6 citations · 9 across the 9 of their papers we have counts for
10 papers
Investigating Length Bias and Robustness in LVLMs for Multiple-Choice Question Answering
Md. Atabuzzaman, Hani Alomari, Chris Thomas
Large Vision-Language Models (LVLMs) demonstrate strong performance on multiple-choice question answering (MCQA), yet their reliability under biased conditions remains underexplore…
IntroConformal: Conformal Factuality Guarantees for Large Vision-Language Models via Introspective Signals
Md. Atabuzzaman, Christian Alexander, Chris Thomas
Large Vision-Language Models (LVLMs) have achieved strong multimodal performance, yet ensuring the factual correctness of generated content remains challenging. Existing methods th…
Reliability Challenges in Diffusion Vision-Language Models
Md. Atabuzzaman, Chris Thomas
Diffusion-based Large Vision-Language Models (dLVLMs) have recently emerged as a compelling alternative to autoregressive (AR) LVLMs, offering advantages in parallel decoding, bidi…
Zero-Shot Fine-Grained Image Classification Using Large Vision-Language Models
Md. Atabuzzaman, Andrew Zhang, Chris Thomas
Large Vision-Language Models (LVLMs) have demonstrated impressive performance on vision-language reasoning tasks. However, their potential for zero-shot fine-grained image classifi…
Benchmarking and Mitigating MCQA Selection Bias of Large Vision-Language Models
Md. Atabuzzaman, Ali Asgarov, Chris Thomas
Large Vision-Language Models (LVLMs) have achieved strong performance on vision-language tasks, particularly Visual Question Answering (VQA). While prior work has explored unimodal…
ENTER: Event Based Interpretable Reasoning for VideoQA
Hammad Ayyubi, Junzhang Liu, Ali Asgarov +10
In this paper, we present ENTER, an interpretable Video Question Answering (VideoQA) system based on event graphs. Event graphs convert videos into graphical representations, where…
JourneyBench: A Challenging One-Stop Vision-Language Understanding Benchmark of Generated Images
Zhecan Wang, Junzhang Liu, Chia-Wei Tang +11
Existing vision-language understanding benchmarks largely consist of images of objects in their usual contexts. As a consequence, recent multimodal large language models can perfor…
What Matters in Explanations: Towards Explainable Fake Review Detection Focusing on Transformers
Md Shajalal, Md Atabuzzaman, Alexander Boden +2
Customers' reviews and feedback play crucial role on electronic commerce~(E-commerce) platforms like Amazon, Zalando, and eBay in influencing other customers' purchasing decisions.…
Arabic Sentiment Analysis with Noisy Deep Explainable Model
Md. Atabuzzaman, Md Shajalal, Maksuda Bilkis Baby +1
Sentiment Analysis (SA) is an indispensable task for many real-world applications. Compared to limited resourced languages (i.e., Arabic, Bengali), most of the research on SA are c…
Textual Entailment Recognition with Semantic Features from Empirical Text Representation
Md Shajalal, Md Atabuzzaman, Maksuda Bilkis Baby +2
Textual entailment recognition is one of the basic natural language understanding(NLU) tasks. Understanding the meaning of sentences is a prerequisite before applying any natural l…