Martins, Allan de MedeirosSilva Júnior, Silvan Ferreira da2025-08-222025-08-222025-06-13SILVA JÚNIOR, Silvan Ferreira da. Integrating textual queries with aI-based object detection: a compositional prompt-guided approach. Orientador: Dr. Allan de Medeiros Martins. 2025. 73f. Tese (Doutorado em Engenharia Elétrica e de Computação) - Centro de Tecnologia, Universidade Federal do Rio Grande do Norte, Natal, 2025.https://repositorio.ufrn.br/handle/123456789/65285In the field of computer vision, object detection and recognition play a central role in many applications that support automated decision-making. Over recent years, new algorithms and methodologies have emerged to further enhance the automatic identification of target objects. In particular, the rise of deep learning and language models has opened many possibilities in this area, although challenges in contextual query analysis and human interactions persist. This thesis presents a novel neuro-symbolic object detection framework that aligns object proposals with textual prompts using a deep learning module while enabling logical reasoning through a symbolic module. By integrating deep learning with symbolic reasoning, object detection and scene understanding are considerably enhanced, enabling complex, query-driven interactions. Using a synthetic 3D image dataset, the results demonstrate that this framework effectively generalizes to complex queries, combining simple attribute-based descriptions without explicit training on compound prompts. We present the numerical results and comprehensive discussions, highlighting the potential of our approach for emerging smart applications.enAcesso AbertoNeuro-symbolic AIPrompt-guided object detectionCrossmodal reasoningVisual-language alignmentdoctoralThesis