Hierarchical Object Detection with Deep Reinforcement Learning论文笔记
这是一篇NIPS2016中有关目标检测的文章，虽然有点老，但是其free anchor的思想还是值得借鉴。尤其今年和free anchor相关的目标检测研究大火，但其大多是从先探测物体的中心点或者是关键点，由点到面从而学习到精确的轮廓或者位置信息。 一、研究动机 受人眼观察世界方式的启发：我们首先将我们的视线集中在一幅图片的显著位置，通过对该区域的信息提取有助于将我们的视线转移到相关区域直到完全理
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相关文章
- 1. 论文笔记：《Playing Atari with Deep Reinforcement Learning》
- 2. 论文解析——Deep Reinforcement Learning of Region Proposal Networks for Object Detection
- 3. 【论文翻译】Playing Atari with Deep Reinforcement Learning
- 4. 论文笔记——NEURAL ARCHITECTURE SEARCH WITH REINFORCEMENT LEARNING
- 5. 论文笔记《Neural Architecture Search With Reinforcement Learning》
- 6. 【论文笔记】NEURAL COMBINATORIAL OPTIMIZATION WITH REINFORCEMENT LEARNING
- 7. 论文笔记 Reinforcement Learning with Derivative-Free Exploration
- 8. Mastering Complex Control in MOBA Games with Deep Reinforcement Learning（论文笔记）
- 9. Generating Test Input with Deep Reinforcement Learning 论文笔记
- 10. Deep Learning Object Detection