arXiv Machine Learning
Jun 15

Pix2Fact: When Vision Is Not Enough -- Benchmarking Fine-Grained VQA with Web Verification on High-Resolution Real-World Scenes

arXiv:2602. 00593v4 Announce Type: replace-cross Abstract: Despite progress on general tasks, vision-language models (VLMs) still struggle with challenges that demand both fine-grained visual grounding and external knowledge, a synergy overlooked by existing benchmarks that evaluate these abilities in isolation.

By Yifan Jiang, Cong Zhang, Bofei Zhang, Qiaofeng Zheng, Yifan Yang, Bingzhang Wang, Yew-Soon Ong
arXiv Machine Learning
Aug 13

Multilingual OCR-Aware Fine-Tuning and Prompt-Guided Chain-of-Thought Reasoning for Multimodal Large Language Models

arXiv:2605. 16409v3 Announce Type: replace-cross Abstract: Optical character recognition (OCR) and multilingual scene-text understanding remain challenging for multimodal large language models (MLLMs), particularly in real-world images containing small or degraded text, cluttered layouts, occlusion, handwriting, and complex typography.

By Qinwu Xu, Yifan Jiang, Haoyu Ren
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