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Yanjiang Wang

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3 papers

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EAAI Journal 2024 Journal Article

Ensembling Multi-View Discriminative Semantic Feature for Few-Shot Classification

Rui Xu
Shuai Shao
Lei Xing
Yanjiang Wang
Baodi Liu
Weifeng Liu

Few-Shot Classification (FSC) is an innovative application in machine learning. FSC consists of two main components: (1) Pre-training, where a feature extraction model (FEM) is trained using base data, and (2) Meta-testing, where the FEM is utilized to extract features from novel data (with categories different from the base data) for classification. Implementing FSC presents several challenges. For example, due to the cross-domain limitation (base data → novel data), the FEM may generate inappropriate features for new classes, leading to a Sample-Feature-Mismatch problem, and the corresponding feature is dubbed as Original-Shift-Feature (OSF). This paper proposes a generative method to construct Multi-View Discriminative Semantic Feature (MVDSF) to address the fundamental problem from the perspective of improving the discriminability of OSF. Typically, a linear projection is designed to transform OSF into a semantic space, generating Discriminative-Semantic-Features (DSF). By incorporating a reconstructive representation that is not solely reliant on the FEM, the influence of the Sample-Feature-Mismatch problem is reduced. Furthermore, considering that descriptions based on a single DSF tend to be one-sided, an attention mechanism is devised to fuse multi-view DSF, thereby improving the robustness of the proposed method. The effectiveness of MVDSF is evaluated on five benchmark few-shot learning datasets, where it achieves outstanding performance. This evaluation demonstrates the efficiency and performance of the proposed approach.

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EAAI Journal 2024 Journal Article

Feedback-Irrelevant Mapping: An evaluation method for decoupled few-shot classification

Rui Xu
Shuai Shao
Lei Xing
Yanjiang Wang
Baodi Liu
Weifeng Liu

Few-shot classification (FSC) has become a significant area of research in recent years. A prevalent method in this field is the separation of feature representations from classifiers, involving two key steps: pre-training a feature extractor on a large base dataset and then applying it to novel, category-diverse data with limited samples. The ability of the pre-trained extractor to adapt to new data is vital for effective feature discrimination and classification performance. Traditional evaluation techniques, such as Class Activation Mapping (CAM), encounter challenges due to the lack of back-propagation when extracting features from novel classes. To address this, we present Feedback-Irrelevant Mapping (FIM), an approach based on dictionary learning theory. FIM utilizes channel weights in feature maps for both visualization and performance evaluation of the extractor on new data. We also introduce two quantitative metrics, Visual Localization Accuracy (VLA) and Visual Intersection Accuracy (VIA), to enable consistent assessments. Our experiments demonstrate that FIM not only preserves the model’s performance but can also improve it by approximately 0. 5%. When applied to various decoupled FSC frameworks, FIM effectively evaluates their capabilities. An important finding is that larger pre-trained models do not always equate to superior FSC results. For instance, models with a ResNet-12 backbone have shown to surpass others by at least 1. 7%.

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EAAI Journal 2023 Journal Article

CSN: Component supervised network for few-shot classification

Rui Xu
Shuai Shao
Lei Xing
Yujun Wei
Weifeng Liu
Baodi Liu
Yanjiang Wang

The few-shot classification (FSC) task aims to classify data with limited labeled examples across different categories. Typically, researchers pre-train a feature extractor using base data and use it to extract features from novel data for classification Notably, the novel set only has a few annotated samples and has non-overlapped categories from the base set, which leads to the fact that the pre-trained feature extractor cannot adapt to the novel data flawlessly. We dub this problem as Feature-Extractor-Maladaptive (FEM) problem. Starting from the root cause of this problem, this paper presents a new scheme called Component Supervised Network (CSN), to improve the performance of FSC. We believe that even though the categories in the base and novel sets are different, their sample components share similarities. For example, both cats and dogs contain leg and head components. These entity components are intra-class stable and have cross-category versatility, making them useful for generalization to new categories. However, finding common information among different categories is difficult in real-world scenarios, hindering the possibility of modeling based on this assumption. To overcome this, we first design a Dictionary-based Implicit-Component Generator (DICG) to mine common information of different sets; then construct an implicit-component-based auxiliary task to improve the adaptability of the feature extractor. We evaluated our CSN on three benchmark datasets (mini-ImageNet, tiered-ImageNet, and FC100) and achieved improvements of at least 0. 9% compared to classical methods, demonstrating the efficiency of our approach.

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