通过随机筛选关键图像块，提升少样本食物识别准确率
Stochastic-based Patch Filtering for Few-Shot Learning
- 基于随机过滤机制，保留与类别相关性高的图像块
- 在三个食物数据集上超越现有最先进方法
- 适合处理视觉复杂度高的少样本图像分类任务
食物图像因视觉复杂性和多样性给少样本学习带来挑战。例如，意面菜品可能搭配不同配菜，处于不同光照和拍摄角度。这导致在对比查询图像与支持图像时难以聚焦关键特征，造成误分类。为此，我们提出基于随机的图像块过滤方法（SPFF），优先关注与类别表示相关性更高的图像块嵌入。SPFF的核心思想是：与类别感知嵌入相似度低的图像块更易被随机剔除。通过根据出现概率过滤图像块嵌入，并构建查询图像与对应支持图像间的相似性矩阵，实现对关键特征区域的聚焦。定性分析表明，SPFF能有效定位类别特异性食物特征显著的图像块，同时滤除无关区域。我们在食品-101、VireoFood-172 和 UECFood-256 三个少样本分类基准上进行了充分实验，结果表明该方法优于现有最先进方法。
原文摘要 · Abstract (English)
Food images present unique challenges for few-shot learning models due to their visual complexity and variability. For instance, a pasta dish might appear with various garnishes on different plates and in diverse lighting conditions and camera perspectives. This problem leads to losing focus on the most important elements when comparing the query with support images, resulting in misclassification. To address this issue, we propose Stochastic-based Patch Filtering for Few-Shot Learning (SPFF) to attend to the patch embeddings that show greater correlation with the class representation. The key concept of SPFF involves the stochastic filtering of patch embeddings, where patches less similar to the class-aware embedding are more likely to be discarded. With patch embedding filtered according to the probability of appearance, we use a similarity matrix that quantifies the relationship between the query image and its respective support images. Through a qualitative analysis, we demonstrate that SPFF effectively focuses on patches where class-specific food features are most prominent while successfully filtering out non-relevant patches. We validate our approach through extensive experiments on few-shot classification benchmarks: Food-101, VireoFood-172 and UECFood-256, outperforming the existing SoA methods.
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