An Attention-Based Deep Learning Model for Multiple Pedestrian Attributes Recognition
動機： 1.考慮到不一樣的全身屬性之間的語義相關性強。 2.每一類佔得權重大小影響屬性的識別程度，好比說背景和前景的比重web 貢獻： 1.提出了一種PAR的多任務分類模型，其主要特徵是聚焦於前景(人體)特徵，減弱特徵表示中背景區域的影響; 2.提出了一個加權和損失函數，有效處理優化機制中每一個類別(如性別、身材、年齡等)的貢獻，在推理步驟中禁止某些類別占主導地位; 3.受到注意機制的啓發，實現了
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相關文章
- 1. An Attention-Based Deep Learning Model for Multiple Pedestrian Attributes Recognition
- 2. Deep Residual Learning for Image Recognition
- 3. ResNet--Deep Residual Learning for Image Recognition
- 4. Pose Guided Deep Model for Pedestrian Attribute Recognition in Surveillance Scenarios
- 5. Grouping Attribute Recognition for Pedestrian with Joint Recurrent Learning
- 6. 【ReID】【Skimming】An Implicit Attention Mechanism for Deep Learning Pedestrian Re-identification Frame
- 7. 【論文翻譯】Deep Residual Learning for Image Recognition
- 8. [ResNet]Deep Residual Learning for Image Recognition
- 9. ResNet —— Deep Residual Learning for Image Recognition
- 10. Deep Residual Learning for Image Recognition （zh）