Papers › Relational Embedding for Few-Shot Classification
Relational Embedding for Few-Shot Classification
Dahyun Kang, Heeseung Kwon, Juhong Min, Minsu Cho
We propose to address the problem of few-shot classification by meta-learning "what to observe" and "where to attend" in a relational perspective. Our method leverages relational patterns within and between images via self-correlational representation (SCR) and cross-correlational attention (CCA). Within each image, the SCR module transforms a base feature map into a self-correlation tensor and learns to extract structural patterns from the tensor. Between the images, the CCA module computes cross-correlation between two image representations and learns to produce co-attention between them. Our Relational Embedding Network (RENet) combines the two relational modules to learn relational embedding in an end-to-end manner. In experimental evaluation, it achieves consistent improvements over state-of-the-art methods on four widely used few-shot classification benchmarks of miniImageNet, tieredImageNet, CUB-200-2011, and CIFAR-FS.
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Code
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Code Syntology ran Syntology
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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 | CIFAR-FS 5-way (1-shot) | RENet | Accuracy | 74.51 | #28 of 38 | Archive leaderboard | report |
| Few-Shot Image Classification | CIFAR-FS 5-way (5-shot) | RENet | Accuracy | 86.60 | #28 of 39 | Archive leaderboard | report |
| Few-Shot Image Classification | CUB 200 5-way 1-shot | RENet | Accuracy | 79.49 | #20 of 36 | Archive leaderboard | report |
| Few-Shot Image Classification | CUB 200 5-way 5-shot | RENet | Accuracy | 91.11 | #17 of 32 | Archive leaderboard | report |
| Few-Shot Image Classification | Mini-Imagenet 5-way (1-shot) | RENet | Accuracy | 67.60 | #44 of 105 | Archive leaderboard | report |
| Few-Shot Image Classification | Mini-Imagenet 5-way (5-shot) | RENet | Accuracy | 82.58 | #39 of 95 | Archive leaderboard | report |
| Few-Shot Image Classification | Tiered ImageNet 5-way (1-shot) | RENet | Accuracy | 71.61 | #28 of 49 | Archive leaderboard | report |
| Few-Shot Image Classification | Tiered ImageNet 5-way (5-shot) | RENet | Accuracy | 85.28 | #32 of 51 | 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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