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Visual Question Answering: A Survey on Techniques and Common Trends in Recent Literature

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arxiv 2305.11033 v2 pith:ZYF6QCVQ submitted 2023-05-18 cs.CV cs.AIcs.LG

Ana Cláudia Akemi Matsuki de Faria , Felype de Castro Bastos , José Victor Nogueira Alves da Silva , Vitor Lopes Fabris , Valeska de Sousa Uchoa , Décio Gonçalves de Aguiar Neto , Claudio Filipi Goncalves dos Santos This is my paper · ORCID
classification cs.CVcs.AIcs.LG
keywords recentwereareaanalyzedansweringcommonprovidedquestion
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Visual Question Answering (VQA) is an emerging area of interest for researches, being a recent problem in natural language processing and image prediction. In this area, an algorithm needs to answer questions about certain images. As of the writing of this survey, 25 recent studies were analyzed. Besides, 6 datasets were analyzed and provided their link to download. In this work, several recent pieces of research in this area were investigated and a deeper analysis and comparison among them were provided, including results, the state-of-the-art, common errors, and possible points of improvement for future researchers.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Towards Omnidirectional Reasoning with 360-R1: A Dataset, Benchmark, and GRPO-based Method

    cs.CV 2025-05 conditional novelty 6.0 of 10

    OmniVQA is a first open-source dataset and benchmark for 360-degree visual question answering, and 360-R1 uses GRPO with three LLM-based rewards to improve an existing multimodal model on it.

  2. GC-KBVQA: A New Four-Stage Framework for Enhancing Knowledge Based Visual Question Answering Performance

    cs.CL 2025-05 conditional novelty 5.0 of 10

    A modular zero-shot KB-VQA framework using Grounding DINO, dual captioners, semantic caption filtering, and LLM prompting reports new state-of-the-art numbers on OK-VQA, A-OKVQA, and VQAv2.

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