  Self-supervised representation learning for visual pre-training has achieved
remarkable success with sample (instance or pixel) discrimination and semantics
discovery of instance, whereas there still exists a non-negligible gap between
pre-trained model and downstream dense prediction tasks. Concretely, these
downstream tasks require more accurate representation, in other words, the
pixels from the same object must belong to a shared semantic category, which is
lacking in the previous methods. In this work, we present Dense Semantic
Contrast (DSC) for modeling semantic category decision boundaries at a dense
level to meet the requirement of these tasks. Furthermore, we propose a dense
cross-image semantic contrastive learning framework for multi-granularity
representation learning. Specially, we explicitly explore the semantic
structure of the dataset by mining relations among pixels from different
perspectives. For intra-image relation modeling, we discover pixel neighbors
from multiple views. And for inter-image relations, we enforce pixel
representation from the same semantic class to be more similar than the
representation from different classes in one mini-batch. Experimental results
show that our DSC model outperforms state-of-the-art methods when transferring
to downstream dense prediction tasks, including object detection, semantic
segmentation, and instance segmentation. Code will be made available.
