publications

2025

1. Preprint

   

   SloMo-Fast: Slow-Momentum and Fast-Adaptive Teachers for Source-Free Continual Test-Time Adaptation

   2025

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   Continual Test-Time Adaptation (CTTA) is crucial for deploying models in real-world applications with unseen, evolving target domains. Existing CTTA methods, however, often rely on source data or prototypes, limiting their applicability in privacy-sensitive and resource-constrained settings. Additionally, these methods suffer from long-term forgetting, which degrades performance on previously encountered domains as target domains shift. To address these challenges, we propose SloMo-Fast, a source-free, dual-teacher CTTA framework designed for enhanced adaptability and generalization. It includes two complementary teachers: the Slow-Teacher, which exhibits slow forgetting and retains long-term knowledge of previously encountered domains to ensure robust generalization, and the Fast-Teacher rapidly adapts to new domains while accumulating and integrating knowledge across them. This framework efficiently preserves knowledge of past domains, adapts efficiently to new ones. Our extensive experimental results demonstrate that SloMo-Fast consistently outperforms state-of-the-art methods across CTTA benchmarks, achieving a mean error rate of 33.8% in various TTA settings. Notably, it surpasses existing methods by a margin of at least 1.5%. Additionally, SloMo-Fast achieves significant performance improvements in Mixed Domain and our proposed new benchmark Mixed domain comes after Continual Domain scenarios along with Cyclic repeatation in continual test time adaptation setting, indicating its ability to learn generalized representations across domains.

2. Preprint

   

   FedCTTA: A Collaborative Approach to Continual Test-Time Adaptation in Federated Learning

   2025

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   Federated Learning (FL) enables collaborative model training across distributed clients without sharing raw data, making it ideal for privacy-sensitive applications. However, FL models often suffer performance degradation due to distribution shifts between training and deployment. Test-Time Adaptation (TTA) offers a promising solution by allowing models to adapt using only test samples. However, existing TTA methods in FL face challenges such as computational overhead, privacy risks from feature sharing, and scalability concerns due to memory constraints. To address these limitations, we propose Federated Continual Test-Time Adaptation (FedCTTA), a privacy-preserving and computationally efficient framework for federated adaptation. Unlike prior methods that rely on sharing local feature statistics, FedCTTA avoids direct feature exchange by leveraging similarity-aware aggregation based on model output distributions over randomly generated noise samples. This approach ensures adaptive knowledge sharing while preserving data privacy. Furthermore, FedCTTA minimizes the entropy at each client for continual adaptation, enhancing the model’s confidence in evolving target distributions. Our method eliminates the need for server-side training during adaptation and maintains a constant memory footprint, making it scalable even as the number of clients or training rounds increases. Extensive experiments show that FedCTTA surpasses existing methods across diverse temporal and spatial heterogeneity scenarios.

2024

1. Accepted

   

   MoE-TTA: Enhancing Continual Test-Time Adaptation for Vision-Language Models through Mixture of Experts

   Md Akil Raihan Iftee, Wahida Mahjabin, Anik Ekka, and 1 more author

   2024

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   Continual learning enables vision-language models to incrementally acquire new knowledge without relying on access to the entire historical dataset. This capability is crucial for adapting to evolving data distributions in real-world scenar- ios, where models must handle domain shifts and incorporate new information while retaining previously learned knowledge. However, maintaining performance in large-scale models remains challenging due to parameter shifts during learning and the sig- nificant computational costs associated with full-model updates. To address these challenges, this paper introduces a novel method for Continual Test-Time Domain Adaptation (CTTDA) on vision- language datasets, leveraging a MoE and adapter modules to optimize domain-specific adaptation while maintaining zero-shot classification capabilities. Utilising LoRA in MoE framework within transformer layers, the model efficiently updates a small set of parameters by dynamically selecting experts. This approach minimizes computational costs by activating only a portion of the model, avoiding the need for full-model updates during domain adaptation. During test-time adaptation, entropy loss is calculated without access to labels, improving the model’s fine-tuning and guiding towards confident predictions across domains. A contrastive warm-up phase further optimizes the adapter blocks by enhancing the differentiation of domain-specific and domain- invariant features, thereby establishing a strong foundation for effective test-time adaptation. The proposed MoE-TTA model achieves an average accuracy of 36.43% across diverse ImageNet datasets and 32.93% in fine-grained classification, demonstrating promising results, especially in datasets like EuroSAT (51.67%), while being lower than several competitors, with no doubt in its ability to capture domain shifts effectively.

2. Accepted

   

   Organ-Seg: A Vision-Language and LLM-Enhanced Framework for User-Guided Abdominal Organ Image Segmentation

   Md Akil Raihan Iftee, Tasfia Faija, Partho Choudhury Shoumya, and 1 more author

   2024

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   Medical image segmentation plays a vital role in diagnostic and treatment planning, where precision is crucial for accurate outcomes. Traditional segmentation methods, while effective in many areas, often fail to incorporate user-driven guidance, leading to errors in region identification, especially when irrelevant regions are segmented. In this study, we present a new, instruction-based medical image segmentation framework that enhances user interaction while delivering precise and context-aware results. Our approach addresses the limitations of previous works, such as vision-large language models (LLM) like LLaVA, which provide context but do not perform segmentation, and the Segment Anything Model, which performs segmenta- tion but does not incorporate user’s text-guided instruction. We propose a segmentation model framework that combines vision-language embeddings from LLava with SAM to perform accurate, query-based segmentation of medical images. A key innovation of our model framework is its ability to handle false premises—situations where a user queries for an organ not present in the image—by employing a similarity-based mechanism that prevents incorrect segmentation. Tested on MRI datasets, FLARE22, our system achieves the highest segmentation dice coefficient 63.9%, with significantly improved relevance and reliability. The results demonstrate the effectiveness of our approach in refining segmentation quality and enhancing user- guided interaction, thus offering an advanced tool for medical imaging applications.

