Clustered-patch Element Connection for Few-shot Learning

• URL: http://arxiv.org/abs/2304.10093v3
• Date: Wed, 17 Jul 2024 03:51:29 GMT
• Title: Clustered-patch Element Connection for Few-shot Learning
• Authors: Jinxiang Lai, Siqian Yang, Junhong Zhou, Wenlong Wu, Xiaochen Chen, Jun Liu, Bin-Bin Gao, Chengjie Wang,
• Abstract summary: We propose a novel Clustered-patch Element Connection layer to correct the mismatch problem. Our CECNet outperforms the state-of-the-art methods on classification benchmark.
• Score: 29.94768391960718
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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