2 citations · 4 across the 24 of their papers we have counts for
25 papers
CAT-GS: Balanced Multimodal Learning via Calibrated Gating and Fusion Surgery
Mahir Shahriar Tamim, Sharjil Khan, Md. Samiul Alim +3
End-to-end training of multimodal neural networks often exhibits unstable neural dynamics characterized by three coupled failure modes that degrade learning: (i) modality imbalance…
Beyond Global Editing: Per-Instance Disentangled Subspaces for Training-Free Hallucination Mitigation in LVLMs
Ali Cheraghian, Hamidreza Dastmalchi, Hamed Barzamini +4
Recent advances in large vision-language models (LVLMs) have enabled powerful multimodal reasoning by integrating visual encoders with large language models (LLMs). However, their…
CARNet Cycle-Conditioned Core Aggregation and Redistribution for Multivariate Time Series Forecasting
Awsaf Tausif Adib, Md. Shahria Sarker Shuvo, Md. Estehaar Ahmed Emon +4
Accurately modeling cross-variate dependencies remains a key challenge in multivariate time series forecasting, particularly in the presence of strong periodic patterns. Many exist…
A Point Cloud Transformer for Remote Monitoring and Automated Assessment of Physical Rehabilitation Exercises
Kazi Rafat, Md. Ismail Hossain, M M Lutfe Elahi +4
Rehabilitation exercises are essential in restoring lost physical functions of patients suffering from various diseases (e.g., Parkinson's, back pain). Carrying out these rehabilit…
One Step Closer to Ground Truth: A Multi-Scale Residual-Aware Representation Learning Pipeline for Predicting Time Series Data
Amrijit Biswas, Mustafa Kamal, Robin Krambroeckers +4
Transformer-based models have emerged as leading paradigms in time-series forecasting in recent years, employing self-attention mechanisms to capture long-range dependencies. Despi…
Beyond Real Weights: Hypercomplex Representations for Stable Quantization
Jawad Ibn Ahad, Maisha Rahman, Amrijit Biswas +5
Multimodal language models (MLLMs) require large parameter capacity to align high-dimensional visual features with linguistic representations, making them computationally heavy and…
LAET: A Layer-wise Adaptive Ensemble Tuning Framework for Pretrained Language Models
Jawad Ibn Ahad, Muhammad Rafsan Kabir, Robin Krambroeckers +3
Natural Language Processing (NLP) has transformed the financial industry, enabling advancements in areas such as textual analysis, risk management, and forecasting. Large language…
Teacher-Guided One-Shot Pruning via Context-Aware Knowledge Distillation
Md. Samiul Alim, Sharjil Khan, Amrijit Biswas +3
Unstructured pruning remains a powerful strategy for compressing deep neural networks, yet it often demands iterative train-prune-retrain cycles, resulting in significant computati…
Dynamic Temperature Scheduler for Knowledge Distillation
Sibgat Ul Islam, Jawad Ibn Ahad, Fuad Rahman +3
Knowledge Distillation (KD) trains a smaller student model using a large, pre-trained teacher model, with temperature as a key hyperparameter controlling the softness of output pro…
Label Semantics for Robust Hyperspectral Image Classification
Rafin Hassan, Zarin Tasnim Roshni, Rafiqul Bari +4
Hyperspectral imaging (HSI) classification is a critical tool with widespread applications across diverse fields such as agriculture, environmental monitoring, medicine, and materi…
HadaSmileNet: Hadamard fusion of handcrafted and deep-learning features for enhancing facial emotion recognition of genuine smiles
Mohammad Junayed Hasan, Nabeel Mohammed, Shafin Rahman +1
The distinction between genuine and posed emotions represents a fundamental pattern recognition challenge with significant implications for data mining applications in social scien…
Z-Pruner: Post-Training Pruning of Large Language Models for Efficiency without Retraining
Samiul Basir Bhuiyan, Md. Sazzad Hossain Adib, Mohammed Aman Bhuiyan +4
