Weak feature representation problem has influenced the performance of few-shot classification task for a long time. To alleviate this problem, recent researchers build connections between support and query instances through embedding patch features to generate discriminative representations. However, we observe that there exists semantic mismatches (foreground/ background) among these local patches, because the location and size of the target object are not fixed. What is worse, these mismatches result in unreliable similarity confidences, and complex dense connection exacerbates the problem. According to this, we propose a novel Clustered-patch Element Connection (CEC) layer to correct the mismatch problem. The CEC layer leverages Patch Cluster and Element Connection operations to collect and establish reliable connections with high similarity patch features, respectively. Moreover, we propose a CECNet, including CEC layer based attention module and distance metric. The former is utilized to generate a more discriminative representation benefiting from the global clustered-patch features, and the latter is introduced to reliably measure the similarity between pair-features. Extensive experiments demonstrate that our CECNet outperforms the state-of-the-art methods on classification benchmark. Furthermore, our CEC approach can be extended into few-shot segmentation and detection tasks, which achieves competitive performances.

翻译：弱特征表示问题长期影响着小样本分类任务的性能。为缓解该问题，近期研究者通过嵌入补丁特征构建支持样本与查询样本之间的连接，以生成判别性表示。然而，我们观察到这些局部补丁之间存在语义不匹配（前景/背景）现象，因为目标物体的位置和尺寸并非固定。更严重的是，此类不匹配导致相似度置信度不可靠，而复杂的密集连接进一步加剧了该问题。据此，我们提出一种新型的聚类补丁元素连接（CEC）层以修正不匹配问题。CEC层分别利用补丁聚类操作和元素连接操作收集并建立与高相似度补丁特征的可靠连接。此外，我们提出CECNet，包含基于CEC层的注意力模块和距离度量模块。前者利用全局聚类补丁特征生成更具判别性的表示，后者则用于可靠度量成对特征间的相似性。大量实验表明，我们的CECNet在分类基准测试中优于现有最先进方法。更重要的是，CEC方法可扩展至小样本分割与检测任务，并取得了具有竞争力的性能。