Cycle-Object Consistency for Image-to-Image Domain Adaptation

Artikel i vetenskaplig tidskrift, 2023

Recent advances in generative adversarial networks (GANs) have been proven effective in performing domain adaptation for object detectors through data augmentation. While GANs are exceptionally successful, those methods that can preserve objects well in the image-to-image translation task usually require an auxiliary task, such as semantic segmentation to prevent the image content from being too distorted. However, pixel-level annotations are difficult to obtain in practice. Alternatively, instance-aware image-translation model treats object instances and background separately. Yet, it requires object detectors at test time, assuming that off-the-shelf detectors work well in both domains. In this work, we present AugGAN-Det, which introduces Cycle-object Consistency (CoCo) loss to generate instance-aware translated images across complex domains. The object detector of the target domain is directly leveraged in generator training and guides the preserved objects in the translated images to carry target-domain appearances. Compared to previous models, which e.g., require pixel-level semantic segmentation to force the latent distribution to be object-preserving, this work only needs bounding box annotations which are significantly easier to acquire. Next, as to the instance-aware GAN models, our model, AugGAN-Det, internalizes global and object style-transfer without explicitly aligning the instance features. Most importantly, a detector is not required at test time. Experimental results demonstrate that our model outperforms recent object-preserving and instance-level models and achieves state-of-the-art detection accuracy and visual perceptual quality.