Beyond OCR Accuracy: Text-Centric VQA Under Image Degradation with Modular and End-to-End
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The Flow has not summarised this story yet — read it at arXiv Computer Vision.
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.
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.
arXiv:2606. 00435v1 Announce Type: cross Abstract: Vision-language models (VLMs) can produce confident visual answers even when the required visual evidence is missing, blank, or unrelated to the question.
We present HunyuanOCR-1. 5, a lightweight end-to-end OCR-specialized vision-language model.
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.
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...