Co-training for Deep Object Detection: Comparing Single-modal and Multi-modal Approaches
This work addresses the data labeling bottleneck in computer vision for researchers and practitioners, offering an incremental improvement in semi-supervised object detection methods.
The paper tackles the problem of reducing human labeling effort for training deep object detectors by using co-training, a semi-supervised learning method, to generate self-labeled bounding boxes. It finds that multi-modal co-training, using RGB and depth views, outperforms single-modal approaches in standard and domain shift settings, with performance gains observed in virtual-to-real scenarios.
Top-performing computer vision models are powered by convolutional neural networks (CNNs). Training an accurate CNN highly depends on both the raw sensor data and their associated ground truth (GT). Collecting such GT is usually done through human labeling, which is time-consuming and does not scale as we wish. This data labeling bottleneck may be intensified due to domain shifts among image sensors, which could force per-sensor data labeling. In this paper, we focus on the use of co-training, a semi-supervised learning (SSL) method, for obtaining self-labeled object bounding boxes (BBs), i.e., the GT to train deep object detectors. In particular, we assess the goodness of multi-modal co-training by relying on two different views of an image, namely, appearance (RGB) and estimated depth (D). Moreover, we compare appearance-based single-modal co-training with multi-modal. Our results suggest that in a standard SSL setting (no domain shift, a few human-labeled data) and under virtual-to-real domain shift (many virtual-world labeled data, no human-labeled data) multi-modal co-training outperforms single-modal. In the latter case, by performing GAN-based domain translation both co-training modalities are on pair; at least, when using an off-the-shelf depth estimation model not specifically trained on the translated images.