Look into Person: Self-supervised Structure-sensitive Learning and A New Benchmark for Human Parsing
0.Abstract ： 說明本篇文章的主要工做 ： 一是提出了一個用於人體解析的大數據集 Look into Person (LIP)， 這個數據集相比以前的數據集更大，覆蓋情景更多，更復雜，做者還在這個數據集上詳細分析了以前的各類人體解析方法的優劣 。二是提出了一種 self-supervised structure-sensitive approaches 去結合 high-level 的人
相關文章
- 1. [paper] Look Into Person
- 2. 圖像分割"LIP: Self-supervised Structure-sensitive Learning and A New Benchmark for Human Parsing"
- 3. 論文閱讀：《LIP: Self-supervised Structure-sensitive Learning and A New Benchmark for Human Parsing》
- 4. Human Semantic Parsing for Person Re-identification(Parsing 方法)
- 5. Human Semantic Parsing for Person Re-identification
- 6. human parsing—學習筆記
- 7. 論文閱讀理解 - Look into Person: Self-supervised Structure-sensitive Learning
- 8. [paper] Multiple Human Parsing
- 9. Macro-Micro Adversarial Network for Human Parsing
- 10. Person Re-Identification (ReID), a New Facial Recognition Record
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相關文章
- 1. [paper] Look Into Person
- 2. 圖像分割"LIP: Self-supervised Structure-sensitive Learning and A New Benchmark for Human Parsing"
- 3. 論文閱讀：《LIP: Self-supervised Structure-sensitive Learning and A New Benchmark for Human Parsing》
- 4. Human Semantic Parsing for Person Re-identification(Parsing 方法)
- 5. Human Semantic Parsing for Person Re-identification
- 6. human parsing—學習筆記
- 7. 論文閱讀理解 - Look into Person: Self-supervised Structure-sensitive Learning
- 8. [paper] Multiple Human Parsing
- 9. Macro-Micro Adversarial Network for Human Parsing
- 10. Person Re-Identification (ReID), a New Facial Recognition Record