Abstract This work aims to build pixel-to-pixel correspondences between images from the same visual class but with different geometries and visual similarities. This task is particularly challenging because (i) their visual content is similar only on the high-level structure, and (ii) background clutters keep bringing in noises. To address these problems, this paper proposes an object-aware method to estimate per-pixel correspondences from semantic to low-level by learning a classifier for each selected discriminative grid cell and guiding the localization of every pixel under the semantic constraint. Specifically, an Object-aware Hierarchical Graph (OHG) model is constructed to regulate matching consistency from one coarse grid cell containing whole object(s), to fine grid cells covering smaller semantic elements, and finally to every pixel. A guidance layer is introduced as the semantic constraint on local structure matching. In addition, we propose to learn the important high-level structure for each grid cell in an “objectness-driven” way as an alternative to handcrafted descriptors in defining a better visual similarity. The proposed method has been extensively evaluated on various challenging benchmarks and real-world images. The results show that our method significantly outperforms the state-of-the-arts in terms of semantic flow accuracy