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Deep neural networks have demonstrated advanced abilities on various visual classification tasks, which heavily rely on the large-scale training samples with annotated ground-truth. However, it is unrealistic always to require such annotation in real-world applications. Recently, Few-Shot learning (FS), as an attempt to address the shortage of training samples, has made significant progress in generic classification tasks. Nonetheless, it is still challenging for current FS models to distinguish the subtle differences between fine-grained categories given limited training data. To filling the classification gap, in this paper, we address the Few-Shot Fine-Grained (FSFG) classification problem, which focuses on tackling the fine-grained classification under the challenging few-shot learning setting. A novel low-rank pairwise bilinear pooling operation is proposed to capture the nuanced differences between the support and query images for learning an effective distance metric. Moreover, a feature alignment layer is designed to match the support image features with query ones before the comparison. We name the proposed model Low-Rank Pairwise Alignment Bilinear Network (LRPABN), which is trained in an end-to-end fashion. Comprehensive experimental results on four widely used fine-grained classification datasets demonstrate that our LRPABN model achieves the superior performances compared to state-of-the-art methods.

In this paper, we study the fine-grained categorization problem under the few-shot setting, i.e., each fine-grained class only contains a few labeled examples, termed Fine-Grained Few-Shot classification (FGFS). The core predicament in FGFS is the high intra-class variance yet low inter-class fluctuations in the dataset. In traditional fine-grained classification, the high intra-class variance can be somewhat relieved by conducting the supervised training on the abundant labeled samples. However, with few labeled examples, it is hard for the FGFS model to learn a robust class representation with the significantly higher intra-class variance. Moreover, the inter-and intra-class variance are closely related. The significant intra-class variance in FGFS often aggravates the low inter-class variance issue.To address the above challenges, we propose a Target-Oriented Alignment Network (TOAN) to tackle the FGFS problem from both intra-and inter-class perspective. To reduce the intra-class variance, we propose a target-oriented matching mechanism to reformulate the spatial features of each support image to match the query ones in the embedding space. To enhance the inter-class discrimination, we devise discriminative fine-grained features by integrating local compositional concept representations with the global second-order pooling. We conducted extensive experiments on four public datasets for fine-grained categorization, and the results show the proposed TOAN obtains the state-of-the-art.

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The predicament in semi-supervised few-shot learning (SSFSL) is to maximize the value of the extra unlabeled data to boost the few-shot learner. In this paper, we propose a Poisson Transfer Network (PTN) to mine the unlabeled information for SSFSL from two aspects. First, the Poisson Merriman–Bence–Osher (MBO) model builds a bridge for the communications between labeled and unlabeled examples. This model serves as a more stable and informative classifier than traditional graph-based SSFSL methods in the message-passing process of the labels. Second, the extra unlabeled samples are employed to transfer the knowledge from base classes to novel classes through contrastive learning. Specifically, we force the augmented positive pairs close while push the negative ones distant. Our contrastive transfer scheme implicitly learns the novel-class embeddings to alleviate the over-fitting problem on the few labeled data. Thus, we can mitigate the degeneration of embedding generality in novel classes. Extensive experiments indicate that PTN outperforms the state-of-the-art few-shot and SSFSL models on miniImageNet and tieredImageNet benchmark datasets.

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