CrDoCo: Pixel-level Domain Transfer with Cross-Domain Consistency (2001.03182v1)

Abstract: Unsupervised domain adaptation algorithms aim to transfer the knowledge learned from one domain to another (e.g., synthetic to real images). The adapted representations often do not capture pixel-level domain shifts that are crucial for dense prediction tasks (e.g., semantic segmentation). In this paper, we present a novel pixel-wise adversarial domain adaptation algorithm. By leveraging image-to-image translation methods for data augmentation, our key insight is that while the translated images between domains may differ in styles, their predictions for the task should be consistent. We exploit this property and introduce a cross-domain consistency loss that enforces our adapted model to produce consistent predictions. Through extensive experimental results, we show that our method compares favorably against the state-of-the-art on a wide variety of unsupervised domain adaptation tasks.

• The paper introduces a cross-domain consistency loss that enforces invariant pixel-level predictions across domain-translated images.
• It employs a pixel-wise adversarial domain adaptation algorithm combining image translation and task-specific networks.
• Experiments show significant improvements in semantic segmentation, depth prediction, and optical flow estimation metrics.

Overview of "CrDoCo: Pixel-level Domain Transfer with Cross-Domain Consistency"

The paper "CrDoCo: Pixel-level Domain Transfer with Cross-Domain Consistency," authored by Yun-Chun Chen et al., introduces a novel approach for unsupervised domain adaptation (UDA) in dense prediction tasks such as semantic segmentation, depth prediction, and optical flow estimation. The core contribution of this work is the establishment of a cross-domain consistency loss which enhances the pixel-level transfer by enforcing consistency between the predictions of images and their cross-domain translated counterparts.

Methodology

The authors propose a pixel-wise adversarial domain adaptation algorithm termed CrDoCo, composed of two primary components: an image-to-image translation network and domain-specific task networks. The image translation network leverages data augmentation techniques to transform images from the source domain to appear as if they belong to the target domain, allowing the model to better generalize across domains. The task networks process these augmented images to perform the respective dense prediction tasks.

The innovative aspect of CrDoCo lies in the cross-domain consistency loss. This loss ensures that while images may vary in appearance due to domain translation, the task predictions for corresponding images should remain consistent. Essentially, CrDoCo enforces the premise that the semantic understanding of scenes should remain invariant even when image styles differ due to domain shifts. This is pivotal in reducing the domain gap and improving the model's performance on the target domain without needing target domain labels.

Experimental Results

The paper presents extensive experiments demonstrating CrDoCo's efficacy across various challenging UDA tasks:

1. Semantic Segmentation: The model outperforms previous state-of-the-art methods on synthetic-to-real domain adaptation benchmarks such as GTA5 to Cityscapes and SYNTHIA to Cityscapes. Significant improvements in mean Intersection over Union (IoU) and pixel accuracy are noted, particularly when employing the proposed cross-domain consistency loss.
2. Depth Prediction: The method produces promising results on synthetic-to-real adaptation tasks from SUNCG to NYUv2, illustrating its versatility across differing dense prediction applications.
3. Optical Flow Estimation: CrDoCo demonstrates lower endpoint errors and enhanced performance metrics compared to baseline methods when adapting from the MPI Sintel dataset to real-world KITTI datasets.

Theoretical and Practical Implications

CrDoCo, with its innovative cross-domain consistency loss, addresses a critical challenge in domain adaptation: ensuring model robustness and reliability in the absence of target domain annotations. This technique is broadly applicable across numerous dense prediction tasks and domains, underscoring its versatility and potential impact in fields like autonomous driving, where models are often trained on simulated data. Furthermore, the model's architecture can be extended to real-to-real domain adaptations, indicating possible applicability in dynamic, continuously evolving environments.

Conclusion and Future Directions

The paper enriches the field of unsupervised domain adaptation by introducing a trainable model that bridges the domain gap via pixel-wise and pattern consistency strategies. Future work could explore integrating the cross-domain consistency framework into more complex multi-domain scenarios or optimizing the computationally intensive nature of the task networks. Additionally, incorporating few-shot learning paradigms could offer pathways to further enhance model adaptability and learning efficiency in even more challenging cross-domain settings.