Papers › Multi-scale Adaptive Task Attention Network for Few-Shot Learning
Multi-scale Adaptive Task Attention Network for Few-Shot Learning
Haoxing Chen, Huaxiong Li, Yaohui Li, Chunlin Chen
The goal of few-shot learning is to classify unseen categories with few labeled samples. Recently, the low-level information metric-learning based methods have achieved satisfying performance, since local representations (LRs) are more consistent between seen and unseen classes. However, most of these methods deal with each category in the support set independently, which is not sufficient to measure the relation between features, especially in a certain task. Moreover, the low-level information-based metric learning method suffers when dominant objects of different scales exist in a complex background. To address these issues, this paper proposes a novel Multi-scale Adaptive Task Attention Network (MATANet) for few-shot learning. Specifically, we first use a multi-scale feature generator to generate multiple features at different scales. Then, an adaptive task attention module is proposed to select the most important LRs among the entire task. Afterwards, a similarity-to-class module and a fusion layer are utilized to calculate a joint multi-scale similarity between the query image and the support set. Extensive experiments on popular benchmarks clearly show the effectiveness of the proposed MATANet compared with state-of-the-art methods.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Few-Shot Image Classification | CUB-200-2011 5-way (1-shot) | MATANet | Accuracy | 67.33 | #1 of 1 | Archive leaderboard | report |
| Few-Shot Image Classification | CUB-200-2011 5-way (5-shot) | MATANet | Accuracy | 83.92 | #1 of 1 | Archive leaderboard | report |
| Few-Shot Image Classification | Mini-Imagenet 5-way (1-shot) | MATANet | Accuracy | 53.63 | #87 of 105 | Archive leaderboard | report |
| Few-Shot Image Classification | Mini-Imagenet 5-way (5-shot) | MATANet | Accuracy | 72.67 | #75 of 95 | Archive leaderboard | report |
| Few-Shot Image Classification | Stanford Cars 5-way (1-shot) | MATANet | Accuracy | 73.15 | #1 of 6 | Archive leaderboard | report |
| Few-Shot Image Classification | Stanford Cars 5-way (5-shot) | MATANet | Accuracy | 91.89 | #1 of 6 | Archive leaderboard | report |
| Few-Shot Image Classification | Stanford Dogs 5-way (1-shot) | MATANet | Accuracy | 55.63 | #2 of 3 | Archive leaderboard | report |
| Few-Shot Image Classification | Stanford Dogs 5-way (5-shot) | MATANet | Accuracy | 70.29 | #2 of 6 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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