2018-ECCV-Beyond Part Models:Person Retrieval with Refined Part Pooling論文筆記
2018-ECCV-Beyond Part Models:Person Retrieval with Refined Part Pooling論文筆記 一、提出問題 本文主要研究基於分塊特徵的re-id方法，目前的基於分塊特徵的方法性能較好，但是一方面這些方法利用了額外的信息(pose estimation等)，另一方面因爲目標域與源域的差異性，這些方法產生局部塊的模型精度相對不高而且容易引入噪聲
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- 8. [論文閱讀] Beyond part models: Person retrieval with refined part pooling
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相關文章
- 1. 【論文筆記-02】Beyond Part Model Person Retrieval with Refined Part Pooling
- 2. Beyond Part Models: Person Retrieval with Refined Part Pooling[論文筆記]
- 3. Beyond Part Models- Person Retrieval with Refined Part Pooling
- 4. Beyond Part Models: Person Retrieval with Refined Part Pooling (ECCV2018)
- 5. 論文之Beyond Part Models: Person Retrieval with Refined Part Pooling
- 6. 《Beyond Part Models: Person Retrieval with Refined Part Pooling 》PCB論文解讀
- 7. 讀論文Beyond Part Models Person Retrieval with Refined Part Pooling
- 8. [論文閱讀] Beyond part models: Person retrieval with refined part pooling
- 9. 論文筆記 Beyond Part Models: Person Retrieval with Refined Part Pooling_ECCV_2018
- 10. part-aligned系列論文：1711.Beyond Part Models- Person Retrieval with Refined Part Pooling 論文閱讀筆記