9 citations · 27 across the 13 of their papers we have counts for
13 papers
Continual Test-Time Adaptation in Computer Vision: Methods, Benchmarks, and Future Directions
Sarthak Kumar Maharana, Shambhavi Mishra, Yunbei Zhang +6
Deep neural nets achieve remarkable performance when training and test data share the same distribution, but this assumption frequently breaks in real-world deployment, where data…
Position: AI Governance Needs ISO-like Interoperability Protocols, Not Just Laws
Azmine Toushik Wasi, Mst Rafia Islam, Mahfuz Ahmed Anik +3
As Artificial Intelligence (AI) systems become deeply integrated into critical global infrastructure, the urgency for robust governance frameworks has intensified. However, current…
SweEval: Do LLMs Really Swear? A Safety Benchmark for Testing Limits for Enterprise Use
Hitesh Laxmichand Patel, Amit Agarwal, Arion Das +6
Enterprise customers are increasingly adopting Large Language Models (LLMs) for critical communication tasks, such as drafting emails, crafting sales pitches, and composing casual…
Crowdsource, Crawl, or Generate? Creating SEA-VL, a Multicultural Vision-Language Dataset for Southeast Asia
Samuel Cahyawijaya, Holy Lovenia, Joel Ruben Antony Moniz +89
Southeast Asia (SEA) is a region of extraordinary linguistic and cultural diversity, yet it remains significantly underrepresented in vision-language (VL) research. This often resu…
MVTamperBench: Evaluating Robustness of Vision-Language Models
Amit Agarwal, Srikant Panda, Angeline Charles +8
Multimodal Large Language Models (MLLMs), are recent advancement of Vision-Language Models (VLMs) that have driven major advances in video understanding. However, their vulnerabili…
GReFEL: Geometry-Aware Reliable Facial Expression Learning under Bias and Imbalanced Data Distribution
Azmine Toushik Wasi, Taki Hasan Rafi, Raima Islam +2
Reliable facial expression learning (FEL) involves the effective learning of distinctive facial expression characteristics for more reliable, unbiased and accurate predictions in r…
Exploring Bengali Religious Dialect Biases in Large Language Models with Evaluation Perspectives
Azmine Toushik Wasi, Raima Islam, Mst Rafia Islam +2
While Large Language Models (LLM) have created a massive technological impact in the past decade, allowing for human-enabled applications, they can produce output that contains ste…
BanglaAutoKG: Automatic Bangla Knowledge Graph Construction with Semantic Neural Graph Filtering
Azmine Toushik Wasi, Taki Hasan Rafi, Raima Islam +1
Knowledge Graphs (KGs) have proven essential in information processing and reasoning applications because they link related entities and give context-rich information, supporting e…
When SMILES have Language: Drug Classification using Text Classification Methods on Drug SMILES Strings
Azmine Toushik Wasi, Šerbetar Karlo, Raima Islam +2
Complex chemical structures, like drugs, are usually defined by SMILES strings as a sequence of molecules and bonds. These SMILES strings are used in different complex machine lear…
CADGL: Context-Aware Deep Graph Learning for Predicting Drug-Drug Interactions
Azmine Toushik Wasi, Taki Hasan Rafi, Raima Islam +2
Examining Drug-Drug Interactions (DDIs) is a pivotal element in the process of drug development. DDIs occur when one drug's properties are affected by the inclusion of other drugs.…
Fairness and Privacy-Preserving in Federated Learning: A Survey
Taki Hasan Rafi, Faiza Anan Noor, Tahmid Hussain +1
Federated learning (FL) as distributed machine learning has gained popularity as privacy-aware Machine Learning (ML) systems have emerged as a technique that prevents privacy leaka…
ARBEx: Attentive Feature Extraction with Reliability Balancing for Robust Facial Expression Learning
Azmine Toushik Wasi, Karlo Šerbetar, Raima Islam +2
In this paper, we introduce a framework ARBEx, a novel attentive feature extraction framework driven by Vision Transformer with reliability balancing to cope against poor class dis…
A Generalized Look at Federated Learning: Survey and Perspectives
Taki Hasan Rafi, Faiza Anan Noor, Tahmid Hussain +2
Federated learning (FL) refers to a distributed machine learning framework involving learning from several decentralized edge clients without sharing local dataset. This distribute…