activity
20232026
most citedFrom Image to Language: A Critical Analysis of Visual Question Answering (VQA) Approaches, Challenges, and Opportunities

90 citations · 91 across the 8 of their papers we have counts for

collaborators

13 papers

cs.CV2026

VisCurate: Evaluating Behavioral Equivalence of Visual Skills

Md Farhan Ishraq, Md Farhan Ishmam, Reaz Hassan Joarder +1

Modern vision-based agents have shifted toward skill-based problem-solving, where a skill is an executable image-to-image operation, e.g., cropping, thresholding, or edge detection…

cs.LG2026

Don't Retrain, Remix: Adaptation of Web Agents using Mixture Hypernetworks

Md Farhan Ishmam, Minh Pham Dinh, Kenneth Marino

The web is not static or uniform. Websites that serve the same purpose can look and navigate very differently. Even within a single site, successive redesigns can change how famili…

cs.CL2026

Can LLMs Tell Stories Out of Order? A Corpus and Benchmark for Nonlinear Narrative Generation

Syed Rifat Raiyan, Md. Tariquzzaman, Md Farhan Ishmam +1

Every story has two orders: the order in which its events happen and the order in which they are told. A story is nonlinear when the two differ. We introduce a benchmark of 83 publ…

cs.AI2026

Teaching Agents in their Own Style: Blackbox Teacher Distillation for Web Agents

Md Farhan Ishmam, Kenneth Marino

Frontier web agents using closed models have high inference and computational costs. Smaller open models can be an alternative, but to be performant, they require training supervis…

cs.CL2026

The Hard Part Comes After Search: Benchmarking Web Agents on Synthesizing, Organizing, and Displaying Knowledge

Alexander Gill, Md Farhan Ishmam, Xuyen Nguyen +6

Existing computer-use agent benchmarks do not fully evaluate agents acting as assistants. A useful assistant retrieves information across complex, multi-step workflows, synthesizes…

cs.CL2026

DORA Explorer: Improving the Exploration Ability of LLMs Without Training

Priya Gurjar, Md Farhan Ishmam, Kenneth Marino

Large language model (LLM) agents for sequential decision-making struggle to produce diverse outputs. This leads to insufficient exploration, suboptimal solutions, and repeated act…

cs.AI2026

TimeWarp: Evaluating Web Agents by Revisiting the Past

Md Farhan Ishmam, Kenneth Marino

As web agents close the gap with humans on benchmarks, one question arises: Do today's agents perform just as well on tomorrow's web? We introduce TimeWarp, a benchmark that emulat…

cs.HC2025★ 1 cited

Prompting with Sign Parameters for Low-resource Sign Language Instruction Generation

Md Tariquzzaman, Md Farhan Ishmam, Saiyma Sittul Muna +2

Sign Language (SL) enables two-way communication for the deaf and hard-of-hearing community, yet many sign languages remain under-resourced in the AI space. Sign Language Instructi…

cs.CL2024

FourierKAN outperforms MLP on Text Classification Head Fine-tuning

Abdullah Al Imran, Md Farhan Ishmam

In resource constraint settings, adaptation to downstream classification tasks involves fine-tuning the final layer of a classifier (i.e. classification head) while keeping rest of…

cs.CL2024

Contextual Breach: Assessing the Robustness of Transformer-based QA Models

Asir Saadat, Nahian Ibn Asad

Contextual question-answering models are susceptible to adversarial perturbations to input context, commonly observed in real-world scenarios. These adversarial noises are designed…

cs.CL2024

BnSentMix: A Diverse Bengali-English Code-Mixed Dataset for Sentiment Analysis

Sadia Alam, Md Farhan Ishmam, Navid Hasin Alvee +3

The widespread availability of code-mixed data can provide valuable insights into low-resource languages like Bengali, which have limited datasets. Sentiment analysis has been a fu…

cs.CV2024

Visual Robustness Benchmark for Visual Question Answering (VQA)

Md Farhan Ishmam, Ishmam Tashdeed, Talukder Asir Saadat +3

Can Visual Question Answering (VQA) systems perform just as well when deployed in the real world? Or are they susceptible to realistic corruption effects e.g. image blur, which can…

cs.CV2023★ 90 cited

From Image to Language: A Critical Analysis of Visual Question Answering (VQA) Approaches, Challenges, and Opportunities

Md Farhan Ishmam, Md Sakib Hossain Shovon, M. F. Mridha +1

The multimodal task of Visual Question Answering (VQA) encompassing elements of Computer Vision (CV) and Natural Language Processing (NLP), aims to generate answers to questions on…