  Learning multi-label image recognition with incomplete annotation is gaining
popularity due to its superior performance and significant labor savings when
compared to training with fully labeled datasets. Existing literature mainly
focuses on label completion and co-occurrence learning while facing
difficulties with the most common single-positive label manner. To tackle this
problem, we present a semantic contrastive bootstrapping (Scob) approach to
gradually recover the cross-object relationships by introducing class
activation as semantic guidance. With this learning guidance, we then propose a
recurrent semantic masked transformer to extract iconic object-level
representations and delve into the contrastive learning problems on multi-label
classification tasks. We further propose a bootstrapping framework in an
Expectation-Maximization fashion that iteratively optimizes the network
parameters and refines semantic guidance to alleviate possible disturbance
caused by wrong semantic guidance. Extensive experimental results demonstrate
that the proposed joint learning framework surpasses the state-of-the-art
models by a large margin on four public multi-label image recognition
benchmarks. Codes can be found at https://github.com/iCVTEAM/Scob.
