Establishing semantic correspondence between pairs of images within the same category or similar scenes is challenging due to large intra-class variations. In this paper, we introduce a novel problem termed ’Small Object Semantic Correspondence (SOSC).’ The challenge in SOSC arises from the close proximity of keypoints in small objects, leading to the fusion of their respective features, which complicates accurate keypoint prediction and correspondence identification. To address this issue, we propose the Keypoint Bounding box-centered Cropping (KBC) method, designed to increase the spatial separation between keypoints in small objects, thereby enabling the independent learning of these keypoints. The KBC method is seamlessly integrated into our proposed inference pipeline and can be easily incorporated into other methodologies, resulting in significant performance improvements. Furthermore, we introduce a novel framework called KBCNet, which serves as our baseline model. KBCNet includes a Cross-Scale Feature Alignment (CSFA) module and an efficient 4D convolutional decoder. The CSFA module aligns multi-scale features, enriching keypoint representations by integrating fine-grained details with deep semantic features. Meanwhile, the 4D convolutional decoder, based on efficient 4D convolution, ensures both high efficiency and rapid convergence. To empirically validate the effectiveness of our approach, we conducted extensive experiments on three widely used benchmarks: PF-PASCAL, PF-WILLOW, and SPair-71k. Our KBC method demonstrated a substantial performance improvement of 7.5% on the SPair-71k dataset, providing strong evidence of its efficacy.