MB-ORES: A Multi-Branch Object Reasoner for Visual Grounding in Remote Sensing

• URL: http://arxiv.org/abs/2503.24219v1
• Date: Mon, 31 Mar 2025 15:36:41 GMT
• Title: MB-ORES: A Multi-Branch Object Reasoner for Visual Grounding in Remote Sensing
• Authors: Karim Radouane, Hanane Azzag, Mustapha lebbah,
• Abstract summary: We propose a unified framework that integrates object detection (OD) and visual grounding (VG) for remote sensing (RS) imagery.<n>Our model demonstrates superior performance on the OPT-RSVG and DIOR-RSVG datasets.
• Score: 0.08192907805418585
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a unified framework that integrates object detection (OD) and visual grounding (VG) for remote sensing (RS) imagery. To support conventional OD and establish an intuitive prior for VG task, we fine-tune an open-set object detector using referring expression data, framing it as a partially supervised OD task. In the first stage, we construct a graph representation of each image, comprising object queries, class embeddings, and proposal locations. Then, our task-aware architecture processes this graph to perform the VG task. The model consists of: (i) a multi-branch network that integrates spatial, visual, and categorical features to generate task-aware proposals, and (ii) an object reasoning network that assigns probabilities across proposals, followed by a soft selection mechanism for final referring object localization. Our model demonstrates superior performance on the OPT-RSVG and DIOR-RSVG datasets, achieving significant improvements over state-of-the-art methods while retaining classical OD capabilities. The code will be available in our repository: \url{https://github.com/rd20karim/MB-ORES}.

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