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 Leaf image patterns have been actively researched for plant species recognition. However, as a very challenging finegrained pattern identification issue, cultivar recognition in which the leaf image patterns usually have very subtle difference among cultivars has not yet received considerable attention in computer vision community. In this paper, a novel leaf image descriptor, named local angle cooccurrence histograms, is proposed for addressing this issue. It is a kind of co-occurrence descriptors that encoding both shape and texture features which make them more informative than the existing individual descriptors and cooccurrence features. A feature fusion scheme is proposed to integrate the handcrafted descriptors with deep learning features for further boosting the retrieval performance. The experimental results on the challenging soybean cultivar recognition and peanut cultivar recognition both indicate the superiority of the proposed method over the state-of-the-art methods on leaf image pattern characterization and validate the effectiveness of the proposed method for fine-grained leaf image retrieval.

In the domain of data enhancement, image restoration and data augmentation are two tasks gaining increasing attention. Current image restoration models focus on improving clarity using pre-trained generative models, and data augmentation methods try to generate new samples with the help of generative models. These two related topics have long been studied completely separately. We propose a downstream-friendly restoration framework based on pre-trained generative models with the capability of data augmentation for face images. We carefully design our framework to achieve high fidelity when inheriting the generation ability from the pre-trained generator. To achieve this goal, we use a modified U-Net to predict the biases of latent codes and feature maps to guide the generator. We further propose to adopt linear interpolation as an approach to enriching the datasets for downstream tasks, especially for classimbalanced tasks. Effectiveness of our method is demonstrated through experiments on three datasets and one downstream task.

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