Dense Object Detection
17 papers with code • 1 benchmarks • 3 datasets
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Most implemented papers
Focal Loss for Dense Object Detection
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.
Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection
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
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.
PP-YOLOE: An evolved version of YOLO
In this report, we present PP-YOLOE, an industrial state-of-the-art object detector with high performance and friendly deployment.
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
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
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
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.
Localization Distillation for Dense Object Detection
Previous KD methods for object detection mostly focus on imitating deep features within the imitation regions instead of mimicking classification logit due to its inefficiency in distilling localization information and trivial improvement.