art Deep Convolutional Neural Networks
Md Zahangir Alom, Peheding Sidike, Mahmudul Hasan +2
In spite of advances in object recognition technology, Handwritten Bangla Character Recognition (HBCR) remains largely unsolved due to the presence of many ambiguous handwritten ch…
Inception Recurrent Convolutional Neural Network for Object Recognition
Md Zahangir Alom, Mahmudul Hasan, Chris Yakopcic +1
Deep convolutional neural networks (DCNNs) are an influential tool for solving various problems in the machine learning and computer vision fields. In this paper, we introduce a ne…
Learning Temporal Regularity in Video Sequences
Mahmudul Hasan, Jonghyun Choi, Jan Neumann +2
Perceiving meaningful activities in a long video sequence is a challenging problem due to ambiguous definition of 'meaningfulness' as well as clutters in the scene. We approach thi…