Your "Flamingo" is My "Bird": Fine-Grained, or Not

• URL: http://arxiv.org/abs/2011.09040v3
• Date: Sun, 28 Mar 2021 06:17:51 GMT
• Title: Your "Flamingo" is My "Bird": Fine-Grained, or Not
• Authors: Dongliang Chang, Kaiyue Pang, Yixiao Zheng, Zhanyu Ma, Yi-Zhe Song, and Jun Guo
• Abstract summary: We investigate how to tailor for different fine-grained definitions under divergent levels of expertise. We first conduct a comprehensive human study where we confirm that most participants prefer multi-granularity labels. We then discover the key intuition that: coarse-level label prediction exacerbates fine-grained feature learning.
• Score: 60.25769809922673
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Whether what you see in Figure 1 is a "flamingo" or a "bird", is the question we ask in this paper. While fine-grained visual classification (FGVC) strives to arrive at the former, for the majority of us non-experts just "bird" would probably suffice. The real question is therefore -- how can we tailor for different fine-grained definitions under divergent levels of expertise. For that, we re-envisage the traditional setting of FGVC, from single-label classification, to that of top-down traversal of a pre-defined coarse-to-fine label hierarchy -- so that our answer becomes "bird"-->"Phoenicopteriformes"-->"Phoenicopteridae"-->"flamingo". To approach this new problem, we first conduct a comprehensive human study where we confirm that most participants prefer multi-granularity labels, regardless whether they consider themselves experts. We then discover the key intuition that: coarse-level label prediction exacerbates fine-grained feature learning, yet fine-level feature betters the learning of coarse-level classifier. This discovery enables us to design a very simple albeit surprisingly effective solution to our new problem, where we (i) leverage level-specific classification heads to disentangle coarse-level features with fine-grained ones, and (ii) allow finer-grained features to participate in coarser-grained label predictions, which in turn helps with better disentanglement. Experiments show that our method achieves superior performance in the new FGVC setting, and performs better than state-of-the-art on traditional single-label FGVC problem as well. Thanks to its simplicity, our method can be easily implemented on top of any existing FGVC frameworks and is parameter-free.

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