Latent representation features in deep learning (DL) exhibit excellent potential for visual data applications. For example, in traffic monitoring and video surveillance, the features simultaneously perform image analysis for machine vision and image reconstruction for human viewing. However, the existing deep features that appeal to machine and human receivers are always combinations of separated pieces and specific features. Due to these features being extracted from different branches in collaboration frameworks, the inherent relations between machine and human vision are insufficiently explored. Therefore, to obtain one set of representative and generic features, we propose a dynamic groupwise splitting network based on image content to explore and extract generic features for the two different receivers. First, we analyze the characteristics of the latent features and adopt intermediate features as the base features. Then, a feature classification and transformation mechanism based on image content is proposed to enhance the base features for further image reconstruction and analysis. Consequently, an end-to-end model with multimodel cascading and multistage training realizes both machine and human vision tasks. Extensive experiments show that our human–machine vision collaboration framework has high practical value and performance.