Deep unsupervised learning of visual similarities
作者：
Highlights：
• Unsupervised visual similarity learning is framed as a surrogate classification task.
• Use weak estimates of local similarities to group samples into compact cliques.
• Train a ConvNet to learn visual similarities by learning to categorize cliques.
• Optimization problem to sample training minibatches without conflicting relations.
• Competitive performance on detailed posture analysis and object classification.
摘要
•Unsupervised visual similarity learning is framed as a surrogate classification task.•Use weak estimates of local similarities to group samples into compact cliques.•Train a ConvNet to learn visual similarities by learning to categorize cliques.•Optimization problem to sample training minibatches without conflicting relations.•Competitive performance on detailed posture analysis and object classification.
论文关键词：Visual similarity learning,Deep learning,Self-supervised learning,Human pose analysis,Object retrieval
论文评审过程：Received 3 February 2017, Revised 3 October 2017, Accepted 23 January 2018, Available online 31 January 2018, Version of Record 9 February 2018.
论文官网地址：https://doi.org/10.1016/j.patcog.2018.01.036
