Learning to recognize novel visual categories from a few examples is a challenging task for machines in realworld applications. In contrast, humans have the ability to discriminate even similar objects with little supervision. This paper attempts to address the few-shot fine-grained recognition problem. We propose a feature fusion model to explore the largest discriminative features by focusing on key regions. The model utilizes focus-area location to discover the perceptually similar regions among objects. High-order integration is employed to capture the interaction information among intra-parts. We also design a Center Neighbor Loss to form robust embedding space distribution for generating discriminative features. Furthermore, we build a typical fine-grained and few-shot learning dataset miniPPlankton from the real-world application in the area of marine ecological environment. Extensive experiments are carried out to validate the performance of our model. First the model is evaluated with two challenging experiments based on the miniDogsNet and Caltech-UCSD public datasets. The results demonstrate that our model achieves competitive performance compared with state-of-the-art models. Then, we implement our model for the real-world phytoplankton recognition task. The experimental results show the superiority of the proposed model compared with others on the miniPPlankton dataset.

Though saliency detectors has made stunning progress recently. The performances of the state-of-the-art saliency detectors are not acceptable in some confusing areas, e.g., object boundary. We argue that the feature spatial independence should be one of the root cause. This paper explores the ubiquitous relations on the deep features to promote the existing saliency detectors efficiently. We establish the relation by maximizing the mutual information of the deep features of the same category via deep neural networks to break this independence. We introduce a threshold-constrained training pair construction strategy to ensure that we can accurately estimate the relations between different image parts in a self-supervised way. The relation can be utilized to further excavate the salient areas and inhibit confusing backgrounds. The experiments demonstrate that our method can significantly boost the performance of the state-of-the-art saliency detectors on various benchmark datasets. Besides, our model is label-free and extremely efficient. The inference speed is 140 FPS on a single GTX1080 GPU.

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Low-shot learning indicates the ability to recognize unseen objects based on very limited labeled training samples, which simulates human visual intelligence. According to this concept, we propose a multi-level similarity model (MLSM) to capture the deep encoded distance metric between the support and query samples. Our approach is achieved based on the fact that the image similarity learning can be decomposed into imagelevel, global-level, and object-level. Once the similarity function is established, MLSM will be able to classify images for unseen classes by computing the similarity scores between a limited number of labeled samples and the target images. Furthermore, we conduct 5-way experiments with both 1-shot and 5-shot setting on Caltech-UCSD datasets. It is demonstrated that the proposed model can achieve promising results compared with the existing methods in practical applications.