Mutual Supervision for Dense Object Detection

• URL: http://arxiv.org/abs/2109.05986v1
• Date: Mon, 13 Sep 2021 14:04:13 GMT
• Title: Mutual Supervision for Dense Object Detection
• Authors: Ziteng Gao, Limin Wang, Gangshan Wu
• Abstract summary: We propose a novel supervisory paradigm, termed as Mutual Supervision (MuSu) MuSu defines training samples for the regression head mainly based on classification predicting scores and in turn, defines samples for the classification head based on localization scores from the regression head. Experimental results show that the convergence of detectors trained by this mutual supervision is guaranteed and the effectiveness of the proposed method is verified on the challenging MS COCO benchmark.
• Score: 37.30539436044029
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The classification and regression head are both indispensable components to build up a dense object detector, which are usually supervised by the same training samples and thus expected to have consistency with each other for detecting objects accurately in the detection pipeline. In this paper, we break the convention of the same training samples for these two heads in dense detectors and explore a novel supervisory paradigm, termed as Mutual Supervision (MuSu), to respectively and mutually assign training samples for the classification and regression head to ensure this consistency. MuSu defines training samples for the regression head mainly based on classification predicting scores and in turn, defines samples for the classification head based on localization scores from the regression head. Experimental results show that the convergence of detectors trained by this mutual supervision is guaranteed and the effectiveness of the proposed method is verified on the challenging MS COCO benchmark. We also find that tiling more anchors at the same location benefits detectors and leads to further improvements under this training scheme. We hope this work can inspire further researches on the interaction of the classification and regression task in detection and the supervision paradigm for detectors, especially separately for these two heads.

Related papers

• Hybrid Classification-Regression Adaptive Loss for Dense Object Detection [19.180514552400883]
  We propose a Hybrid Classification-Regression Adaptive Loss, termed as HCRAL. We introduce the Residual of Classification and IoU (RCI) module for cross-task supervision, addressing task inconsistencies, and the Conditioning Factor (CF) to focus on difficult-to-train samples within each task. We also introduce a new strategy named Expanded Adaptive Training Sample Selection (EATSS) to provide additional samples that exhibit classification and regression inconsistencies.
  arXiv  Detail & Related papers  (2024-08-30T10:31:39Z)
• Memory Consistency Guided Divide-and-Conquer Learning for Generalized Category Discovery [56.172872410834664]
  Generalized category discovery (GCD) aims at addressing a more realistic and challenging setting of semi-supervised learning. We propose a Memory Consistency guided Divide-and-conquer Learning framework (MCDL) Our method outperforms state-of-the-art models by a large margin on both seen and unseen classes of the generic image recognition.
  arXiv  Detail & Related papers  (2024-01-24T09:39:45Z)
• Learning Classifiers of Prototypes and Reciprocal Points for Universal Domain Adaptation [79.62038105814658]
  Universal Domain aims to transfer the knowledge between datasets by handling two shifts: domain-shift and categoryshift. Main challenge is correctly distinguishing the unknown target samples while adapting the distribution of known class knowledge from source to target. Most existing methods approach this problem by first training the target adapted known and then relying on the single threshold to distinguish unknown target samples.
  arXiv  Detail & Related papers  (2022-12-16T09:01:57Z)
• Assessing Generalization of SGD via Disagreement [71.17788927037081]
  We empirically show that the test error of deep networks can be estimated by simply training the same architecture on the same training set but with a different run of Gradient Descent (SGD) This finding not only provides a simple empirical measure to directly predict the test error using unlabeled test data, but also establishes a new conceptual connection between generalization and calibration.
  arXiv  Detail & Related papers  (2021-06-25T17:53:09Z)
• Deep Clustering based Fair Outlier Detection [19.601280507914325]
  We propose an instance-level weighted representation learning strategy to enhance the joint deep clustering and outlier detection. Our DCFOD method consistently achieves superior performance on both the outlier detection validity and two types of fairness notions in outlier detection.
  arXiv  Detail & Related papers  (2021-06-09T15:12:26Z)
• Self-supervised driven consistency training for annotation efficient histopathology image analysis [13.005873872821066]
  Training a neural network with a large labeled dataset is still a dominant paradigm in computational histopathology. We propose a self-supervised pretext task that harnesses the underlying multi-resolution contextual cues in histology whole-slide images to learn a powerful supervisory signal for unsupervised representation learning. We also propose a new teacher-student semi-supervised consistency paradigm that learns to effectively transfer the pretrained representations to downstream tasks based on prediction consistency with the task-specific un-labeled data.
  arXiv  Detail & Related papers  (2021-02-07T19:46:21Z)
• FADER: Fast Adversarial Example Rejection [19.305796826768425]
  Recent defenses have been shown to improve adversarial robustness by detecting anomalous deviations from legitimate training samples at different layer representations. We introduce FADER, a novel technique for speeding up detection-based methods. Our experiments outline up to 73x prototypes reduction compared to analyzed detectors for MNIST dataset and up to 50x for CIFAR10 respectively.
  arXiv  Detail & Related papers  (2020-10-18T22:00:11Z)
• Joint Visual and Temporal Consistency for Unsupervised Domain Adaptive Person Re-Identification [64.37745443119942]
  This paper jointly enforces visual and temporal consistency in the combination of a local one-hot classification and a global multi-class classification. Experimental results on three large-scale ReID datasets demonstrate the superiority of proposed method in both unsupervised and unsupervised domain adaptive ReID tasks.
  arXiv  Detail & Related papers  (2020-07-21T14:31:27Z)
• Scope Head for Accurate Localization in Object Detection [135.9979405835606]
  We propose a novel detector coined as ScopeNet, which models anchors of each location as a mutually dependent relationship. With our concise and effective design, the proposed ScopeNet achieves state-of-the-art results on COCO.
  arXiv  Detail & Related papers  (2020-05-11T04:00:09Z)

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.