Self-Enhanced R-CNNs for Human Detection with Semi-Supervised assumptions

Abstract

Vision-based human detection is a fundamental task in visual content analysis. It has a wide range of applications, especially for person search and retrieval. To reduce the reliance of detection models on large amount of labeled data, we modify Faster R-CNN to facilitate semi-supervised human detection. Specifically, a Reliability analysis (Ra) module is included as an add-on into our Self-Enhanced R-CNN (SE-RCNN) model. The unlabeled images can be pseudo-annotated reliably under the help of this module. as a result, both labeled and unlabeled data are fed simultaneously for model optimization. The additional supervision, in turn, guides the training of a detection module in our model. The two aspects, extracting precise proposals and generating reliable pseudo annotations, can be mutually reinforced. Unlike previous related works, it is the first attempt to build a single-stage semi-supervised human detection model. In our experiment, we observe that the Ra module plays an important role in exploiting unlabeled data and leads to state-of-the-art results of SE-RCNN on multiple benchmarks.