Recent representation learning approaches mainly fall into two paradigms: contrastive learning (CL) and masked image modeling (MIM). Combining these two methods may boost the performance, but its learning process still heavily depends on the random masking strategy. We conjecture that the random masking may hinder learning the comprehensive relationship between concept and visual patches. To overcome these limitations, we propose an information-density masking (IDM) strategy for general visual transformers. Specifically, the IDM mask out the visual patches according to their activation values of attention maps. To obtain the attention maps before the reconstruction, a self-supervised training framework CAMAE is further proposed. In addition, in order to reduce the redundancy among different attention maps, we introduce a pattern-learning balance (PLB) sampling to adaptively adjust the learning progress in different attention spaces. Extensive experiments indicate that our method efficiently retains more comprehensive visual characteristics and achieves state-of-the-art performance.