LEDetection: A Simple Framework for Semi-Supervised Few-Shot Object Detection

• URL: http://arxiv.org/abs/2303.05739v3
• Date: Wed, 14 Feb 2024 10:22:25 GMT
• Title: LEDetection: A Simple Framework for Semi-Supervised Few-Shot Object Detection
• Authors: Phi Vu Tran
• Abstract summary: This paper studies the new task of semi-supervised FSOD by considering a realistic scenario in which both base and novel labels are simultaneously scarce. We introduce SoftER Teacher, a robust detector combining pseudo-labeling with consistency learning on region proposals. Rigorous experiments show that SoftER Teacher surpasses the novel performance of a strong supervised detector using only 10% of required base labels.
• Score: 4.3512163406552
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Few-shot object detection (FSOD) is a challenging problem aimed at detecting novel concepts from few exemplars. Existing approaches to FSOD all assume abundant base labels to adapt to novel objects. This paper studies the new task of semi-supervised FSOD by considering a realistic scenario in which both base and novel labels are simultaneously scarce. We explore the utility of unlabeled data within our proposed label-efficient detection framework and discover its remarkable ability to boost semi-supervised FSOD by way of region proposals. Motivated by this finding, we introduce SoftER Teacher, a robust detector combining pseudo-labeling with consistency learning on region proposals, to harness unlabeled data for improved FSOD without relying on abundant labels. Rigorous experiments show that SoftER Teacher surpasses the novel performance of a strong supervised detector using only 10% of required base labels, without catastrophic forgetting observed in prior approaches. Our work also sheds light on a potential relationship between semi-supervised and few-shot detection suggesting that a stronger semi-supervised detector leads to a more effective few-shot detector.

Related papers

• Exploiting Unlabeled Data with Multiple Expert Teachers for Open Vocabulary Aerial Object Detection and Its Orientation Adaptation [58.37525311718006]
  We put forth a novel formulation of the aerial object detection problem, namely open-vocabulary aerial object detection (OVAD) We propose CastDet, a CLIP-activated student-teacher detection framework that serves as the first OVAD detector specifically designed for the challenging aerial scenario. Our framework integrates a robust localization teacher along with several box selection strategies to generate high-quality proposals for novel objects.
  arXiv  Detail & Related papers  (2024-11-04T12:59:13Z)
• Revisiting Class Imbalance for End-to-end Semi-Supervised Object Detection [1.6249267147413524]
  Semi-supervised object detection (SSOD) has made significant progress with the development of pseudo-label-based end-to-end methods. Many methods face challenges due to class imbalance, which hinders the effectiveness of the pseudo-label generator. In this paper, we examine the root causes of low-quality pseudo-labels and present novel learning mechanisms to improve the label generation quality.
  arXiv  Detail & Related papers  (2023-06-04T06:01:53Z)
• 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)
• A Weakly Supervised Learning Framework for Salient Object Detection via Hybrid Labels [96.56299163691979]
  This paper focuses on a new weakly-supervised salient object detection (SOD) task under hybrid labels. To address the issues of label noise and quantity imbalance in this task, we design a new pipeline framework with three sophisticated training strategies. Experiments on five SOD benchmarks show that our method achieves competitive performance against weakly-supervised/unsupervised methods.
  arXiv  Detail & Related papers  (2022-09-07T06:45:39Z)
• Open-Set Semi-Supervised Object Detection [43.464223594166654]
  Recent developments for Semi-Supervised Object Detection (SSOD) have shown the promise of leveraging unlabeled data to improve an object detector. We consider a more practical yet challenging problem, Open-Set Semi-Supervised Object Detection (OSSOD) Our proposed framework effectively addresses the semantic expansion issue and shows consistent improvements on many OSSOD benchmarks.
  arXiv  Detail & Related papers  (2022-08-29T17:04:30Z)
• 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)
• Unsupervised Domain Adaptive Salient Object Detection Through Uncertainty-Aware Pseudo-Label Learning [104.00026716576546]
  We propose to learn saliency from synthetic but clean labels, which naturally has higher pixel-labeling quality without the effort of manual annotations. We show that our proposed method outperforms the existing state-of-the-art deep unsupervised SOD methods on several benchmark datasets.
  arXiv  Detail & Related papers  (2022-02-26T16:03:55Z)
• 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)
• EHSOD: CAM-Guided End-to-end Hybrid-Supervised Object Detection with Cascade Refinement [53.69674636044927]
  We present EHSOD, an end-to-end hybrid-supervised object detection system. It can be trained in one shot on both fully and weakly-annotated data. It achieves comparable results on multiple object detection benchmarks with only 30% fully-annotated data.
  arXiv  Detail & Related papers  (2020-02-18T08:04:58Z)

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.