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In response to requests for a comprehensive Web 3.0 visual question-answering system, this paper presents a strategic framework. This framework is more coherent than Web 2.0. It converges document and image collections. These datasets build a term pool using category-label extraction or TF-IDF. Latent semantic indexing, metadata generation, and deep learning classification using a resilient GRU model aggregate supplementary knowledge. The YAGO Knowledge Store Repository preprocesses and improves queries. Wikidata API helps enrich intermediate auxiliary knowledge. This strengthens the semantic network and lays the groundwork for semantic computations. The Bose-Einstein index and SoCPMI quantify semantic similarity. Integrating an imperialistic competitive algorithm accelerates convergence to the best solution by computing intermediate optimal solutions. Impressively, the suggested framework achieves 95.81% precision, 96.07% false discovery, and 0.05% false discovery. These results strengthen the framework’s position as a top visual question-answering solution, exceeding baseline models.

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InCVQA: Incremental Knowledge Derivation Scheme for Visual Question Answering Using Gated Recurrent Units

  • 会议论文

  • 发表日期：

    2025年

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  • Anamaya Vyas,
  • Gerard Deepak

摘要

In response to requests for a comprehensive Web 3.0 visual question-answering system, this paper presents a strategic framework. This framework is more coherent than Web 2.0. It converges document and image collections. These datasets build a term pool using category-label extraction or TF-IDF. Latent semantic indexing, metadata generation, and deep learning classification using a resilient GRU model aggregate supplementary knowledge. The YAGO Knowledge Store Repository preprocesses and improves queries. Wikidata API helps enrich intermediate auxiliary knowledge. This strengthens the semantic network and lays the groundwork for semantic computations. The Bose-Einstein index and SoCPMI quantify semantic similarity. Integrating an imperialistic competitive algorithm accelerates convergence to the best solution by computing intermediate optimal solutions. Impressively, the suggested framework achieves 95.81% precision, 96.07% false discovery, and 0.05% false discovery. These results strengthen the framework’s position as a top visual question-answering solution, exceeding baseline models.