title: MB-ORES: A Multi-Branch Object Reasoner for Visual Grounding in Remote
    Sensing
summary: 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: https://github.com/rd20karim/MB-ORES.
paper_authors._id: 67ed2033d170f0444d5d3b64
paper_authors.name: Karim Radouane
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-02T11:54:20.276Z
paper_authors._id: 665a137c8528df6b00414572
paper_authors.avatarUrl: /avatars/c7b5b2a9baf634a5279f9f1d24f70c23.svg
paper_authors.fullname: Karim Radouane
paper_authors.type: user
paper_authors.user: rdkarim
paper_authors._id: 67ed2033d170f0444d5d3b65
paper_authors.name: Hanane Azzag
paper_authors._id: 67ed2033d170f0444d5d3b66
paper_authors.name: Mustapha lebbah
paper_title: MB-ORES: A Multi-Branch Object Reasoner for Visual Grounding in Remote
    Sensing
paper_summary: 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: https://github.com/rd20karim/MB-ORES.
paper_ai_summary: A unified framework combining object detection and visual grounding in remote sensing uses a graph-based approach with multi-branch and object reasoning networks, achieving superior performance on specific datasets.
paper_ai_keywords: object detection visual grounding remote sensing referring expression partially supervised graph representation multi-branch network object reasoning network OPT-RSVG DIOR-RSVG