Confidence-driven Bounding Box Localization for Small Object Detection

• URL: http://arxiv.org/abs/2303.01803v1
• Date: Fri, 3 Mar 2023 09:19:08 GMT
• Title: Confidence-driven Bounding Box Localization for Small Object Detection
• Authors: Huixin Sun, Baochang Zhang, Yanjing Li, Xianbin Cao
• Abstract summary: We present Confidence-driven Bounding Box localization (C-BBL) method to rectify the gradients. C-BBL quantizes continuous labels into grids and formulates two-hot ground truth labels. We demonstrate the generalizability of C-BBL to different label systems and effectiveness for high resolution detection.
• Score: 30.906712428887147
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite advancements in generic object detection, there remains a performance gap in detecting small objects compared to normal-scale objects. We for the first time observe that existing bounding box regression methods tend to produce distorted gradients for small objects and result in less accurate localization. To address this issue, we present a novel Confidence-driven Bounding Box Localization (C-BBL) method to rectify the gradients. C-BBL quantizes continuous labels into grids and formulates two-hot ground truth labels. In prediction, the bounding box head generates a confidence distribution over the grids. Unlike the bounding box regression paradigms in conventional detectors, we introduce a classification-based localization objective through cross entropy between ground truth and predicted confidence distribution, generating confidence-driven gradients. Additionally, C-BBL describes a uncertainty loss based on distribution entropy in labels and predictions to further reduce the uncertainty in small object localization. The method is evaluated on multiple detectors using three object detection benchmarks and consistently improves baseline detectors, achieving state-of-the-art performance. We also demonstrate the generalizability of C-BBL to different label systems and effectiveness for high resolution detection, which validates its prospect as a general solution.

Related papers

• Harnessing Uncertainty-aware Bounding Boxes for Unsupervised 3D Object Detection [22.297964850282177]
  Unsupervised 3D object detection aims to identify objects of interest from unlabeled raw data, such as LiDAR points. Recent approaches usually adopt pseudo 3D bounding boxes (3D bboxes) from clustering algorithm to initialize the model training. We introduce a new uncertainty-aware framework for unsupervised 3D object detection, dubbed UA3D.
  arXiv  Detail & Related papers  (2024-08-01T15:01:07Z)
• Bi-discriminator Domain Adversarial Neural Networks with Class-Level Gradient Alignment [87.8301166955305]
  We propose a novel bi-discriminator domain adversarial neural network with class-level gradient alignment. BACG resorts to gradient signals and second-order probability estimation for better alignment of domain distributions. In addition, inspired by contrastive learning, we develop a memory bank-based variant, i.e. Fast-BACG, which can greatly shorten the training process.
  arXiv  Detail & Related papers  (2023-10-21T09:53:17Z)
• Exploiting Low-confidence Pseudo-labels for Source-free Object Detection [54.98300313452037]
  Source-free object detection (SFOD) aims to adapt a source-trained detector to an unlabeled target domain without access to the labeled source data. Current SFOD methods utilize a threshold-based pseudo-label approach in the adaptation phase. We propose a new approach to take full advantage of pseudo-labels by introducing high and low confidence thresholds.
  arXiv  Detail & Related papers  (2023-10-19T12:59:55Z)
• Localization-Guided Track: A Deep Association Multi-Object Tracking Framework Based on Localization Confidence of Detections [4.565826090373598]
  localization confidence is applied in MOT for the first time, with appearance clarity and localization accuracy of detection boxes taken into account. Our proposed method outperforms the compared state-of-art tracking methods.
  arXiv  Detail & Related papers  (2023-09-18T13:45:35Z)
• Small Object Detection via Coarse-to-fine Proposal Generation and Imitation Learning [52.06176253457522]
  We propose a two-stage framework tailored for small object detection based on the Coarse-to-fine pipeline and Feature Imitation learning. CFINet achieves state-of-the-art performance on the large-scale small object detection benchmarks, SODA-D and SODA-A.
  arXiv  Detail & Related papers  (2023-08-18T13:13:09Z)
• GLENet: Boosting 3D Object Detectors with Generative Label Uncertainty Estimation [70.75100533512021]
  In this paper, we formulate the label uncertainty problem as the diversity of potentially plausible bounding boxes of objects. We propose GLENet, a generative framework adapted from conditional variational autoencoders, to model the one-to-many relationship between a typical 3D object and its potential ground-truth bounding boxes with latent variables. The label uncertainty generated by GLENet is a plug-and-play module and can be conveniently integrated into existing deep 3D detectors.
  arXiv  Detail & Related papers  (2022-07-06T06:26:17Z)
• Mitigating the Mutual Error Amplification for Semi-Supervised Object Detection [92.52505195585925]
  We propose a Cross Teaching (CT) method, aiming to mitigate the mutual error amplification by introducing a rectification mechanism of pseudo labels. In contrast to existing mutual teaching methods that directly treat predictions from other detectors as pseudo labels, we propose the Label Rectification Module (LRM)
  arXiv  Detail & Related papers  (2022-01-26T03:34:57Z)
• Boundary Distribution Estimation for Precise Object Detection [12.247010914825971]
  In the field of object detection, the task of object localization is typically accomplished through a dedicated that emphasizes bounding box regression. This traditionally predicts the object's position by regressing the box's center position and scaling factors. In this paper, we address the shortcomings of previous methods through theoretical analysis and experimental verification. Our approach enhances the accuracy of bounding box localization by refining the box edges based on the estimated distribution at the object's boundary.
  arXiv  Detail & Related papers  (2021-11-02T06:58:22Z)
• Uncertainty-Aware Model Adaptation for Unsupervised Cross-Domain Object Detection [12.807987076435928]
  This work tackles the unsupervised cross-domain object detection problem. It aims to generalize a pre-trained object detector to a new target domain without labels.
  arXiv  Detail & Related papers  (2021-08-28T09:37:18Z)
• 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.