Research on image description algorithms with integrated attention mechanism

In order to better capture image information and focus more on key areas of images, this paper proposes an image description method based on the encoder-decoder framework that integrates the Selective Kernel (SK) mechanism with a soft attention model. The SK attention adaptively adjusts the weights of convolution kernels at different scales, allowing the model to autonomously select the most suitable kernel for feature extraction based on the content of the image, thus better handling multi-scale information in images. Additionally, this paper integrates a soft attention model, which calculates attention weights for different regions within the image, enabling the model to focus more on areas relevant to the current descriptive vocabulary when generating descriptions, resulting in more accurate and detailed descriptive statements.