  Training the multi-label image recognition models with partial labels, in
which merely some labels are known while others are unknown for each image, is
a considerably challenging and practical task. To address this task, current
algorithms mainly depend on pre-training classification or similarity models to
generate pseudo labels for the unknown labels. However, these algorithms depend
on sufficient multi-label annotations to train the models, leading to poor
performance especially with low known label proportion. In this work, we
propose to blend category-specific representation across different images to
transfer information of known labels to complement unknown labels, which can
get rid of pre-training models and thus does not depend on sufficient
annotations. To this end, we design a unified semantic-aware representation
blending (SARB) framework that exploits instance-level and prototype-level
semantic representation to complement unknown labels by two complementary
modules: 1) an instance-level representation blending (ILRB) module blends the
representations of the known labels in an image to the representations of the
unknown labels in another image to complement these unknown labels. 2) a
prototype-level representation blending (PLRB) module learns more stable
representation prototypes for each category and blends the representation of
unknown labels with the prototypes of corresponding labels to complement these
labels. Extensive experiments on the MS-COCO, Visual Genome, Pascal VOC 2007
datasets show that the proposed SARB framework obtains superior performance
over current leading competitors on all known label proportion settings, i.e.,
with the mAP improvement of 4.6%, 4.%, 2.2% on these three datasets when the
known label proportion is 10%. Codes are available at
https://github.com/HCPLab-SYSU/HCP-MLR-PL.

Computer Vision and Pattern Recognition