Decoupled IoU Regression for Object Detection

• URL: http://arxiv.org/abs/2202.00866v1
• Date: Wed, 2 Feb 2022 04:01:11 GMT
• Title: Decoupled IoU Regression for Object Detection
• Authors: Yan Gao and Qimeng Wang and Xu Tang and Haochen Wang and Fei Ding and Jing Li and Yao Hu
• Abstract summary: Non-maximum suppression (NMS) is widely used in object detection pipelines for removing duplicated bounding boxes. Inconsistency between the confidence for NMS and the real localization confidence seriously affects detection performance. We propose a novel Decoupled IoU Regression model to handle these problems.
• Score: 31.9114940121939
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Non-maximum suppression (NMS) is widely used in object detection pipelines for removing duplicated bounding boxes. The inconsistency between the confidence for NMS and the real localization confidence seriously affects detection performance. Prior works propose to predict Intersection-over-Union (IoU) between bounding boxes and corresponding ground-truths to improve NMS, while accurately predicting IoU is still a challenging problem. We argue that the complex definition of IoU and feature misalignment make it difficult to predict IoU accurately. In this paper, we propose a novel Decoupled IoU Regression (DIR) model to handle these problems. The proposed DIR decouples the traditional localization confidence metric IoU into two new metrics, Purity and Integrity. Purity reflects the proportion of the object area in the detected bounding box, and Integrity refers to the completeness of the detected object area. Separately predicting Purity and Integrity can divide the complex mapping between the bounding box and its IoU into two clearer mappings and model them independently. In addition, a simple but effective feature realignment approach is also introduced to make the IoU regressor work in a hindsight manner, which can make the target mapping more stable. The proposed DIR can be conveniently integrated with existing two-stage detectors and significantly improve their performance. Through a simple implementation of DIR with HTC, we obtain 51.3% AP on MS COCO benchmark, which outperforms previous methods and achieves state-of-the-art.

Related papers

• Unified-IoU: For High-Quality Object Detection [1.62877896907106]
  We propose a new IoU loss function, called Unified-IoU (UIoU), which is more concerned with the weight assignment between different quality prediction boxes. Our proposed method achieves better performance on multiple datasets, especially at a high IoU threshold.
  arXiv  Detail & Related papers  (2024-08-13T04:56:45Z)
• Do We Still Need Non-Maximum Suppression? Accurate Confidence Estimates and Implicit Duplication Modeling with IoU-Aware Calibration [6.093524345727119]
  We show that we can eliminate classic NMS-style post-processing by using IoU-aware calibration. IoU-aware calibration is a conditional Beta calibration. Our experiments show that the proposed IoU-aware calibration can successfully model duplicate detections.
  arXiv  Detail & Related papers  (2023-09-06T15:47:33Z)
• 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)
• Directly Optimizing IoU for Bounding Box Localization [5.018156030818881]
  This paper presents a novel method to maximize the detection of bounding boxes for the bounding boxes. The Smooth IoU method has shown performance gains over the standard Huber loss. It has been evaluated on the Oxford IIIT, Udacity self-driving car, PA Pets Union, and VWFS Car Damage datasets.
  arXiv  Detail & Related papers  (2023-04-14T17:08:12Z)
• Rethinking IoU-based Optimization for Single-stage 3D Object Detection [103.83141677242871]
  We propose a Rotation-Decoupled IoU (RDIoU) method that can mitigate the rotation-sensitivity issue. Our RDIoU simplifies the complex interactions of regression parameters by decoupling the rotation variable as an independent term.
  arXiv  Detail & Related papers  (2022-07-19T15:35:23Z)
• The KFIoU Loss for Rotated Object Detection [115.334070064346]
  In this paper, we argue that one effective alternative is to devise an approximate loss who can achieve trend-level alignment with SkewIoU loss. Specifically, we model the objects as Gaussian distribution and adopt Kalman filter to inherently mimic the mechanism of SkewIoU. The resulting new loss called KFIoU is easier to implement and works better compared with exact SkewIoU.
  arXiv  Detail & Related papers  (2022-01-29T10:54:57Z)
• Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge Computing: A Contextual-Bandit Approach [81.5261621619557]
  We propose an adaptive anomaly detection scheme with hierarchical edge computing (HEC) We first construct multiple anomaly detection DNN models with increasing complexity, and associate each of them to a corresponding HEC layer. Then, we design an adaptive model selection scheme that is formulated as a contextual-bandit problem and solved by using a reinforcement learning policy network.
  arXiv  Detail & Related papers  (2021-08-09T08:45:47Z)
• Uncertainty-Aware Deep Calibrated Salient Object Detection [74.58153220370527]
  Existing deep neural network based salient object detection (SOD) methods mainly focus on pursuing high network accuracy. These methods overlook the gap between network accuracy and prediction confidence, known as the confidence uncalibration problem. We introduce an uncertaintyaware deep SOD network, and propose two strategies to prevent deep SOD networks from being overconfident.
  arXiv  Detail & Related papers  (2020-12-10T23:28:36Z)
• 3D IoU-Net: IoU Guided 3D Object Detector for Point Clouds [68.44740333471792]
  We add a 3D IoU prediction branch to the regular classification and regression branches. We propose a 3D IoU-Net with IoU sensitive feature learning and an IoU alignment operation. The experimental results on the KITTI car detection benchmark show that 3D IoU-Net with IoU perception achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2020-04-10T09:24:29Z)
• Seeing without Looking: Contextual Rescoring of Object Detections for AP Maximization [4.346179456029563]
  We propose to incorporate context in object detection by post-processing the output of an arbitrary detector. Rescoring is done by conditioning on contextual information from the entire set of detections. We show that AP can be improved by simply reassigning the detection confidence values.
  arXiv  Detail & Related papers  (2019-12-27T18:56:29Z)

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.