Curriculum Self-Paced Learning for Cross-Domain Object Detection
This addresses domain adaptation for object detection, which is crucial for deploying models in real-world scenarios with varying data distributions, but it appears incremental as it builds on existing methods like Cycle-GAN and self-paced learning.
The paper tackles the problem of source domain bias in object detectors like Faster R-CNN when applied to new target domains, proposing a novel self-paced learning algorithm that improves cross-domain object detection results, showing better performance than state-of-the-art methods on four benchmarks.
Training (source) domain bias affects state-of-the-art object detectors, such as Faster R-CNN, when applied to new (target) domains. To alleviate this problem, researchers proposed various domain adaptation methods to improve object detection results in the cross-domain setting, e.g. by translating images with ground-truth labels from the source domain to the target domain using Cycle-GAN. On top of combining Cycle-GAN transformations and self-paced learning in a smart and efficient way, in this paper, we propose a novel self-paced algorithm that learns from easy to hard. Our method is simple and effective, without any overhead during inference. It uses only pseudo-labels for samples taken from the target domain, i.e. the domain adaptation is unsupervised. We conduct experiments on four cross-domain benchmarks, showing better results than the state of the art. We also perform an ablation study demonstrating the utility of each component in our framework. Additionally, we study the applicability of our framework to other object detectors. Furthermore, we compare our difficulty measure with other measures from the related literature, proving that it yields superior results and that it correlates well with the performance metric.