Few-shot Object Detection in Remote Sensing: Lifting the Curse of Incompletely Annotated Novel Objects

• URL: http://arxiv.org/abs/2309.10588v1
• Date: Tue, 19 Sep 2023 13:00:25 GMT
• Title: Few-shot Object Detection in Remote Sensing: Lifting the Curse of Incompletely Annotated Novel Objects
• Authors: Fahong Zhang, Yilei Shi, Zhitong Xiong, and Xiao Xiang Zhu
• Abstract summary: We propose a self-training-based FSOD (ST-FSOD) approach to object detection. Our proposed method outperforms the state-of-the-art in various FSOD settings by a large margin.
• Score: 23.171410277239534
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Object detection is an essential and fundamental task in computer vision and satellite image processing. Existing deep learning methods have achieved impressive performance thanks to the availability of large-scale annotated datasets. Yet, in real-world applications the availability of labels is limited. In this context, few-shot object detection (FSOD) has emerged as a promising direction, which aims at enabling the model to detect novel objects with only few of them annotated. However, many existing FSOD algorithms overlook a critical issue: when an input image contains multiple novel objects and only a subset of them are annotated, the unlabeled objects will be considered as background during training. This can cause confusions and severely impact the model's ability to recall novel objects. To address this issue, we propose a self-training-based FSOD (ST-FSOD) approach, which incorporates the self-training mechanism into the few-shot fine-tuning process. ST-FSOD aims to enable the discovery of novel objects that are not annotated, and take them into account during training. On the one hand, we devise a two-branch region proposal networks (RPN) to separate the proposal extraction of base and novel objects, On another hand, we incorporate the student-teacher mechanism into RPN and the region of interest (RoI) head to include those highly confident yet unlabeled targets as pseudo labels. Experimental results demonstrate that our proposed method outperforms the state-of-the-art in various FSOD settings by a large margin. The codes will be publicly available at https://github.com/zhu-xlab/ST-FSOD.

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)
• ECEA: Extensible Co-Existing Attention for Few-Shot Object Detection [52.16237548064387]
  Few-shot object detection (FSOD) identifies objects from extremely few annotated samples. Most existing FSOD methods, recently, apply the two-stage learning paradigm, which transfers the knowledge learned from abundant base classes to assist the few-shot detectors by learning the global features. We propose an Extensible Co-Existing Attention (ECEA) module to enable the model to infer the global object according to the local parts.
  arXiv  Detail & Related papers  (2023-09-15T06:55:43Z)
• Improved Region Proposal Network for Enhanced Few-Shot Object Detection [23.871860648919593]
  Few-shot object detection (FSOD) methods have emerged as a solution to the limitations of classic object detection approaches. We develop a semi-supervised algorithm to detect and then utilize unlabeled novel objects as positive samples during the FSOD training stage. Our improved hierarchical sampling strategy for the region proposal network (RPN) also boosts the perception of the object detection model for large objects.
  arXiv  Detail & Related papers  (2023-08-15T02:35:59Z)
• Identification of Novel Classes for Improving Few-Shot Object Detection [12.013345715187285]
  Few-shot object detection (FSOD) methods offer a remedy by realizing robust object detection using only a few training samples per class. We develop a semi-supervised algorithm to detect and then utilize unlabeled novel objects as positive samples during training to improve FSOD performance. Our experimental results indicate that our method is effective and outperforms the existing state-of-the-art (SOTA) FSOD methods.
  arXiv  Detail & Related papers  (2023-03-18T14:12:52Z)
• Open-Set Object Detection Using Classification-free Object Proposal and Instance-level Contrastive Learning [25.935629339091697]
  Open-set object detection (OSOD) is a promising direction to handle the problem consisting of two subtasks: objects and background separation, and open-set object classification. We present Openset RCNN to address the challenging OSOD. We show that our Openset RCNN can endow the robot with an open-set perception ability to support robotic rearrangement tasks in cluttered environments.
  arXiv  Detail & Related papers  (2022-11-21T15:00:04Z)
• Incremental-DETR: Incremental Few-Shot Object Detection via Self-Supervised Learning [60.64535309016623]
  We propose the Incremental-DETR that does incremental few-shot object detection via fine-tuning and self-supervised learning on the DETR object detector. To alleviate severe over-fitting with few novel class data, we first fine-tune the class-specific components of DETR with self-supervision. We further introduce a incremental few-shot fine-tuning strategy with knowledge distillation on the class-specific components of DETR to encourage the network in detecting novel classes without catastrophic forgetting.
  arXiv  Detail & Related papers  (2022-05-09T05:08:08Z)
• Learning Open-World Object Proposals without Learning to Classify [110.30191531975804]
  We propose a classification-free Object Localization Network (OLN) which estimates the objectness of each region purely by how well the location and shape of a region overlaps with any ground-truth object. This simple strategy learns generalizable objectness and outperforms existing proposals on cross-category generalization.
  arXiv  Detail & Related papers  (2021-08-15T14:36:02Z)
• Few-shot Weakly-Supervised Object Detection via Directional Statistics [55.97230224399744]
  We propose a probabilistic multiple instance learning approach for few-shot Common Object Localization (COL) and few-shot Weakly Supervised Object Detection (WSOD) Our model simultaneously learns the distribution of the novel objects and localizes them via expectation-maximization steps. Our experiments show that the proposed method, despite being simple, outperforms strong baselines in few-shot COL and WSOD, as well as large-scale WSOD tasks.
  arXiv  Detail & Related papers  (2021-03-25T22:34:16Z)
• Exploring Bottom-up and Top-down Cues with Attentive Learning for Webly Supervised Object Detection [76.9756607002489]
  We propose a novel webly supervised object detection (WebSOD) method for novel classes. Our proposed method combines bottom-up and top-down cues for novel class detection. We demonstrate our proposed method on PASCAL VOC dataset with three different novel/base splits.
  arXiv  Detail & Related papers  (2020-03-22T03:11:24Z)
• Progressive Object Transfer Detection [84.48927705173494]
  We propose a novel Progressive Object Transfer Detection (POTD) framework. First, POTD can leverage various object supervision of different domains effectively into a progressive detection procedure. Second, POTD consists of two delicate transfer stages, i.e., Low-Shot Transfer Detection (LSTD), and Weakly-Supervised Transfer Detection (WSTD)
  arXiv  Detail & Related papers  (2020-02-12T00:16:24Z)

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.