EARL: An Elliptical Distribution aided Adaptive Rotation Label
Assignment for Oriented Object Detection in Remote Sensing Images
- URL: http://arxiv.org/abs/2301.05856v2
- Date: Tue, 17 Oct 2023 02:07:00 GMT
- Title: EARL: An Elliptical Distribution aided Adaptive Rotation Label
Assignment for Oriented Object Detection in Remote Sensing Images
- Authors: Jian Guan, Mingjie Xie, Youtian Lin, Guangjun He, Pengming Feng
- Abstract summary: Adaptive Rotation Label Assignment (EARL) is proposed to select high-quality positive samples adaptively in anchor-free detectors.
In this paper, an adaptive scale sampling (ADS) strategy is presented to select samples adaptively among multi-level feature maps according to the scales of targets.
In addition, a dynamic elliptical distribution aided sampling (DED) strategy is proposed to make the sample distribution more flexible to fit the shapes and orientations of targets.
- Score: 22.963695067213084
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Label assignment is a crucial process in object detection, which
significantly influences the detection performance by determining positive or
negative samples during training process. However, existing label assignment
strategies barely consider the characteristics of targets in remote sensing
images (RSIs) thoroughly, e.g., large variations in scales and aspect ratios,
leading to insufficient and imbalanced sampling and introducing more
low-quality samples, thereby limiting detection performance. To solve the above
problems, an Elliptical Distribution aided Adaptive Rotation Label Assignment
(EARL) is proposed to select high-quality positive samples adaptively in
anchor-free detectors. Specifically, an adaptive scale sampling (ADS) strategy
is presented to select samples adaptively among multi-level feature maps
according to the scales of targets, which achieves sufficient sampling with
more balanced scale-level sample distribution. In addition, a dynamic
elliptical distribution aided sampling (DED) strategy is proposed to make the
sample distribution more flexible to fit the shapes and orientations of
targets, and filter out low-quality samples. Furthermore, a spatial distance
weighting (SDW) module is introduced to integrate the adaptive distance
weighting into loss function, which makes the detector more focused on the
high-quality samples. Extensive experiments on several popular datasets
demonstrate the effectiveness and superiority of our proposed EARL, where
without bells and whistles, it can be easily applied to different detectors and
achieve state-of-the-art performance. The source code will be available at:
https://github.com/Justlovesmile/EARL.
Related papers
- Better Sampling, towards Better End-to-end Small Object Detection [7.7473020808686694]
Small object detection remains unsatisfactory due to limited characteristics and high density and mutual overlap.
We propose methods enhancing sampling within an end-to-end framework.
Our model demonstrates a significant enhancement, achieving a 2.9% increase in average precision (AP) over the state-of-the-art (SOTA) on the VisDrone dataset.
arXiv Detail & Related papers (2024-05-17T04:37:44Z) - Metric-aligned Sample Selection and Critical Feature Sampling for
Oriented Object Detection [4.677438149607058]
We introduce affine transformation to evaluate the quality of samples and propose a distance-based label assignment strategy.
The proposed metric-aligned selection (MAS) strategy can dynamically select samples according to the shape and rotation characteristic of objects.
The results show the state-of-the-art accuracy of the proposed detector.
arXiv Detail & Related papers (2023-06-29T06:36:46Z) - Selecting Learnable Training Samples is All DETRs Need in Crowded
Pedestrian Detection [72.97320260601347]
In crowded pedestrian detection, the performance of DETRs is still unsatisfactory due to the inappropriate sample selection method.
We propose Sample Selection for Crowded Pedestrians, which consists of the constraint-guided label assignment scheme (CGLA)
Experimental results show that the proposed SSCP effectively improves the baselines without introducing any overhead in inference.
arXiv Detail & Related papers (2023-05-18T08:28:01Z) - Temporal Output Discrepancy for Loss Estimation-based Active Learning [65.93767110342502]
We present a novel deep active learning approach that queries the oracle for data annotation when the unlabeled sample is believed to incorporate high loss.
Our approach achieves superior performances than the state-of-the-art active learning methods on image classification and semantic segmentation tasks.
arXiv Detail & Related papers (2022-12-20T19:29:37Z) - Pareto Optimization for Active Learning under Out-of-Distribution Data
Scenarios [79.02009938011447]
We propose a sampling scheme, which selects optimal subsets of unlabeled samples with fixed batch size from the unlabeled data pool.
Experimental results show its effectiveness on both classical Machine Learning (ML) and Deep Learning (DL) tasks.
arXiv Detail & Related papers (2022-07-04T04:11:44Z) - ScatterSample: Diversified Label Sampling for Data Efficient Graph
Neural Network Learning [22.278779277115234]
In some applications where graph neural network (GNN) training is expensive, labeling new instances is expensive.
We develop a data-efficient active sampling framework, ScatterSample, to train GNNs under an active learning setting.
Our experiments on five datasets show that ScatterSample significantly outperforms the other GNN active learning baselines.
arXiv Detail & Related papers (2022-06-09T04:05:02Z) - Dynamic Label Assignment for Object Detection by Combining Predicted and
Anchor IoUs [20.41563386339572]
We introduce a simple and effective approach to perform label assignment dynamically based on the training status with predictions.
Our approach shows improvements in the performance of the detection models with the adaptive label assignment algorithm.
arXiv Detail & Related papers (2022-01-23T23:14:07Z) - Semi-Supervised Domain Adaptation with Prototypical Alignment and
Consistency Learning [86.6929930921905]
This paper studies how much it can help address domain shifts if we further have a few target samples labeled.
To explore the full potential of landmarks, we incorporate a prototypical alignment (PA) module which calculates a target prototype for each class from the landmarks.
Specifically, we severely perturb the labeled images, making PA non-trivial to achieve and thus promoting model generalizability.
arXiv Detail & Related papers (2021-04-19T08:46:08Z) - ANL: Anti-Noise Learning for Cross-Domain Person Re-Identification [25.035093667770052]
We propose an Anti-Noise Learning (ANL) approach, which contains two modules.
FDA module is designed to gather the id-related samples and disperse id-unrelated samples, through the camera-wise contrastive learning and adversarial adaptation.
Reliable Sample Selection ( RSS) module utilizes an Auxiliary Model to correct noisy labels and select reliable samples for the Main Model.
arXiv Detail & Related papers (2020-12-27T02:38:45Z) - 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) - Learning a Unified Sample Weighting Network for Object Detection [113.98404690619982]
Region sampling or weighting is significantly important to the success of modern region-based object detectors.
We argue that sample weighting should be data-dependent and task-dependent.
We propose a unified sample weighting network to predict a sample's task weights.
arXiv Detail & Related papers (2020-06-11T16:19:16Z)
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.