Large language models (LLMs) have rapidly advanced in recent years, achieving remarkable performance across a wide range of natural language processing tasks. However, this progres…
Temporal Window Smoothing of Exogenous Variables for Improved Time Series Prediction
Mustafa Kamal, Niyaz Bin Hashem, Robin Krambroeckers +2
Although most transformer-based time series forecasting models primarily depend on endogenous inputs, recent state-of-the-art approaches have significantly improved performance by…
LegalRAG: A Hybrid RAG System for Multilingual Legal Information Retrieval
Muhammad Rafsan Kabir, Rafeed Mohammad Sultan, Fuad Rahman +4
Natural Language Processing (NLP) and computational linguistic techniques are increasingly being applied across various domains, yet their use in legal and regulatory tasks remains…
VideoLights: Feature Refinement and Cross-Task Alignment Transformer for Joint Video Highlight Detection and Moment Retrieval
Dhiman Paul, Md Rizwan Parvez, Nabeel Mohammed +1
Prevailing joint prediction transformers for Video Highlight Detection and Moment Retrieval (HD/MR) exhibit deficiencies in handling cross-task dynamics, achieving robust video-tex…
Test-Time Adaptation of 3D Point Clouds via Denoising Diffusion Models
Hamidreza Dastmalchi, Aijun An, Ali Cheraghian +2
Test-time adaptation (TTA) of 3D point clouds is crucial for mitigating discrepancies between training and testing samples in real-world scenarios, particularly when handling corru…
BanglaDialecto: An End-to-End AI-Powered Regional Speech Standardization
Md. Nazmus Sadat Samin, Jawad Ibn Ahad, Tanjila Ahmed Medha +4
This study focuses on recognizing Bangladeshi dialects and converting diverse Bengali accents into standardized formal Bengali speech. Dialects, often referred to as regional langu…
Empowering Meta-Analysis: Leveraging Large Language Models for Scientific Synthesis
Jawad Ibn Ahad, Rafeed Mohammad Sultan, Abraham Kaikobad +4
This study investigates the automation of meta-analysis in scientific documents using large language models (LLMs). Meta-analysis is a robust statistical method that synthesizes th…
Foundation Model-Powered 3D Few-Shot Class Incremental Learning via Training-free Adaptor
Sahar Ahmadi, Ali Cheraghian, Morteza Saberi +4
Recent advances in deep learning for processing point clouds hold increased interest in Few-Shot Class Incremental Learning (FSCIL) for 3D computer vision. This paper introduces a…
3D Point Cloud Network Pruning: When Some Weights Do not Matter
Amrijit Biswas, Md. Ismail Hossain, M M Lutfe Elahi +4
A point cloud is a crucial geometric data structure utilized in numerous applications. The adoption of deep neural networks referred to as Point Cloud Neural Networks (PC- NNs), fo…
Beyond Labels: Aligning Large Language Models with Human-like Reasoning
Muhammad Rafsan Kabir, Rafeed Mohammad Sultan, Ihsanul Haque Asif +5
Aligning large language models (LLMs) with a human reasoning approach ensures that LLMs produce morally correct and human-like decisions. Ethical concerns are raised because curren…
Backpropagation-free Network for 3D Test-time Adaptation
Yanshuo Wang, Ali Cheraghian, Zeeshan Hayder +7
Real-world systems often encounter new data over time, which leads to experiencing target domain shifts. Existing Test-Time Adaptation (TTA) methods tend to apply computationally h…
Continual Test-time Domain Adaptation via Dynamic Sample Selection
Yanshuo Wang, Jie Hong, Ali Cheraghian +4
The objective of Continual Test-time Domain Adaptation (CTDA) is to gradually adapt a pre-trained model to a sequence of target domains without accessing the source data. This pape…
ChatGPT-guided Semantics for Zero-shot Learning
Fahimul Hoque Shubho, Townim Faisal Chowdhury, Ali Cheraghian +3
Zero-shot learning (ZSL) aims to classify objects that are not observed or seen during training. It relies on class semantic description to transfer knowledge from the seen classes…