Paper: CenterNet: Keypoint Triplets for Object Detection reading notes - Code World

Paper: CenterNet: Keypoint Triplets for Object Detection reading notes

Others 2019-06-11 19:33:38 views: null

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Origin blog.csdn.net/m0_37263345/article/details/90487869

Paper: CenterNet: Keypoint Triplets for Object Detection reading notes

Papers Translation | CenterNet: Keypoint Triplets for Object Detection

[Reading notes for target detection papers] Feature-Enhanced CenterNet for Small Object Detection in Remote Sensing Images

Object keypoint similarity in keypoint detection

[Paper reading notes] EfficientDet: Scalable and Efficient Object Detection

Paper reading - Towards Adversarially Robust Object Detection

[Paper Reading] DiffusionDet: Diffusion Model for Object Detection

Paper: Feature Selective Anchor-Free Module for Single-Shot Object Detection reading notes

Paper: Cascade R-CNN: Delving into High Quality Object Detection reading notes

Paper reading notes 9-Deformable DETR: Deformable Transformers for end-to-end object detection

[Notes] paper CenterNet: Objects as Points

EfficientDet: Scalable and Efficient Object Detection Paper Notes

[Read the paper] Objects as Points and CenterNet | Target Detection

End-to-End Object Detection with Transformers (DETR) paper reading and understanding

[Paper Reading] Zero-Shot Object Detection: A Robust Regional Feature Synthesizer for Object Detection

Paper notes: LSTD A Low-Shot Transfer Detector for Object Detection

[Notes] paper FCOS: Fully Convolutional One-Stage Object Detection

Learning efficient object detection models with knowledge distillation paper notes

[Paper Notes] NeRF-RPN: A general framework for object detection in NeRFs

[Paper reading] Untargeted Backdoor Attack Against Object Detection (untargeted backdoor attack against target detection)

[Read the paper] Objects as Points, also known as CenterNet | Target Detection

GAN paper reading notes

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CPNDet: Corner Proposal Network for Anchor-free, Two-stage Object Detection (paper reading)

Paper reading: "Bridging the Gap in 3D Object Detection for Autonomous Driving"

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