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
arXiv Computer Vision
2d ago

Counterfactual Reasoning for Robust Visual Question Answering

The paper introduces a new training framework for Visual Question Answering that leverages counterfactual contrastive learning to mitigate language bias and improve out‑of‑distribution generalization. It comprises a three‑stage curriculum for stable optimization, an enhanced Batch‑Contrastive loss for discriminative feature learning, and two regularizers—Answer‑Contrastive and Gradient‑Discrepancy—to refine predictions and enforce causal visual grounding. The resulting model attains 61.64% accuracy on the bias‑sensitive VQA‑CP v2 benchmark while maintaining 62.80% on the standard VQA v2 dataset, achieving a small generalization gap of 1.16%.

By Truong-Binh Duong, Thanh-Ngan Tran, Ngoc-Thao Nguyen, Bac Le
Hugging Face Trending Papers
Jul 8

Comparative Study of Domain-adapted VLMs for General Document Visual Question Answering

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.

arXiv Machine Learning
Sep 1

A Visual Question Answering Model to Automate Nondestructive Evaluation Image Analysis

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.

By Mehrdad Shafiei Dizaji, Hoda Azari