Look into Person: Self-supervised Structure-sensitive Learning and A New Benchmark for Human Parsing
摘要 由於人體解析巨大的應用潛力，引起了廣泛的研究。但是現存的數據集只有有限的圖像和標註，缺乏人類外觀的多變性； 本論文引入了一個新的benchmark，「Look into Person (LIP)」， 在可擴展性、多樣性和難度方面取得了重大進展，對未來以人爲中心的研究具有重大貢獻； 這個全面的數據集包含50000多幅經過精心標註的圖像，其中包含19個語義部件標籤，它們是從更廣泛的視角、遮擋和復
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