Research on images multi-objective classification method based on attention mechanism

At present, image processing has become an important method in tremendous downstream applications including unmanned driving, facial recognition, medical detection, etc. However, existing processing methods are concentrated on single targets and leak the ability of disposing multiple objectives especially in complex environment. As far as we can concern, current methods have unacceptable classification accuracy in multiple objectives scenario due to the confounding influence from similar pixels information. In this work, we propose a novel framework to identify multiple targets by extracting features from feature space and not rely on the pixel difference information. Initially, we utilize a supervised contrastive learning (S-CTL) to encourage the feature representation of input images. Subsequently, multiple convolutional neural networks are trained to classify the labels of multiple features with attention mechanism. From our extensive experiment analysis and results, we can significantly observe that our model can outperform existing multi-objectives classification methods with satisfied accuracy and reasonable computation costs.