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.
arXiv:2606. 28329v1 Announce Type: cross Abstract: The growing adoption of AI in healthcare, particularly in preventive care, highlights the critical need for accessibility and precision in Medical Question Answering (MedQA).
This article presents a Visual Question Answering (VQA) model tailored for nondestructive evaluation (NDE) image analysis. The system combines a ResNet‑50 image encoder with a GPT‑2 language generator, allowing inspectors to ask targeted questions such as "Is there a crack?" or "Where is the defect located?" and receive precise answers. By facilitating direct question‑and‑answer interactions, the VQA model aims to improve inspection efficiency, reduce errors, and enhance usability in field scenarios.
The paper investigates how to best integrate large language model (LLM) guidance into visual question answering (VQA) systems. By prompting the 70‑billion‑parameter Llama 3.3 model to identify visual concepts, distractors, and external facts for each question, the authors inject this guidance into a frozen Vision Transformer (ViT) and a T5 text‑to‑text model via two methods: decision‑level late fusion and attention‑matrix injection. On the OK‑VQA benchmark, the shallow late‑fusion approach outperforms the deeper attention injection, achieving 39.4% exact match versus 31.8%, and a paraphrasing pipeline further expands the training set tenfold, indicating that the depth of integration should match the data regime.
Document Visual Question Answering (DocVQA) presents a complex multimodal challenge, requiring models to exploit visual, textual, and layout information from documents. Although Vision-Language Models (VLMs) have shown remarkable performance in text-vision tasks, their robustness and transferability to different document domains remains underexplored.
The paper introduces three new vision‑centric evaluation benchmarks—temporal frame retrieval, video future prediction, and causal memory distortion—to assess visual question answering in large video models. Unlike traditional benchmarks that rely on text-based multiple choice questions, these tasks require models to reason directly from visual inputs. The authors find that current state‑of‑the‑art models struggle with visual queries, highlighting a gap in visual understanding that future research should address.
arXiv:2607. 07179v1 Announce Type: cross Abstract: Document Visual Question Answering (DocVQA) presents a complex multimodal challenge, requiring models to exploit visual, textual, and layout information from documents.
The paper introduces a Multi-layer Fusing Transformer that uses cross‑attention to merge visual and textual features across multiple layers, enabling extraction of information from low to high levels. Experiments and ablation studies on the ViVQA dataset demonstrate that this architecture outperforms competitive baselines for Vietnamese Visual Question Answering. The work addresses a notable gap in VQA research for Vietnamese, a language with limited prior resources.
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.
DocAttriBench (DAB) is a large‑scale benchmark for fine‑grained, element‑level source attribution in Document Visual Question Answering (VQA). It introduces MAPPET, a Mask‑based Perplexity‑Derived Attribution method that uses document layout and language modeling to identify the most informative layout element for each answer. The benchmark contains 237k documents and 296k question‑answer pairs with element‑level grounding, and it evaluates multimodal LLMs on answer accuracy, attribution accuracy, and overall answer quality, revealing that even strong models often fail to localize supporting elements.
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...
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...
The paper introduces the Generative Embedding Benchmark (GEB), which evaluates how much content from an embedding can be recovered by a decoder that only has access to the frozen embedding and a question, without the original image or intermediate features. GEB uses a curated visual‑question‑answering dataset with 1,800 development and 900 test items covering natural images, scene text, and visual documents. Experiments on seven public embedding models show that visual‑only scores range from 28.25 to 33.21, while joint image‑question encoding boosts scores up to 65.56, revealing that generative readout uncovers information bottlenecks not captured by traditional separability‑based benchmarks.