Boosting feature representation for enhanced unsupervised landmark detection

Landmark detection is a crucial task that focuses on identifying specific landmarks representing distinct object features within an image. However, existing methods primarily adopt Convolutional Neural Networks as the backbone for feature extraction, which struggle to capture global contextual information. Due to the structural dependencies of landmarks, these methods fail to effectively utilize these relationships, leading to lower detection accuracy. To overcome these limitations, we propose a novel framework, CTBN, which combines CNNs, Transformer, and Convolutional Block Attention Modules to enhance feature representation for landmark detection. In our framework, CNNs extract fine-grained local features, Transformer captures global context, and CBAM adaptively refinesfeatures by focusing on important regions and channels. We validate our performance on multiple datasets, including CelebA, AFLW and Deepfashion. The experiments demonstrate that our method achieves superior performance, significantly improves the accuracy of landmark detection.