[論文閱讀]DenseBox: Unifying Landmark Localization with End to End Object Detection
文章目錄 contribution DenseBox for Detection ground truth生成 模型設計 多任務訓練 平衡採樣 結合關鍵點的模型 對比 contribution （1）使用全卷積網絡進行目標檢測任務，並可以準確有效的檢測多個對象 （2）多任務學習中使用landmark localization會進一步提高目標檢測準確性 DenseBox for Detection
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相關文章
- 1. DenseBox: Unifying Landmark Localization with End to End Object Detection
- 2. End-to-End Object Detection with Transformers論文閱讀筆記
- 3. End-to-End Object Detection with Transformers
- 4. MyDLNote-Detection: DETR : End-to-End Object Detection with Transformers
- 5. 論文閱讀：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》論文筆記
- 8. 論文閱讀：End-To-End Memory Networks
- 9. 如何看待End-to-End Object Detection with Transformers？
- 10. 論文筆記+源碼 DETR:End-to-End Object Detection with Transformers