In few-shot fine-grained recognition (FS-FGR) tasks, the main challenge is to distinguish novel categories with high intra-class variations and low inter-class differences given scarce training data. Existing studies explore discriminative features through a compact network to avoid overfitting, while they achieve marginal performance gain owing to the limited representation capability. Motivated by the significant progress of the vision foundation model, we introduce it to describe visual attributes and boost the performance of the compact feature extractor. A few-shot fine-grained recognition method with Variational Feature Imitation Conditioned on Visual Descriptions, VFI-CVD for short, has been proposed in this paper. It simultaneously exploits the pre-trained knowledge from a vision foundation model and the expert knowledge mined by a feature extractor. Specifically, the intra-class variations shared across object categories are encoded into a common distribution thus we can augment features by sampling latent variables. To enhance the learning of intra-class variations, a condition exchange strategy (CES) is put forward to interact the knowledge between samples through feature cross-imitation. In the inference stage, the learned knowledge is further integrated through the joint prediction of visual descriptions and cross-imitated features. Comprehensive experimental results on four fine-grained benchmark datasets show that the proposed VFI-CVD achieves state-of-the-art performance, e.g., 90.37% under the 5-way 1-shot setting on CUB-200-2011. It surpasses existing methods by a large margin, especially in the challenging 30-way recognition tasks and cross-domain evaluation. The source code is publicly available:https://github.com/Lx-zjwf/VFI-CVD.

In recent years, hashing methods have been popular in the large-scale media search for low storage and strong representation capabilities. To describe objects with similar overall appearance but subtle differences, more and more studies focus on hashing-based fine-grained image retrieval. Existing hashing networks usually generate both local and global features through attention guidance on the same deep activation tensor, which limits the diversity of feature representations. To handle this limitation, we substitute convolutional descriptors for attention-guided features and propose an Attributes Grouping and Mining Hashing (AGMH), which groups and embeds the category-specific visual attributes in multiple descriptors to generate a comprehensive feature representation for efficient fine-grained image retrieval. Specifically, an Attention Dispersion Loss (ADL) is designed to force the descriptors to attend to various local regions and capture diverse subtle details. Moreover, we propose a Stepwise Interactive External Attention (SIEA) to mine critical attributes in each descriptor and construct correlations between fine-grained attributes and objects. The attention mechanism is dedicated to learning discrete attributes, which will not cost additional computations in hash codes generation. Finally, the compact binary codes are learned by preserving pairwise similarities. Experimental results demonstrate that AGMH consistently yields the best performance against state-of-the-art methods on fine-grained benchmark datasets.

Smoke in remote sensing (RS) images is regarded as the indicator of fire disasters and it is vital to distinguish smoke from other RS scenes. Convolutional neural network (CNN) based classification networks have viewed great success but the cross-entropy loss may suffer from domain gap when training data and testing data do not follow independent and identical distributions. Served as an objective function, the optimal transport (OT) distance is able to tackle the domain gap by aligning two distributions in domain adaptation tasks. Generally speaking, the OT-based algorithms can be divided into two categories: methods that compute OT distance on minibatches, and methods which rely on the adversarial training. In this letter, we propose a hybrid OT algorithm, termed Joint MiniBatch and Adversarial Optimal Transport (JMBAOT), to eliminate the domain gap for cross-scene classification of RS smoke-related scenes. JMBAOT takes advantages of both the minibatch and adversarial OT to better distinguish different classes on the feature space and improve the performance of domain adaptation. In a large amount of experiments, JMBAOT shows its effectiveness and achieves the state-of-the-art (SOTA) performance.