Few-shot Object Detection on Remote Sensing Images

• URL: http://arxiv.org/abs/2006.07826v2
• Date: Tue, 16 Jun 2020 03:55:42 GMT
• Title: Few-shot Object Detection on Remote Sensing Images
• Authors: Jingyu Deng, Xiang Li, Yi Fang
• Abstract summary: We introduce a few-shot learning-based method for object detection on remote sensing images. We build our few-shot object detection model upon YOLOv3 architecture and develop a multi-scale object detection framework.
• Score: 11.40135025181393
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we deal with the problem of object detection on remote sensing images. Previous methods have developed numerous deep CNN-based methods for object detection on remote sensing images and the report remarkable achievements in detection performance and efficiency. However, current CNN-based methods mostly require a large number of annotated samples to train deep neural networks and tend to have limited generalization abilities for unseen object categories. In this paper, we introduce a few-shot learning-based method for object detection on remote sensing images where only a few annotated samples are provided for the unseen object categories. More specifically, our model contains three main components: a meta feature extractor that learns to extract feature representations from input images, a reweighting module that learn to adaptively assign different weights for each feature representation from the support images, and a bounding box prediction module that carries out object detection on the reweighted feature maps. We build our few-shot object detection model upon YOLOv3 architecture and develop a multi-scale object detection framework. Experiments on two benchmark datasets demonstrate that with only a few annotated samples our model can still achieve a satisfying detection performance on remote sensing images and the performance of our model is significantly better than the well-established baseline models.

Related papers

• Few-shot target-driven instance detection based on open-vocabulary object detection models [1.0749601922718608]
  Open-vocabulary object detection models bring closer visual and textual concepts in the same latent space. We propose a lightweight method to turn the latter into a one-shot or few-shot object recognition models without requiring textual descriptions.
  arXiv  Detail & Related papers  (2024-10-21T14:03:15Z)
• 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)
• Exploring Robust Features for Few-Shot Object Detection in Satellite Imagery [17.156864650143678]
  We develop a few-shot object detector based on a traditional two-stage architecture. A large-scale pre-trained model is used to build class-reference embeddings or prototypes. We perform evaluations on two remote sensing datasets containing challenging and rare objects.
  arXiv  Detail & Related papers  (2024-03-08T15:20:27Z)
• 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)
• A Simple Baseline for Multi-Camera 3D Object Detection [94.63944826540491]
  3D object detection with surrounding cameras has been a promising direction for autonomous driving. We present SimMOD, a Simple baseline for Multi-camera Object Detection. We conduct extensive experiments on the 3D object detection benchmark of nuScenes to demonstrate the effectiveness of SimMOD.
  arXiv  Detail & Related papers  (2022-08-22T03:38:01Z)
• Multi-patch Feature Pyramid Network for Weakly Supervised Object Detection in Optical Remote Sensing Images [39.25541709228373]
  We propose a new architecture for object detection with a multiple patch feature pyramid network (MPFP-Net) MPFP-Net is different from the current models that during training only pursue the most discriminative patches. We introduce an effective method to regularize the residual values and make the fusion transition layers strictly norm-preserving.
  arXiv  Detail & Related papers  (2021-08-18T09:25:39Z)
• Aug3D-RPN: Improving Monocular 3D Object Detection by Synthetic Images with Virtual Depth [64.29043589521308]
  We propose a rendering module to augment the training data by synthesizing images with virtual-depths. The rendering module takes as input the RGB image and its corresponding sparse depth image, outputs a variety of photo-realistic synthetic images. Besides, we introduce an auxiliary module to improve the detection model by jointly optimizing it through a depth estimation task.
  arXiv  Detail & Related papers  (2021-07-28T11:00:47Z)
• You Better Look Twice: a new perspective for designing accurate detectors with reduced computations [56.34005280792013]
  BLT-net is a new low-computation two-stage object detection architecture. It reduces computations by separating objects from background using a very lite first-stage. Resulting image proposals are then processed in the second-stage by a highly accurate model.
  arXiv  Detail & Related papers  (2021-07-21T12:39:51Z)
• 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)
• Few-shot Object Detection with Feature Attention Highlight Module in Remote Sensing Images [10.92844145381214]
  We propose a few-shot object detector which is designed for detecting novel objects based on only a few examples. Our model is composed of a feature-extractor, a feature attention highlight module as well as a two-stage detection backend. Experiments demonstrate the effectiveness of the proposed method for few-shot cases.
  arXiv  Detail & Related papers  (2020-09-03T12:38:49Z)
• One-Shot Object Detection without Fine-Tuning [62.39210447209698]
  We introduce a two-stage model consisting of a first stage Matching-FCOS network and a second stage Structure-Aware Relation Module. We also propose novel training strategies that effectively improve detection performance. Our method exceeds the state-of-the-art one-shot performance consistently on multiple datasets.
  arXiv  Detail & Related papers  (2020-05-08T01:59:23Z)

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.