TY  - JOUR
AU  - Jose L. Gomez
AU  - Gabriel Villalonga
AU  - Antonio Lopez
PY  - 2021//
TI  - Co-Training for Deep Object Detection: Comparing Single-Modal and Multi-Modal Approaches
T2  - SENS
JO  - Sensors
SP  - 3185
VL  - 21
IS  - 9
KW  - co-training
KW  - multi-modality
KW  - vision-based object detection
KW  - ADAS
KW  - self-driving
N2  - 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 par, at least when using an off-the-shelf depth estimation model not specifically trained on the translated images.
UR  - https://doi.org/10.3390/s21093185
L1  - http://refbase.cvc.uab.es/files/GVL2021.pdf
N1  - ADAS; 600.118
ID  - Jose L. Gomez2021
ER  -