Transformation-Invariant Network for Few-Shot Object Detection in Remote Sensing Images

• URL: http://arxiv.org/abs/2303.06817v3
• Date: Thu, 16 Nov 2023 08:00:17 GMT
• Title: Transformation-Invariant Network for Few-Shot Object Detection in Remote Sensing Images
• Authors: Nanqing Liu, Xun Xu, Turgay Celik, Zongxin Gan, Heng-Chao Li
• Abstract summary: Few-shot object detection (FSOD) relies on a large amount of labeled data for training. Scale and orientation variations of objects in remote sensing images pose significant challenges to existing FSOD methods. We propose integrating a feature pyramid network and utilizing prototype features to enhance query features.
• Score: 15.251042369061024
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Object detection in remote sensing images relies on a large amount of labeled data for training. However, the increasing number of new categories and class imbalance make exhaustive annotation impractical. Few-shot object detection (FSOD) addresses this issue by leveraging meta-learning on seen base classes and fine-tuning on novel classes with limited labeled samples. Nonetheless, the substantial scale and orientation variations of objects in remote sensing images pose significant challenges to existing few-shot object detection methods. To overcome these challenges, we propose integrating a feature pyramid network and utilizing prototype features to enhance query features, thereby improving existing FSOD methods. We refer to this modified FSOD approach as a Strong Baseline, which has demonstrated significant performance improvements compared to the original baselines. Furthermore, we tackle the issue of spatial misalignment caused by orientation variations between the query and support images by introducing a Transformation-Invariant Network (TINet). TINet ensures geometric invariance and explicitly aligns the features of the query and support branches, resulting in additional performance gains while maintaining the same inference speed as the Strong Baseline. Extensive experiments on three widely used remote sensing object detection datasets, i.e., NWPU VHR-10.v2, DIOR, and HRRSD demonstrated the effectiveness of the proposed method.

Related papers

• Few-shot Oriented Object Detection with Memorable Contrastive Learning in Remote Sensing Images [11.217630579076237]
  Few-shot object detection (FSOD) has garnered significant research attention in the field of remote sensing. We propose a novel FSOD method for remote sensing images called Few-shot Oriented object detection with Memorable Contrastive learning (FOMC) Specifically, we employ oriented bounding boxes instead of traditional horizontal bounding boxes to learn a better feature representation for arbitrary-oriented aerial objects.
  arXiv  Detail & Related papers  (2024-03-20T08:15:18Z)
• VirtualPainting: Addressing Sparsity with Virtual Points and Distance-Aware Data Augmentation for 3D Object Detection [3.5259183508202976]
  We present an innovative approach that involves the generation of virtual LiDAR points using camera images. We also enhance these virtual points with semantic labels obtained from image-based segmentation networks. Our approach offers a versatile solution that can be seamlessly integrated into various 3D frameworks and 2D semantic segmentation methods.
  arXiv  Detail & Related papers  (2023-12-26T18:03:05Z)
• 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)
• Spatial-Temporal Graph Enhanced DETR Towards Multi-Frame 3D Object Detection [54.041049052843604]
  We present STEMD, a novel end-to-end framework that enhances the DETR-like paradigm for multi-frame 3D object detection. First, to model the inter-object spatial interaction and complex temporal dependencies, we introduce the spatial-temporal graph attention network. Finally, it poses a challenge for the network to distinguish between the positive query and other highly similar queries that are not the best match.
  arXiv  Detail & Related papers  (2023-07-01T13:53:14Z)
• Hi-ResNet: Edge Detail Enhancement for High-Resolution Remote Sensing Segmentation [10.919956120261539]
  High-resolution remote sensing (HRS) semantic segmentation extracts key objects from high-resolution coverage areas. objects of the same category within HRS images show significant differences in scale and shape across diverse geographical environments. We propose a High-resolution remote sensing network (Hi-ResNet) with efficient network structure designs.
  arXiv  Detail & Related papers  (2023-05-22T03:58:25Z)
• Geometric-aware Pretraining for Vision-centric 3D Object Detection [77.7979088689944]
  We propose a novel geometric-aware pretraining framework called GAPretrain. GAPretrain serves as a plug-and-play solution that can be flexibly applied to multiple state-of-the-art detectors. We achieve 46.2 mAP and 55.5 NDS on the nuScenes val set using the BEVFormer method, with a gain of 2.7 and 2.1 points, respectively.
  arXiv  Detail & Related papers  (2023-04-06T14:33:05Z)
• Adaptive Rotated Convolution for Rotated Object Detection [96.94590550217718]
  We present Adaptive Rotated Convolution (ARC) module to handle rotated object detection problem. In our ARC module, the convolution kernels rotate adaptively to extract object features with varying orientations in different images. The proposed approach achieves state-of-the-art performance on the DOTA dataset with 81.77% mAP.
  arXiv  Detail & Related papers  (2023-03-14T11:53:12Z)
• Salient Objects in Clutter [130.63976772770368]
  This paper identifies and addresses a serious design bias of existing salient object detection (SOD) datasets. This design bias has led to a saturation in performance for state-of-the-art SOD models when evaluated on existing datasets. We propose a new high-quality dataset and update the previous saliency benchmark.
  arXiv  Detail & Related papers  (2021-05-07T03:49:26Z)
• I^3Net: Implicit Instance-Invariant Network for Adapting One-Stage Object Detectors [64.93963042395976]
  Implicit Instance-Invariant Network (I3Net) is tailored for adapting one-stage detectors. I3Net implicitly learns instance-invariant features via exploiting the natural characteristics of deep features in different layers. Experiments reveal that I3Net exceeds the state-of-the-art performance on benchmark datasets.
  arXiv  Detail & Related papers  (2021-03-25T11:14:36Z)
• CFC-Net: A Critical Feature Capturing Network for Arbitrary-Oriented Object Detection in Remote Sensing Images [0.9462808515258465]
  In this paper, we discuss the role of discriminative features in object detection. We then propose a Critical Feature Capturing Network (CFC-Net) to improve detection accuracy. We show that our method achieves superior detection performance compared with many state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-01-18T02:31:09Z)

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.