InstaGAN: Instance-aware Image-to-Image Translation (1812.10889v2)

Abstract: Unsupervised image-to-image translation has gained considerable attention due to the recent impressive progress based on generative adversarial networks (GANs). However, previous methods often fail in challenging cases, in particular, when an image has multiple target instances and a translation task involves significant changes in shape, e.g., translating pants to skirts in fashion images. To tackle the issues, we propose a novel method, coined instance-aware GAN (InstaGAN), that incorporates the instance information (e.g., object segmentation masks) and improves multi-instance transfiguration. The proposed method translates both an image and the corresponding set of instance attributes while maintaining the permutation invariance property of the instances. To this end, we introduce a context preserving loss that encourages the network to learn the identity function outside of target instances. We also propose a sequential mini-batch inference/training technique that handles multiple instances with a limited GPU memory and enhances the network to generalize better for multiple instances. Our comparative evaluation demonstrates the effectiveness of the proposed method on different image datasets, in particular, in the aforementioned challenging cases. Code and results are available in https://github.com/sangwoomo/instagan

• The paper introduces an instance-augmented GAN architecture that processes images with segmentation masks to achieve accurate multi-instance transformations.
• The paper leverages a novel context preserving loss to maintain non-target image regions while enabling complex shape changes.
• The paper implements sequential mini-batch training to enhance scalability and demonstrates superior performance compared to CycleGAN across diverse datasets.

An Expert Review of "InstaGAN: Instance-aware Image-to-Image Translation"

The paper "InstaGAN: Instance-aware Image-to-Image Translation" introduces an advanced method to address the challenges of image-to-image translation, particularly in unsupervised scenarios involving multiple instances and significant instance shape transformations. The work leverages generative adversarial networks (GANs) and introduces several novel concepts to enhance the efficiency and reliability of image translations. This review explores the key contributions, numerical results, and implications of this research.

Key Contributions and Methodology

The primary contributions of the paper are threefold:

1. Instance-augmented Neural Architecture: The authors propose a permutation-invariant neural network architecture that concurrently processes an image and its segmentation masks, referred to as instance attributes. This design is critical to ensuring that transformations are contextually accurate and that all instances within an image are appropriately addressed. The architecture capitalizes on the strengths of GANs and is built upon the CycleGAN methods, enabling the translation between domains with minimal information loss.
2. Context Preserving Loss: A key innovation in the paper is the introduction of a context preserving loss function. This loss function is specifically engineered to maintain the background and non-targeted elements of an image unaltered during the transformation process. This approach mitigates issues of false positives and negatives commonly encountered in shape-intensive translations.
3. Sequential Mini-batch Training: Addressing computational constraints is also a significant focus of the research. The proposed sequential mini-batch technique facilitates the processing of multiple instances in a memory-efficient manner, enhancing the scalability and practical applicability of the model to complex image scenarios.

Experimental Evaluation and Results

The authors conduct rigorous experimentation on diverse datasets such as the Clothing Co-Parsing (CCP), multi-human parsing (MHP), and MS COCO datasets. The results underscore the efficacy of InstaGAN in handling challenging multi-instance transfiguration tasks where previous methods, notably CycleGAN, often faltered. Noteworthy improvements are observed in the translation of images involving dynamic transformations such as changing pants into skirts, demonstrating the model’s advanced capacity for shape transformations.

A classification score is used to quantitatively validate the visual outcomes, revealing that InstaGAN consistently achieves higher accuracy compared to CycleGAN, with significant improvements noted on both training and test datasets across various translation tasks.

Implications and Future Directions

InstaGAN's approach has several theoretical and practical implications. From a theoretical standpoint, the model's utilization of set-structured side information and a context-sensitive loss function could inspire novel applications in other domains of cross-domain generation such as neural machine translation and video-to-video transformations. Practically, the ability to transfigure multiple instances accurately indicates potential applications in fashion design, augmented reality, and various visual media industries.

The paper opens several avenues for future research. An intriguing direction would involve integrating temporal coherence for video sequences, potentially improving results further in motion-rich settings. Additionally, exploring advanced applications of segmentation techniques could refine the model’s precision in instance localization and transformation accuracy.

In conclusion, the paper marks a significant stride in the domain of unsupervised image-to-image translation. The thoughtful integration of instance-awareness within the GAN framework signifies a promising evolution in tackling complex visual transformation tasks, likely to influence future developments in the field.