کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6938927 | 1449967 | 2018 | 15 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Curriculum learning of visual attribute clusters for multi-task classification
ترجمه فارسی عنوان
یادگیری برنامه درسی خوشه های بصری برای طبقه بندی چند کاره
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کلمات کلیدی
یادگیری برنامه درسی، طبقه بندی چند کاره، ویژگی های ویژوال،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
چکیده انگلیسی
Visual attributes, from simple objects (e.g., backpacks, hats) to soft-biometrics (e.g., gender, height, clothing) have proven to be a powerful representational approach for many applications such as image description and human identification. In this paper, we introduce a novel method to combine the advantages of both multi-task and curriculum learning in a visual attribute classification framework. Individual tasks are grouped after performing hierarchical clustering based on their correlation. The clusters of tasks are learned in a curriculum learning setup by transferring knowledge between clusters. The learning process within each cluster is performed in a multi-task classification setup. By leveraging the acquired knowledge, we speed-up the process and improve performance. We demonstrate the effectiveness of our method via ablation studies and a detailed analysis of the covariates, on a variety of publicly available datasets of humans standing with their full-body visible. Extensive experimentation has proven that the proposed approach boosts the performance by 4%-10%.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 80, August 2018, Pages 94-108
Journal: Pattern Recognition - Volume 80, August 2018, Pages 94-108
نویسندگان
Nikolaos Sarafianos, Theodoros Giannakopoulos, Christophoros Nikou, Ioannis A. Kakadiaris,