[论文阅读]DenseBox: Unifying Landmark Localization with End to End Object Detection
文章目录 contribution DenseBox for Detection ground truth生成 模型设计 多任务训练 平衡采样 结合关键点的模型 对比 contribution （1）使用全卷积网络进行目标检测任务，并可以准确有效的检测多个对象 （2）多任务学习中使用landmark localization会进一步提高目标检测准确性 DenseBox for Detection
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相关文章
- 1. DenseBox: Unifying Landmark Localization with End to End Object Detection
- 2. End-to-End Object Detection with Transformers论文阅读笔记
- 3. End-to-End Object Detection with Transformers
- 4. MyDLNote-Detection: DETR : End-to-End Object Detection with Transformers
- 5. 论文阅读：Associative Embedding: End-to-End Learning for Joint Detection and Grouping
- 6. DETR : End-to-End Object Detection with Transformers
- 7. ECCV 2020 DETR:《End-to-End Object Detection with Transformers》论文笔记
- 8. 论文阅读：End-To-End Memory Networks
- 9. 如何看待End-to-End Object Detection with Transformers？
- 10. 论文笔记+源码 DETR:End-to-End Object Detection with Transformers