Papers › Multi-level Metric Learning for Few-shot Image Recognition
Multi-level Metric Learning for Few-shot Image Recognition
Haoxing Chen, Huaxiong Li, Yaohui Li, Chunlin Chen
Few-shot learning is devoted to training a model on few samples. Most of these approaches learn a model based on a pixel-level or global-level feature representation. However, using global features may lose local information, and using pixel-level features may lose the contextual semantics of the image. Moreover, such works can only measure the relations between them on a single level, which is not comprehensive and effective. And if query images can simultaneously be well classified via three distinct level similarity metrics, the query images within a class can be more tightly distributed in a smaller feature space, generating more discriminative feature maps. Motivated by this, we propose a novel Part-level Embedding Adaptation with Graph (PEAG) method to generate task-specific features. Moreover, a Multi-level Metric Learning (MML) method is proposed, which not only calculates the pixel-level similarity but also considers the similarity of part-level features and global-level features. Extensive experiments on popular few-shot image recognition datasets prove the effectiveness of our method compared with the state-of-the-art methods. Our code is available at \url{https://github.com/chenhaoxing/M2L}.
Code
No code repository is listed for this paper in the archive or in Syntology's graph.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Few-Shot Image Classification | Stanford Cars 5-way (1-shot) | MML(KL) | Accuracy | 72.43 | #2 of 6 | Archive leaderboard | report |
| Few-Shot Image Classification | Stanford Cars 5-way (5-shot) | MML(KL) | Accuracy | 91.05 | #2 of 6 | Archive leaderboard | report |
| Few-Shot Image Classification | Stanford Dogs 5-way (1-shot) | MML(KL) | Accuracy | 59.05 | #1 of 3 | Archive leaderboard | report |
| Few-Shot Image Classification | Stanford Dogs 5-way (5-shot) | MML(KL) | Accuracy | 75.59 | #1 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.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections