Dense Object Detection
22 papers with code • 1 benchmarks • 3 datasets
Libraries
Use these libraries to find Dense Object Detection models and implementations
DOTA
SKU110K
xView3-SAR
Most implemented papers
Focal Loss for Dense Object Detection
facebookresearch/detectron
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ICCV 2017
Our novel Focal Loss focuses training on a sparse set of hard examples and prevents the vast number of easy negatives from overwhelming the detector during training.
PP-YOLOE: An evolved version of YOLO
PaddlePaddle/PaddleDetection
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In this report, we present PP-YOLOE, an industrial state-of-the-art object detector with high performance and friendly deployment.
Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection
implus/GFocal
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NeurIPS 2020
Specifically, we merge the quality estimation into the class prediction vector to form a joint representation of localization quality and classification, and use a vector to represent arbitrary distribution of box locations.
Precise Detection in Densely Packed Scenes
eg4000/SKU110K_CVPR19
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CVPR 2019
We propose a novel, deep-learning based method for precise object detection, designed for such challenging settings.
Generalized Focal Loss V2: Learning Reliable Localization Quality Estimation for Dense Object Detection
Such a property makes the distribution statistics of a bounding box highly correlated to its real localization quality.
Salience DETR: Enhancing Detection Transformer with Hierarchical Salience Filtering Refinement
xiuqhou/Salience-DETR
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CVPR 2024
DETR-like methods have significantly increased detection performance in an end-to-end manner.
Soft Anchor-Point Object Detection
In this work, we boost the performance of the anchor-point detector over the key-point counterparts while maintaining the speed advantage.
AutoAssign: Differentiable Label Assignment for Dense Object Detection
Megvii-BaseDetection/AutoAssign
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During training, to both satisfy the prior distribution of data and adapt to category characteristics, we present Center Weighting to adjust the category-specific prior distributions.
BorderDet: Border Feature for Dense Object Detection
Megvii-BaseDetection/BorderDet
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ECCV 2020
In this paper, We propose a simple and efficient operator called Border-Align to extract "border features" from the extreme point of the border to enhance the point feature.
A Solution to Product detection in Densely Packed Scenes
Media-Smart/SKU110K-DenseDet
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To grasp the essential feature of the densely packed scenes, we analysis the stages of a detector and investigate the bottleneck which limits the performance.
