MB-ORES: A Multi-Branch Object Reasoner for Visual Grounding in Remote
  Sensing
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.
67ed2033d170f0444d5d3b64
Karim Radouane
claimed_verified
2025-04-02T11:54:20.276Z
665a137c8528df6b00414572
/avatars/c7b5b2a9baf634a5279f9f1d24f70c23.svg
Karim Radouane
user
rdkarim
67ed2033d170f0444d5d3b65
Hanane Azzag
67ed2033d170f0444d5d3b66
Mustapha lebbah
MB-ORES: A Multi-Branch Object Reasoner for Visual Grounding in Remote
  Sensing
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.
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.
object detection visual grounding remote sensing referring expression partially supervised graph representation multi-branch network object reasoning network OPT-RSVG DIOR-RSVG