arXiv Computer Vision

Fusing Visual and Textual Representations via Multi-layer Fusing Transformers for Vietnamese Visual Question Answering

arXiv Machine Learning

Sep 24

VMMU: A Vietnamese Multitask Multimodal Understanding and Reasoning Benchmark

arXiv:2508.13680v5 Announce Type: replace-cross Abstract: We introduce VMMU, a Vietnamese Multitask Multimodal Understanding and Reasoning Benchmark designed to evaluate how vision-language models (V...

By Vy Tuong Dang, An Vo, Emilio Villa-Cueva, Quang Tau, Duc Dm, Thamar Solorio, Daeyoung Kim

llms computer-vision multimodal benchmarks

arXiv Computer Vision

Sep 15

Beyond OCR Accuracy: Text-Centric VQA Under Image Degradation with Modular and End-to-End

arXiv:2609.13815v1 Announce Type: new Abstract: Text-centric Visual Question Answering (VQA) requires reading and reasoning over text embedded in images, a task made substantially harder when images...

By Ritali Vatsi, Rachapudi Jagadeesh, Shruti Singh Baghel, Himani Sharma, Amit Shukla, Pawan Goyal

llms computer-vision nlp fine-tuning multimodal

arXiv Machine Learning

Sep 7

An Integrated Vision-and-Language Pretraining (VLP) and Visual Question Answering (VQA) model to Automate Nondestructive Evaluation Image Analysis

The paper presents ChatNDE Figure to Caption, an AI system that automates the interpretation of nondestructive evaluation (NDE) images. It combines a Vision‑and‑Language Pretraining (VLP) approach using ResNet50 for visual feature extraction and GPT‑2 for natural‑language captioning, evaluated with BLEU scores. Additionally, a Visual Question Answering (VQA) model is integrated to answer specific queries about the images, enhancing interactivity for field inspectors.

By Mehrdad Shafiei Dizaji, Hoda Azari

llms nlp

arXiv AI

Sep 16

Vision And Text Transformer For Predicting Answerability On Visual Question Answering

The paper introduces VT-Transformer, a model that predicts answerability scores for Visual Question Answering by treating the task as a regression problem rather than a binary classification. It leverages visual and textual features within a Transformer architecture and demonstrates improved performance and robustness on the VizWiz 2020 dataset compared to existing baselines.

By Tung Le, Huy Tien Nguyen, Le Minh Nguyen

llms nlp multimodal

arXiv AI

Jul 7

Parameter Efficient Multimodal Instruction Tuning for Romanian Vision Language Models

arXiv:2512. 14926v2 Announce Type: replace-cross Abstract: Focusing on low-resource languages is an essential step toward democratizing generative AI.

By George-Andrei Dima, R\u{a}zvan-Alexandru Sm\u{a}du, Dumitru-Clementin Cercel

llms nlp fine-tuning multimodal

arXiv Computer Vision

Aug 25

QIRL: Optimized Question-Image Relation Learning for Bias-Robust Visual Question Answering

arXiv:2504.03337v2 Announce Type: replace Abstract: Existing bias mitigation methods for Visual Question Answering (VQA), a typical Artificial intelligence application, endure two main limitations. F...

By Quanxing Xu, Ling Zhou, Xian Zhong, Feifei Zhang, Rubing Huang

diffusion nlp benchmarks safety

arXiv Computation and Language

Sep 1

ReVA: A Region-Aware Visual Assistant for Visually Grounded Question Answering

arXiv:2608.28707v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have achieved remarkable progress in Visual Question Answering (VQA), yet they continue to struggle with quest...

By Anoop Senthil

llms rag computer-vision nlp multimodal safety

arXiv AI

Jun 6

Noise-Aware Visual Representation Learning for Medical Visual Question Answering

arXiv:2606. 05535v1 Announce Type: cross Abstract: Medical visual question answering (Med-VQA) has strong potential for clinical decision support by enabling AI models to interpret medical images and answer clinically relevant queries.

By I Putu Adi Pratama, Bahadorreza Ofoghi, Atul Sajjanhar, Shang Gao

llms rag nlp fine-tuning benchmarks

arXiv AI

Sep 15

NoteVQA: Benchmarking VLMs on Real-Life Questions from Human Communities

NoteVQA is a new benchmark that collects 252 real‑life visual questions from the Chinese image‑sharing platform Xiaohongshu, covering 12 topics and 7 user intents. Each question is paired with a concise expert reference and a human‑audited interleaved answer that blends text and visual evidence. The study evaluates VLMs on short‑answer correctness and interleaved answer quality using a new AgenticInterleave framework and a 12‑dimensional IVR‑12 rubric, finding that even state‑of‑the‑art models achieve only about 53% accuracy and lag behind human references in content quality.

By Haonan Jiang, Guojian Zhan, Jiancong Xie, Shijun Wan, Dongiia Zhao, Cheng Chen, Yahui Liu, Yao Hu, Chuan Mu

llms agents multimodal benchmarks

arXiv Computer Vision

2d ago

Skeleton-and-Strategy Prompting: Training-Free Negation Understanding for Vision-Language Models

arXiv:2610.01180v1 Announce Type: new Abstract: Despite the strong performance of Vision-Language Models (VLMs) on a wide range of visual question answering (VQA) tasks, these models consistently str...

By Yuliang Cai, Mohammad Rostami, Jesse Thomason

llms nlp multimodal benchmarks

arXiv AI

Jul 20

Debiasing Text-to-Image Evaluation via Implicit Cultural Alignment Reward Modeling

arXiv:2607. 15740v1 Announce Type: cross Abstract: As Text-to-Image (T2I) systems rapidly advance, evaluating the cultural authenticity of synthesized content has become increasingly important for fair and trustworthy generative AI.

By Bo-An Chang, Yu-Chih Chen

llms diffusion nlp reinforcement-learning multimodal benchmarks safety

arXiv AI

Jun 11

Brain-IT-VQA: From Brain Signals to Answers

arXiv:2605. 29588v2 Announce Type: replace-cross Abstract: Decoding visual content from fMRI signals recorded while a person views images, and specifically answering questions about the seen images, is a long-standing challenge.

By Roman Beliy, Matias Cosarinsky, Oliver Heinimann, Navve Wasserman, Michal Irani

llms nlp benchmarks

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