Local Similarity-Aware Deep Feature Embedding
Refereed conference paper presented and published in conference proceedings


摘要Existing deep embedding methods in vision tasks are capable of learning a compact
Euclidean space from images, where Euclidean distances correspond to a similarity
metric. To make learning more effective and efficient, hard sample mining is
usually employed, with samples identified through computing the Euclidean feature
distance. However, the global Euclidean distance cannot faithfully characterize
the true feature similarity in a complex visual feature space, where the intraclass
distance in a high-density region may be larger than the interclass distance in
low-density regions. In this paper, we introduce a Position-Dependent Deep Metric
(PDDM) unit, which is capable of learning a similarity metric adaptive to local
feature structure. The metric can be used to select genuinely hard samples in a
local neighborhood to guide the deep embedding learning in an online and robust
manner. The new layer is appealing in that it is pluggable to any convolutional
networks and is trained end-to-end. Our local similarity-aware feature embedding
not only demonstrates faster convergence and boosted performance on two complex
image retrieval datasets, its large margin nature also leads to superior generalization
results under the large and open set scenarios of transfer learning and zero-shot
learning on ImageNet 2010 and ImageNet-10K datasets.
著者Chen Huang, Chen Change Loy, Xiaoou Tang
會議名稱Advances in Neural Information Processing Systems
會議論文集題名30th Conference on Neural Information Processing Systems (NIPS 2016)

上次更新時間 2018-18-01 於 08:21