S$^2$Teacher: Step-by-step Teacher for Sparsely Annotated Oriented Object Detection

• URL: http://arxiv.org/abs/2504.11111v1
• Date: Tue, 15 Apr 2025 11:57:00 GMT
• Title: S$^2$Teacher: Step-by-step Teacher for Sparsely Annotated Oriented Object Detection
• Authors: Yu Lin, Jianghang Lin, Kai Ye, You Shen, Yan Zhang, Shengchuan Zhang, Liujuan Cao, Rongrong Ji,
• Abstract summary: We introduce a novel setting called sparsely annotated object detection (SAOOD), which only labels partial instances.<n>Specifically, we focus on two key issues in the setting: (1) sparse labeling leading to overfitting on limited foreground representations, and (2) unlabeled objects (false negatives) confusing feature learning.<n>To this end, we propose the S$2$Teacher, a novel method that progressively mines pseudo-labels for unlabeled objects, from easy to hard, to enhance foreground representations.
• Score: 55.34086214300803
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although fully-supervised oriented object detection has made significant progress in multimodal remote sensing image understanding, it comes at the cost of labor-intensive annotation. Recent studies have explored weakly and semi-supervised learning to alleviate this burden. However, these methods overlook the difficulties posed by dense annotations in complex remote sensing scenes. In this paper, we introduce a novel setting called sparsely annotated oriented object detection (SAOOD), which only labels partial instances, and propose a solution to address its challenges. Specifically, we focus on two key issues in the setting: (1) sparse labeling leading to overfitting on limited foreground representations, and (2) unlabeled objects (false negatives) confusing feature learning. To this end, we propose the S$^2$Teacher, a novel method that progressively mines pseudo-labels for unlabeled objects, from easy to hard, to enhance foreground representations. Additionally, it reweights the loss of unlabeled objects to mitigate their impact during training. Extensive experiments demonstrate that S$^2$Teacher not only significantly improves detector performance across different sparse annotation levels but also achieves near-fully-supervised performance on the DOTA dataset with only 10% annotation instances, effectively balancing detection accuracy with annotation efficiency. The code will be public.

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)
• Towards End-to-End Unsupervised Saliency Detection with Self-Supervised Top-Down Context [25.85453873366275]
  We propose a self-supervised end-to-end salient object detection framework via top-down context. We exploit the self-localization from the deepest feature to construct the location maps which are then leveraged to learn the most instructive segmentation guidance. Our method achieves leading performance among the recent end-to-end methods and most of the multi-stage solutions.
  arXiv  Detail & Related papers  (2023-10-14T08:43:22Z)
• Deep Active Learning with Noisy Oracle in Object Detection [5.5165579223151795]
  We propose a composite active learning framework including a label review module for deep object detection. We show that utilizing part of the annotation budget to correct the noisy annotations partially in the active dataset leads to early improvements in model performance. In our experiments we achieve improvements of up to 4.5 mAP points of object detection performance by incorporating label reviews at equal annotation budget.
  arXiv  Detail & Related papers  (2023-09-30T13:28:35Z)
• Calibrated Teacher for Sparsely Annotated Object Detection [35.74904852812749]
  Fully supervised object detection requires training images in which all instances are annotated. This is actually impractical due to the high labor and time costs and the unavoidable missing annotations. Recent works on sparsely annotated object detection alleviate this problem by generating pseudo labels for the missing annotations. We propose a Calibrated Teacher, of which the confidence estimation of the prediction is well to match its real precision.
  arXiv  Detail & Related papers  (2023-03-14T02:02:39Z)
• Point-Teaching: Weakly Semi-Supervised Object Detection with Point Annotations [81.02347863372364]
  We present Point-Teaching, a weakly semi-supervised object detection framework. Specifically, we propose a Hungarian-based point matching method to generate pseudo labels for point annotated images. We propose a simple-yet-effective data augmentation, termed point-guided copy-paste, to reduce the impact of the unmatched points.
  arXiv  Detail & Related papers  (2022-06-01T07:04:38Z)
• Learning to Detect Instance-level Salient Objects Using Complementary Image Labels [55.049347205603304]
  We present the first weakly-supervised approach to the salient instance detection problem. We propose a novel weakly-supervised network with three branches: a Saliency Detection Branch leveraging class consistency information to locate candidate objects; a Boundary Detection Branch exploiting class discrepancy information to delineate object boundaries; and a Centroid Detection Branch using subitizing information to detect salient instance centroids.
  arXiv  Detail & Related papers  (2021-11-19T10:15:22Z)
• WSSOD: A New Pipeline for Weakly- and Semi-Supervised Object Detection [75.80075054706079]
  We propose a weakly- and semi-supervised object detection framework (WSSOD) An agent detector is first trained on a joint dataset and then used to predict pseudo bounding boxes on weakly-annotated images. The proposed framework demonstrates remarkable performance on PASCAL-VOC and MSCOCO benchmark, achieving a high performance comparable to those obtained in fully-supervised settings.
  arXiv  Detail & Related papers  (2021-05-21T11:58:50Z)
• Instance-aware, Context-focused, and Memory-efficient Weakly Supervised Object Detection [184.563345153682]
  We develop an instance-aware and context-focused unified framework for weakly supervised learning. It employs an instance-aware self-training algorithm and a learnable Concrete DropBlock while devising a memory-efficient sequential batch back-propagation. Our proposed method state-of-the-art results on COCO ($12.1% AP$, $24.8% AP_50$), VOC 2007 ($54.9% AP$), and VOC 2012 ($52.1% AP$)
  arXiv  Detail & Related papers  (2020-04-09T17:57:09Z)
• Weakly-Supervised Salient Object Detection via Scribble Annotations [54.40518383782725]
  We propose a weakly-supervised salient object detection model to learn saliency from scribble labels. We present a new metric, termed saliency structure measure, to measure the structure alignment of the predicted saliency maps. Our method not only outperforms existing weakly-supervised/unsupervised methods, but also is on par with several fully-supervised state-of-the-art models.
  arXiv  Detail & Related papers  (2020-03-17T12:59:50Z)

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.