Learning to Crop Professional Photos by Outpainting and Leveraging Stock Image Data

Konsep Inti

A weakly-supervised approach to learn subject-aware image cropping from a large collection of professional stock photos, without requiring manual annotations beyond the existing stock image dataset.

Abstrak

The paper proposes a weakly-supervised method, GenCrop, to learn subject-aware image cropping from a large collection of professional stock photos. The key challenge is that the stock images are already cropped, and the original uncropped versions are unknown.
The authors address this by combining the stock image dataset with a pre-trained text-to-image diffusion model. The stock images serve as pseudo-labels for good crops, and the diffusion model is used to "outpaint" (i.e., generate plausible uncropped versions) the stock images. This allows the authors to automatically generate a large dataset of cropped-uncropped image pairs to train a cropping model.
The cropping model is designed to be subject-aware, taking both the input image and a subject mask as input. The model uses a CNN feature extractor, a transformer-encoder, and a composition branch to predict the final crop.
The authors evaluate GenCrop on existing subject-aware cropping benchmarks as well as new evaluation sets they created for different subject categories (humans, cats, dogs, birds, horses, cars). GenCrop performs competitively with fully-supervised methods while being superior to comparable weakly-supervised baselines. Qualitative evaluation also shows that GenCrop produces fewer compositional errors compared to prior weakly-supervised approaches.
The authors also explore extending GenCrop to allow conditional control over the crop aspect ratio and tightness, demonstrating the flexibility of their approach.

Statistik

"We filter for images that include an identifiable subject (e.g., person in portraiture; Fig. 2a). This is done with metadata tags first and then with an object detector (Ultralytics 2023)."
"We randomly downscale the image with bilinear interpolation and paste it into a surrounding 512×512 canvas to obtain an image x (Fig. 2c)."
"We also compute a binary mask m with 1's in the area corresponding to valid pixels."

Kutipan

"Our proposed method, GenCrop, addresses this challenge by combining a readily available dataset of stock images with powerful, pre-trained image generation models to synthesize the required inputs."
"The key advantage of GenCrop is that it is weakly-supervised, requiring no new manual crop or scoring annotations beyond access to the original professional image collection."

Wawasan Utama Disaring Dari

Learning Subject-Aware Cropping by Outpainting Professional Photos

by Jame... pada arxiv.org 04-05-2024

https://arxiv.org/pdf/2312.12080.pdf

Pertanyaan yang Lebih Dalam

How could the quality of the outpainted images be further improved to reduce artifacts and better match the distribution of the original stock photos?

To enhance the quality of the outpainted images and reduce artifacts, several strategies can be implemented:

Fine-tuning the Pre-trained Model: Fine-tuning the pre-trained diffusion model on a dataset that closely matches the distribution of the original stock photos can help improve the quality of the outpainted images. This process can help the model better understand the specific characteristics and nuances of the stock photo collection.

Data Augmentation: Introducing data augmentation techniques during the outpainting process can help generate more diverse and realistic uncropped images. Techniques such as rotation, scaling, and adding noise can contribute to creating more natural-looking outpainted images.

Artifact Detection and Correction: Implementing post-processing techniques to detect and correct artifacts in the outpainted images can further enhance their quality. This can involve using image processing algorithms to identify and remove any inconsistencies or distortions in the generated images.

Feedback Loop: Establishing a feedback loop where the quality of the outpainted images is evaluated, and the model is adjusted based on the feedback can lead to continuous improvement in the generation process. This iterative approach can help refine the outpainting process over time.

Domain-specific Training: Training the model on a dataset that specifically focuses on the subject matter of the stock photos can help align the outpainted images more closely with the original distribution. This targeted training can improve the relevance and quality of the generated images.

What other types of spatial tasks, beyond cropping, could benefit from a similar weakly-supervised data generation approach using pre-trained diffusion models?

Several spatial tasks beyond cropping could benefit from a weakly-supervised data generation approach using pre-trained diffusion models:

Object Localization: Weakly-supervised object localization tasks, where the goal is to identify the location of objects within an image without precise annotations, could benefit from this approach. By generating synthetic data with known object locations, the model can learn to localize objects in an image.

Semantic Segmentation: Tasks that involve segmenting an image into different semantic regions could leverage weakly-supervised data generation. By creating synthetic images with labeled semantic segments, the model can learn to segment images without the need for extensive manual annotations.

Image Inpainting: Image inpainting tasks, where missing or damaged parts of an image need to be filled in, can also benefit from this approach. Generating synthetic data with intentionally removed regions can help the model learn to inpaint accurately.

Image Translation: Tasks that involve translating an image from one domain to another, such as style transfer or domain adaptation, could be improved using weakly-supervised data generation. By creating synthetic images representing different domains, the model can learn to translate images effectively.

Image Super-resolution: Image super-resolution tasks, where low-resolution images are converted to high-resolution versions, could also benefit. Generating synthetic low-resolution images from high-resolution ones can help the model learn to enhance image quality.

Could the cropping model be extended to handle more complex scene compositions, such as images with multiple subjects or occluded objects, beyond the single-subject scenarios explored in this work?

Yes, the cropping model could be extended to handle more complex scene compositions beyond single-subject scenarios by incorporating additional features and modifications:

Multi-Subject Detection: Introducing a multi-object detection component to the model can enable it to identify and handle images with multiple subjects. This would involve detecting and considering the spatial relationships between different subjects in the image during the cropping process.

Object Occlusion Handling: Implementing techniques to address object occlusion can help the model deal with scenarios where objects are partially hidden or overlapped in the image. By understanding and accounting for occlusions, the model can generate more accurate and contextually relevant crops.

Semantic Segmentation Integration: Integrating semantic segmentation capabilities into the model can provide a more detailed understanding of the image content. This can help in identifying different objects and regions within the scene, allowing for more precise and context-aware cropping decisions.

Attention Mechanisms: Incorporating attention mechanisms into the model architecture can enhance its ability to focus on relevant regions of the image, especially in complex compositions. This can improve the model's capacity to handle diverse scene compositions effectively.

Hierarchical Cropping: Implementing a hierarchical cropping approach where the model first identifies key regions or subjects in the image and then refines the cropping based on these regions can be beneficial for handling complex scene compositions. This hierarchical strategy can improve the model's adaptability to various scene complexities.