3. Accepted

   

   Next-Gen Heart Disease Prediction: A Gradient Boosting Approach with L1-Regularized Neural Networks

   Md Akil Raihan Iftee, Raiyan Ashraf, Atiqul Islam, and 2 more authors

   2024

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   Medical image segmentation plays a vital role in diagnostic and treatment planning, where precision is crucial for accurate outcomes. Traditional segmentation methods, while effective in many areas, often fail to incorporate user-driven guidance, leading to errors in region identification, especially when irrelevant regions are segmented. In this study, we present a new, instruction-based medical image segmentation framework that enhances user interaction while delivering precise and context-aware results. Our approach addresses the limitations of previous works, such as vision-large language models (LLM) like LLaVA, which provide context but do not perform segmentation, and the Segment Anything Model, which performs segmenta- tion but does not incorporate user’s text-guided instruction. We propose a segmentation model framework that combines vision-language embeddings from LLava with SAM to perform accurate, query-based segmentation of medical images. A key innovation of our model framework is its ability to handle false premises—situations where a user queries for an organ not present in the image—by employing a similarity-based mechanism that prevents incorrect segmentation. Tested on MRI datasets, FLARE22, our system achieves the highest segmentation dice coefficient 63.9%, with significantly improved relevance and reliability. The results demonstrate the effectiveness of our approach in refining segmentation quality and enhancing user- guided interaction, thus offering an advanced tool for medical imaging applications.

2023

1. Thesis Book

   

   3D Cerebrovascular Segmentation Using Semi-Supervised Approach

   Sk. Imran Hossain MD Akil Raihan Iftee

   2023

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2. Published

   

   VioNet: An Enhanced Violence Detection Approach for Videos Using a Fusion Model of Vision Transformer with Bi-LSTM and 3D Convolutional Neural Networks

   Md Akil Raihan Iftee, Md Mominur Rahman, and Sunanda Das

   In International Conference on Big Data, IoT and Machine Learning, 2023

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   The identification of violence in real-world scenarios is imperative as it enables the detection of aggressive behavior, thereby preventing harm to individuals and communities. This is crucial for ensuring public safety and maintaining social order.

3. Published

   

   Unsupervised Binary Classification of Heart Diseases Using an Autoencoder Model with Boosting Algorithm

   Md Akil Raihan Iftee, and Sunanda Das

   In 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), 2023

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   The identification of violence in real-world scenarios is imperative as it enables the detection of aggressive behavior, thereby preventing harm to individuals and communities. This is crucial for ensuring public safety, facilitating effective crime investigation, promoting child safety, safeguarding mental health, and facilitating social media moderation. Various methods, including handcrafted techniques and deep learning algorithms, can be utilized in surveillance or CCTV cameras, as well as smartphones, to enable timely detection of violent behavior and facilitate appropriate action and intervention. In this study, we introduce VioNET, a novel approach that combines a 3D Convolutional Neural Network and a Vision Transformer with Bidirectional LSTM for the purpose of accurately detecting violence in video data. Since video data is inherently sequential, the extraction of spatiotemporal features is essential to accurate detection. The use of these two deep learning methods facilitates the extraction of maximum features, which are then fused together to classify videos with the highest possible accuracy. We evaluate the effectiveness of our approach by employing three datasets: Hockey, Movies, and Violent Flow, for analysis. The proposed model achieved impressive accuracies of 97.85%, 100.00%, and 96.33% on the Hokey, Movie, and Violent Flow datasets, respectively. Based on the obtained results, it is evident that our method showcases superior performance, outperforming several existing approaches in the field and establishing itself as a robust and competitive solution for violence detection in videos.

2022

1. Published

   

   Bangla natural language processing: A comprehensive analysis of classical, machine learning, and deep learning-based methods

   Ovishake Sen, Mohtasim Fuad, Md Nazrul Islam, and 8 more authors

   IEEE Access, 2022

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   The Bangla language is the seventh most spoken language, with 265 million native and non-native speakers worldwide. However, English is the predominant language for online resources and...

2021

1. Published

   

   Recent advances in deep learning techniques for face recognition

   Md Tahmid Hasan Fuad, Awal Ahmed Fime, Delowar Sikder, and 6 more authors

   IEEE Access, 2021

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   In recent years, researchers have proposed many deep learning (DL) methods for various tasks, and particularly face recognition (FR) made an enormous leap using these techniques. Deep FR systems benefit from the hierarchical architecture of the DL methods to learn discriminative face representation. Therefore, DL techniques significantly improve state-of-the-art performance on FR systems and encourage diverse and efficient real-world applications. In this paper, we present a comprehensive analysis of various FR systems that leverage the different types of DL techniques, and for the study, we summarize 171 recent contributions from this area. We discuss the papers related to different algorithms, architectures, loss functions, activation functions, datasets, challenges, improvement ideas, current and future trends of DL-based FR systems. We provide a detailed discussion of various DL methods to understand the current state-of-the-art, and then we discuss various activation and loss functions for the methods. Additionally, we summarize different datasets used widely for FR tasks and discuss challenges related to illumination, expression, pose variations, and occlusion. Finally, we discuss improvement ideas, current and future trends of FR tasks.