LOGOS: Language-guided Oriented Object Detection in Aerial Scenes

• URL: http://arxiv.org/abs/2607.08004v1
• Date: Thu, 09 Jul 2026 00:11:48 GMT
• Title: LOGOS: Language-guided Oriented Object Detection in Aerial Scenes
• Abstract summary: In this paper, we propose LOGOS, a novel transformer-based approach that leverages textual prompts to guide the detection of oriented objects in aerial scenes.<n>In particular, our proposed approach incorporates prompt-modulated content queries to dynamically adjust the model's focus based on the provided text.<n>Our approach offers a significant step forward in improving the robustness and scalability of oriented object detection in remote sensing applications.
• Score: 13.035539845858501
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Object detection in geospatial scenes, such as satellite and aerial imagery, poses significant challenges due to the varying orientations and densities of objects, as well as the complex backgrounds inherent to remote sensing imagery. Traditional methods for oriented object detection have struggled to address issues such as angular discontinuity, fixed query sizes, and inefficiencies in handling sparse or cluttered scenes. In this paper, we propose LOGOS, a novel transformer-based approach that leverages textual prompts to guide the detection of oriented objects in aerial scenes. In particular, our proposed approach incorporates prompt-modulated content queries to dynamically adjust the model's focus based on the provided text, thereby improving object detection accuracy in complex environments. Empirically, extensive experiments on the DOTA dataset demonstrate that LOGOS outperforms existing state-of-the-art methods, particularly in densely packed and rotated object scenarios. Our approach offers a significant step forward in improving the robustness and scalability of oriented object detection in remote sensing applications.

Related papers

• SDDF: Specificity-Driven Dynamic Focusing for Open-Vocabulary Camouflaged Object Detection [12.404050551208366]
  Open-vocabulary object detection (OVOD) aims to detect known and unknown objects in the open world by leveraging text prompts.<n>We construct a benchmark named OVCOD-D by augmenting carefully selected camouflaged object images with fine-grained textual descriptions.<n>Under the open-set evaluation setting, the proposed method achieves an AP of 56.4 on the OVCOD-D benchmark.
  arXiv  Detail & Related papers  (2026-03-27T06:37:32Z)
• Enhancing Rotated Object Detection via Anisotropic Gaussian Bounding Box and Bhattacharyya Distance [0.9786690381850356]
  This paper introduces an improved loss function aimed at enhancing detection accuracy and robustness.<n>We advocate for the use of an anisotropic Gaussian representation to address the issues associated with isotropic variance in square-like objects.<n>Our proposed method addresses these challenges by incorporating a rotation-invariant loss function that effectively captures the geometric properties of rotated objects.
  arXiv  Detail & Related papers  (2025-10-18T10:42:30Z)
• A Multimodal Depth-Aware Method For Embodied Reference Understanding [56.30142869506262]
  Embodied Reference Understanding requires identifying a target object in a visual scene based on both language instructions and pointing cues.<n>We propose a novel ERU framework that jointly leverages data augmentation, depth-map modality, and a depth-aware decision module.
  arXiv  Detail & Related papers  (2025-10-09T14:32:21Z)
• Oriented Tiny Object Detection: A Dataset, Benchmark, and Dynamic Unbiased Learning [51.170479006249195]
  We introduce a new dataset, benchmark, and a dynamic coarse-to-fine learning scheme in this study.<n>Our proposed dataset, AI-TOD-R, features the smallest object sizes among all oriented object detection datasets.<n>We present a benchmark spanning a broad range of detection paradigms, including both fully-supervised and label-efficient approaches.
  arXiv  Detail & Related papers  (2024-12-16T09:14:32Z)
• Detect2Interact: Localizing Object Key Field in Visual Question Answering (VQA) with LLMs [5.891295920078768]
  We introduce an advanced approach for fine-grained object visual key field detection. First, we use the segment anything model (SAM) to generate detailed spatial maps of objects in images. Next, we use Vision Studio to extract semantic object descriptions. Third, we employ GPT-4's common sense knowledge, bridging the gap between an object's semantics and its spatial map.
  arXiv  Detail & Related papers  (2024-04-01T14:53:36Z)
• Innovative Horizons in Aerial Imagery: LSKNet Meets DiffusionDet for Advanced Object Detection [55.2480439325792]
  We present an in-depth evaluation of an object detection model that integrates the LSKNet backbone with the DiffusionDet head. The proposed model achieves a mean average precision (MAP) of approximately 45.7%, which is a significant improvement. This advancement underscores the effectiveness of the proposed modifications and sets a new benchmark in aerial image analysis.
  arXiv  Detail & Related papers  (2023-11-21T19:49:13Z)
• Oriented object detection in optical remote sensing images using deep learning: a survey [13.05317817164702]
  Oriented object detection is a fundamental yet challenging task in remote sensing.<n>Recent advancements in deep learning have significantly enhanced the capabilities of oriented object detection methods.
  arXiv  Detail & Related papers  (2023-02-21T06:31:53Z)
• High-resolution Iterative Feedback Network for Camouflaged Object Detection [128.893782016078]
  Spotting camouflaged objects that are visually assimilated into the background is tricky for object detection algorithms. We aim to extract the high-resolution texture details to avoid the detail degradation that causes blurred vision in edges and boundaries. We introduce a novel HitNet to refine the low-resolution representations by high-resolution features in an iterative feedback manner.
  arXiv  Detail & Related papers  (2022-03-22T11:20:21Z)
• Robust Object Detection via Instance-Level Temporal Cycle Confusion [89.1027433760578]
  We study the effectiveness of auxiliary self-supervised tasks to improve the out-of-distribution generalization of object detectors. Inspired by the principle of maximum entropy, we introduce a novel self-supervised task, instance-level temporal cycle confusion (CycConf) For each object, the task is to find the most different object proposals in the adjacent frame in a video and then cycle back to itself for self-supervision.
  arXiv  Detail & Related papers  (2021-04-16T21:35:08Z)
• Slender Object Detection: Diagnoses and Improvements [74.40792217534]
  In this paper, we are concerned with the detection of a particular type of objects with extreme aspect ratios, namely textbfslender objects. For a classical object detection method, a drastic drop of $18.9%$ mAP on COCO is observed, if solely evaluated on slender objects.
  arXiv  Detail & Related papers  (2020-11-17T09:39:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.