  Feature representation via self-supervised learning has reached remarkable
success in image-level contrastive learning, which brings impressive
performances on image classification tasks. While image-level feature
representation mainly focuses on contrastive learning in single instance, it
ignores the objective differences between pretext and downstream prediction
tasks such as object detection and instance segmentation. In order to fully
unleash the power of feature representation on downstream prediction tasks, we
propose a new end-to-end self-supervised framework called InsCon, which is
devoted to capturing multi-instance information and extracting cell-instance
features for object recognition and localization. On the one hand, InsCon
builds a targeted learning paradigm that applies multi-instance images as
input, aligning the learned feature between corresponding instance views, which
makes it more appropriate for multi-instance recognition tasks. On the other
hand, InsCon introduces the pull and push of cell-instance, which utilizes cell
consistency to enhance fine-grained feature representation for precise boundary
localization. As a result, InsCon learns multi-instance consistency on semantic
feature representation and cell-instance consistency on spatial feature
representation. Experiments demonstrate the method we proposed surpasses MoCo
v2 by 1.1% AP^{bb} on COCO object detection and 1.0% AP^{mk} on COCO instance
segmentation using Mask R-CNN R50-FPN network structure with 90k iterations,
2.1% APbb on PASCAL VOC objection detection using Faster R-CNN R50-C4 network
structure with 24k iterations.
