Cyclic-Bootstrap Labeling for Weakly Supervised Object Detection
- URL: http://arxiv.org/abs/2308.05991v1
- Date: Fri, 11 Aug 2023 07:57:17 GMT
- Title: Cyclic-Bootstrap Labeling for Weakly Supervised Object Detection
- Authors: Yufei Yin, Jiajun Deng, Wengang Zhou, Li Li, Houqiang Li
- Abstract summary: Cyclic-Bootstrap Labeling (CBL) is a novel weakly supervised object detection pipeline.
Uses a weighted exponential moving average strategy to take advantage of various refinement modules.
A novel class-specific ranking distillation algorithm is proposed to leverage the output of weighted ensembled teacher network.
- Score: 134.05510658882278
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Recent progress in weakly supervised object detection is featured by a
combination of multiple instance detection networks (MIDN) and ordinal online
refinement. However, with only image-level annotation, MIDN inevitably assigns
high scores to some unexpected region proposals when generating pseudo labels.
These inaccurate high-scoring region proposals will mislead the training of
subsequent refinement modules and thus hamper the detection performance. In
this work, we explore how to ameliorate the quality of pseudo-labeling in MIDN.
Formally, we devise Cyclic-Bootstrap Labeling (CBL), a novel weakly supervised
object detection pipeline, which optimizes MIDN with rank information from a
reliable teacher network. Specifically, we obtain this teacher network by
introducing a weighted exponential moving average strategy to take advantage of
various refinement modules. A novel class-specific ranking distillation
algorithm is proposed to leverage the output of weighted ensembled teacher
network for distilling MIDN with rank information. As a result, MIDN is guided
to assign higher scores to accurate proposals among their neighboring ones,
thus benefiting the subsequent pseudo labeling. Extensive experiments on the
prevalent PASCAL VOC 2007 \& 2012 and COCO datasets demonstrate the superior
performance of our CBL framework. Code will be available at
https://github.com/Yinyf0804/WSOD-CBL/.
Related papers
- Rank-DETR for High Quality Object Detection [52.82810762221516]
A highly performant object detector requires accurate ranking for the bounding box predictions.
In this work, we introduce a simple and highly performant DETR-based object detector by proposing a series of rank-oriented designs.
arXiv Detail & Related papers (2023-10-13T04:48:32Z) - Nearest Neighbor Guidance for Out-of-Distribution Detection [18.851275688720108]
We propose Nearest Neighbor Guidance (NNGuide) for detecting out-of-distribution (OOD) samples.
NNGuide reduces the overconfidence of OOD samples while preserving the fine-grained capability of the classifier-based score.
Our results demonstrate that NNGuide provides a significant performance improvement on the base detection scores.
arXiv Detail & Related papers (2023-09-26T12:40:35Z) - 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) - Active Teacher for Semi-Supervised Object Detection [80.10937030195228]
We propose a novel algorithm called Active Teacher for semi-supervised object detection (SSOD)
Active Teacher extends the teacher-student framework to an iterative version, where the label set is partially and gradually augmented by evaluating three key factors of unlabeled examples.
With this design, Active Teacher can maximize the effect of limited label information while improving the quality of pseudo-labels.
arXiv Detail & Related papers (2023-03-15T03:59:27Z) - Improving Localization for Semi-Supervised Object Detection [3.5493798890908104]
We introduce an additional classification task for bounding box localization to improve the filtering of predicted bounding boxes.
Our experiments show that our IL-net increases SSOD performance by 1.14% AP on dataset in limited-annotation regime.
arXiv Detail & Related papers (2022-06-21T08:39:38Z) - 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) - COLAM: Co-Learning of Deep Neural Networks and Soft Labels via
Alternating Minimization [60.07531696857743]
Co-Learns DNNs and soft labels through Alternating Minimization of two objectives.
We propose COLAM framework that Co-Learns DNNs and soft labels through Alternating Minimization of two objectives.
arXiv Detail & Related papers (2020-04-26T17:50:20Z)
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.