The paper introduces BrailleBench, a benchmark of 259 real-world Grade‑1 and Grade‑2 Braille images, and evaluates various vision‑language models (VLMs) on the task of transcribing Braille from images. It finds that general‑purpose VLMs perform poorly, while tool‑assisted systems do better. By training a 2B VLM on a synthetic Braille generation pipeline, the authors achieve 87% exact match on Grade‑1 and 61% on Grade‑2 BrailleBench, surpassing other evaluated systems, but also demonstrate that high synthetic validation accuracy does not translate to real‑world performance.
arXiv:2604.09531v2 Announce Type: replace-cross Abstract: Vision-language models (VLMs) still struggle with visual perception tasks such as spatial understanding and viewpoint recognition, largely be...
The paper introduces Test-Time Logit Prompting (TLP), a lightweight framework that adapts vision-language models to missing-modality inputs without accessing source training data. TLP optimizes logit prompts using uncertainty-aware adjustments and modality-complete consistency regularization, thereby maintaining prediction confidence and semantic consistency. Experiments on various benchmarks show that TLP improves recognition performance by up to 8% while requiring only a few hundred tunable parameters and minimal test-time optimization steps.
Vision-language models (VLMs) have achieved remarkable performance by leveraging complementary information from large-scale image-text pairs. However, missing-modality inputs are commonly encountered...
VISTA (Visually Impaired Scene and Task Annotations) is a multimodal egocentric benchmark designed to evaluate goal‑oriented assistance for blind and low‑vision users. It comprises about 1,000 recordings from Project Aria glasses, each providing six synchronized sensor streams and three types of annotations across ten task categories, from object localization to hazard detection. The benchmark tests vision‑language models in zero‑shot settings and introduces a lightweight multimodal baseline that integrates all six modalities, revealing that current models can describe scenes but struggle to generate effective assistive guidance.
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...
The paper introduces FOCUS, a cross‑dataset benchmark for retinal fundus models that evaluates vision‑only encoders, vision‑language dual encoders, and multimodal large language models across ten public datasets. It measures ranking performance, calibration, subgroup disparities, and image‑quality robustness for tasks such as diabetic retinopathy and glaucomatous optic neuropathy. Results show no single model family dominates, with vision‑only encoders ranking highest on average, while fine‑tuning improves in‑domain performance but varies in external transfer.
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:2610.33078v1 Announce Type: cross Abstract: Visual grounding (VG) in visual question answering (VQA) is a fundamental capability of Vision-Language Models (VLMs), enabling the detection of the...
arXiv:2607. 08317v1 Announce Type: new Abstract: Modern AI models achieve strong performance on many established benchmarks, yet they still fail on tasks that humans find almost trivial, such as manipulating a string or drawing a dog with five legs.
The paper introduces Salience-LLaVA, a vision‑language model that prioritizes scene elements based on their importance for low‑vision users. It presents three new salience‑aware datasets—Salience COCO, Salience Flickr, and Salience VizWiz—annotated with object‑level salience verified by low‑vision participants. The authors also propose the SCMI metric to evaluate caption ordering accuracy and demonstrate the system’s practicality by deploying it on assistive glasses.
The paper examines OCR adaptation for low‑resource languages, noting that fine‑tuning often hits a performance ceiling in data‑scarce settings. It identifies that lower layers of language‑specific models learn redundant features while higher layers capture script nuances, leading to a structural inefficiency. To address this, the authors propose PSMC, a framework that pre‑trains a base model, specializes it per language, merges the experts via task arithmetic, and co‑trains a unified multilingual backbone, achieving about a 2% improvement in Word Recognition Rate across 10 Indian scripts without adding parameters.
arXiv:2502. 00241v2 Announce Type: replace-cross Abstract: Incorporating multiple modalities into large language models (LLMs) is a powerful way to enhance their understanding of non-textual data, enabling them to perform multimodal tasks.