Semi-Supervised Pedestrian Instance Synthesis And Detection With Mutual Reinforcement
2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2019)(2019)
Abstract
We propose a GAN-based scene-specific instance synthesis and classification model for semi-supervised pedestrian detection. Instead of collecting unreliable detections from unlabeled data, we adopt a class-conditional GAN for synthesizing pedestrian instances to alleviate the problem of insufficient labeled data. With the help of a base detector, we integrate pedestrian instance synthesis and detection by including a post-refinement classifier (PRC) into a minimax game. A generator and the PRC can mutually reinforce each other by synthesizing high-fidelity pedestrian instances and providing more accurate categorical information. Both of them compete with a class-conditional discriminator and a class-specific discriminator, such that the four fundamental networks in our model can be jointly trained. In our experiments, we validate that the proposed model significantly improves the performance of the base detector and achieves state-of-the-art results on multiple benchmarks. As shown in Figure 1, the result indicates the possibility of using inexpensively synthesized instances for improving semi-supervised detection models.
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Key words
semisupervised pedestrian instance synthesis,mutual reinforcement,classification model,semisupervised pedestrian detection,unreliable detections,unlabeled data,class-conditional GAN,insufficient labeled data,base detector,post-refinement classifier,high-fidelity pedestrian instances,class-conditional discriminator,class-specific discriminator,inexpensively synthesized instances,semisupervised pedestrian instance detection models
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