Cross-Level Multi-Instance Distillation for Self-Supervised Fine-Grained Visual Categorization

• URL: http://arxiv.org/abs/2401.08860v2
• Date: Mon, 26 Feb 2024 23:39:53 GMT
• Title: Cross-Level Multi-Instance Distillation for Self-Supervised Fine-Grained Visual Categorization
• Authors: Qi Bi and Wei Ji and Jingjun Yi and Haolan Zhan and Gui-Song Xia
• Abstract summary: We propose a Cross-level Multi-instance Distillation (CMD) framework to tackle the challenge of fine-grained pre-text representation. Our key idea is to consider the importance of each image patch in determining the fine-grained pre-text representation by multiple instance learning. The proposed method outperforms the contemporary method by upto 10.14% and existing state-of-the-art self-supervised learning approaches by upto 19.78% on both top-1 accuracy and Rank-1 retrieval metric.
• Score: 41.86678318006878
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: High-quality annotation of fine-grained visual categories demands great expert knowledge, which is taxing and time consuming. Alternatively, learning fine-grained visual representation from enormous unlabeled images (e.g., species, brands) by self-supervised learning becomes a feasible solution. However, recent researches find that existing self-supervised learning methods are less qualified to represent fine-grained categories. The bottleneck lies in that the pre-text representation is built from every patch-wise embedding, while fine-grained categories are only determined by several key patches of an image. In this paper, we propose a Cross-level Multi-instance Distillation (CMD) framework to tackle the challenge. Our key idea is to consider the importance of each image patch in determining the fine-grained pre-text representation by multiple instance learning. To comprehensively learn the relation between informative patches and fine-grained semantics, the multi-instance knowledge distillation is implemented on both the region/image crop pairs from the teacher and student net, and the region-image crops inside the teacher / student net, which we term as intra-level multi-instance distillation and inter-level multi-instance distillation. Extensive experiments on CUB-200-2011, Stanford Cars and FGVC Aircraft show that the proposed method outperforms the contemporary method by upto 10.14% and existing state-of-the-art self-supervised learning approaches by upto 19.78% on both top-1 accuracy and Rank-1 retrieval metric.

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