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Learning to Classify Fine-Grained Categories with Privileged Visual-Semantic Misalignment
Ke Chen; Zhaoxiang Zhang
Source PublicationIEEE Transactions on Big Data
AbstractImage categorisation is an active yet challenging research topic in computer vision, which is to classify the images according to their semantic content. Recently, fine-grained object categorisation has attracted wide attention and remains difficult due to feature inconsistency caused by smaller inter-class and larger intra-class variation as well as large varying poses. Most of the existing frameworks focused on exploiting a more discriminative imagery representation or developing a more robust classification framework to mitigate the suffering. The concern has recently been paid to discovering the dependency across fine-grained class labels based on Convolutional Neural Networks. Encouraged by the success of semantic label embedding to discover the fine-grained class labels’ correlation, this paper exploits the misalignment between visual feature space and semantic label embedding space and incorporates it as a privileged information into a cost-sensitive learning framework. Owing to capturing both the variation of imagery feature representation and also the label correlation in the semantic label embedding space, such a visual-semantic misalignment can be employed to reflect the importance of instances, which is more informative that conventional cost-sensitivities. Experiment results demonstrate the effectiveness of the proposed framework on public fine-grained benchmarks with achieving superior performance to state-of-the-arts.
KeywordFine-grained Categorisation Cost-sensitive Learning Deep Feature Visual-semantic Alignment Multiclass Classification
Document Type期刊论文
Corresponding AuthorZhaoxiang Zhang
Recommended Citation
GB/T 7714
Ke Chen,Zhaoxiang Zhang. Learning to Classify Fine-Grained Categories with Privileged Visual-Semantic Misalignment[J]. IEEE Transactions on Big Data,2016,PP(99):2332-7790.
APA Ke Chen,&Zhaoxiang Zhang.(2016).Learning to Classify Fine-Grained Categories with Privileged Visual-Semantic Misalignment.IEEE Transactions on Big Data,PP(99),2332-7790.
MLA Ke Chen,et al."Learning to Classify Fine-Grained Categories with Privileged Visual-Semantic Misalignment".IEEE Transactions on Big Data PP.99(2016):2332-7790.
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