Kavli Affiliate: Zheng Zhu
| First 5 Authors: Chengkun Wang, Wenzhao Zheng, Zheng Zhu, Jie Zhou, Jiwen Lu
| Summary:
This paper proposes an introspective deep metric learning (IDML) framework
for uncertainty-aware comparisons of images. Conventional deep metric learning
methods produce confident semantic distances between images regardless of the
uncertainty level. However, we argue that a good similarity model should
consider the semantic discrepancies with caution to better deal with ambiguous
images for more robust training. To achieve this, we propose to represent an
image using not only a semantic embedding but also an accompanying uncertainty
embedding, which describes the semantic characteristics and ambiguity of an
image, respectively. We further propose an introspective similarity metric to
make similarity judgments between images considering both their semantic
differences and ambiguities. Our framework attains state-of-the-art performance
on the widely used CUB-200-2011, Cars196, and Stanford Online Products datasets
for image retrieval. We further evaluate our framework for image classification
on the ImageNet-1K, CIFAR-10, and CIFAR-100 datasets, which shows that
equipping existing data mixing methods with the proposed introspective metric
consistently achieves better results (e.g., +0.44 for CutMix on ImageNet-1K).
Code is available at: https://github.com/wangck20/IDML.
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