计算机科学
相关性(法律)
特征(语言学)
嵌入
特征学习
卷积神经网络
代表(政治)
等级制度
人工智能
相似性(几何)
模式识别(心理学)
图像(数学)
图像检索
机器学习
哲学
政治
语言学
经济
法学
市场经济
政治学
作者
Xiaofan Zhang,Feng Zhou,Yuanqing Lin,Shaoting Zhang
标识
DOI:10.1109/cvpr.2016.126
摘要
Recent algorithms in convolutional neural networks (CNN) considerably advance the fine-grained image classification, which aims to differentiate subtle differences among subordinate classes. However, previous studies have rarely focused on learning a fined-grained and structured feature representation that is able to locate similar images at different levels of relevance, e.g., discovering cars from the same make or the same model, both of which require high precision. In this paper, we propose two main contributions to tackle this problem. 1) A multitask learning framework is designed to effectively learn fine-grained feature representations by jointly optimizing both classification and similarity constraints. 2) To model the multi-level relevance, label structures such as hierarchy or shared attributes are seamlessly embedded into the framework by generalizing the triplet loss. Extensive and thorough experiments have been conducted on three finegrained datasets, i.e., the Stanford car, the Car-333, and the food datasets, which contain either hierarchical labels or shared attributes. Our proposed method has achieved very competitive performance, i.e., among state-of-the-art classification accuracy when not using parts. More importantly, it significantly outperforms previous fine-grained feature representations for image retrieval at different levels of relevance.
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