MixTeacher: Mining Promising Labels with Mixed Scale Teacher for
Semi-Supervised Object Detection
- URL: http://arxiv.org/abs/2303.09061v1
- Date: Thu, 16 Mar 2023 03:37:54 GMT
- Title: MixTeacher: Mining Promising Labels with Mixed Scale Teacher for
Semi-Supervised Object Detection
- Authors: Liang Liu, Boshen Zhang, Jiangning Zhang, Wuhao Zhang, Zhenye Gan,
Guanzhong Tian, Wenbing Zhu, Yabiao Wang, Chengjie Wang
- Abstract summary: Scale variation across object instances remains a key challenge in object detection task.
We propose a novel framework that addresses the scale variation problem by introducing a mixed scale teacher.
Our experiments on MS COCO and PASCAL VOC benchmarks under various semi-supervised settings demonstrate that our method achieves new state-of-the-art performance.
- Score: 22.047246997864143
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Scale variation across object instances remains a key challenge in object
detection task. Despite the remarkable progress made by modern detection
models, this challenge is particularly evident in the semi-supervised case.
While existing semi-supervised object detection methods rely on strict
conditions to filter high-quality pseudo labels from network predictions, we
observe that objects with extreme scale tend to have low confidence, resulting
in a lack of positive supervision for these objects. In this paper, we propose
a novel framework that addresses the scale variation problem by introducing a
mixed scale teacher to improve pseudo label generation and scale-invariant
learning. Additionally, we propose mining pseudo labels using score promotion
of predictions across scales, which benefits from better predictions from mixed
scale features. Our extensive experiments on MS COCO and PASCAL VOC benchmarks
under various semi-supervised settings demonstrate that our method achieves new
state-of-the-art performance. The code and models are available at
\url{https://github.com/lliuz/MixTeacher}.
Related papers
- Virtual Category Learning: A Semi-Supervised Learning Method for Dense
Prediction with Extremely Limited Labels [63.16824565919966]
This paper proposes to use confusing samples proactively without label correction.
A Virtual Category (VC) is assigned to each confusing sample in such a way that it can safely contribute to the model optimisation.
Our intriguing findings highlight the usage of VC learning in dense vision tasks.
arXiv Detail & Related papers (2023-12-02T16:23:52Z) - Towards Few-Annotation Learning for Object Detection: Are
Transformer-based Models More Efficient ? [11.416621957617334]
In this paper, we propose a semi-supervised method tailored for the current state-of-the-art object detector Deformable DETR.
We evaluate our method on the semi-supervised object detection benchmarks COCO and Pascal VOC, and it outperforms previous methods, especially when annotations are scarce.
arXiv Detail & Related papers (2023-10-30T18:51:25Z) - Semi-Supervised Learning for hyperspectral images by non parametrically
predicting view assignment [25.198550162904713]
Hyperspectral image (HSI) classification is gaining a lot of momentum in present time because of high inherent spectral information within the images.
Recently, to effectively train the deep learning models with minimal labelled samples, the unlabeled samples are also being leveraged in self-supervised and semi-supervised setting.
In this work, we leverage the idea of semi-supervised learning to assist the discriminative self-supervised pretraining of the models.
arXiv Detail & Related papers (2023-06-19T14:13:56Z) - 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) - Label Matching Semi-Supervised Object Detection [85.99282969977541]
Semi-supervised object detection has made significant progress with the development of mean teacher driven self-training.
Label mismatch problem is not yet fully explored in the previous works, leading to severe confirmation bias during self-training.
We propose a simple yet effective LabelMatch framework from two different yet complementary perspectives.
arXiv Detail & Related papers (2022-06-14T05:59:41Z) - Deep Semi-supervised Knowledge Distillation for Overlapping Cervical
Cell Instance Segmentation [54.49894381464853]
We propose to leverage both labeled and unlabeled data for instance segmentation with improved accuracy by knowledge distillation.
We propose a novel Mask-guided Mean Teacher framework with Perturbation-sensitive Sample Mining.
Experiments show that the proposed method improves the performance significantly compared with the supervised method learned from labeled data only.
arXiv Detail & Related papers (2020-07-21T13:27:09Z) - Multi-Scale Positive Sample Refinement for Few-Shot Object Detection [61.60255654558682]
Few-shot object detection (FSOD) helps detectors adapt to unseen classes with few training instances.
We propose a Multi-scale Positive Sample Refinement (MPSR) approach to enrich object scales in FSOD.
MPSR generates multi-scale positive samples as object pyramids and refines the prediction at various scales.
arXiv Detail & Related papers (2020-07-18T09:48:29Z) - Multi-scale Interactive Network for Salient Object Detection [91.43066633305662]
We propose the aggregate interaction modules to integrate the features from adjacent levels.
To obtain more efficient multi-scale features, the self-interaction modules are embedded in each decoder unit.
Experimental results on five benchmark datasets demonstrate that the proposed method without any post-processing performs favorably against 23 state-of-the-art approaches.
arXiv Detail & Related papers (2020-07-17T15:41:37Z) - UniT: Unified Knowledge Transfer for Any-shot Object Detection and
Segmentation [52.487469544343305]
Methods for object detection and segmentation rely on large scale instance-level annotations for training.
We propose an intuitive and unified semi-supervised model that is applicable to a range of supervision.
arXiv Detail & Related papers (2020-06-12T22:45:47Z)
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.