Evaluating Object Remover Performance Using Class-wise Object Removal Images

Core Concepts

Current object remover performance evaluation methods using original images as references are not suitable for measuring the quality of object removal results. Novel evaluation methods that utilize class-wise object removal results and images without target class objects can properly assess the performance of an object remover.

Abstract

The content discusses methods for evaluating the performance of object removers, which are used to erase designated objects from an image while preserving the overall appearance.
The key points are:

Current evaluation methods that use original images as references cannot properly measure the quality of object removal results, as an original image and an object removal result differ in the presence of the removal target.

To validate this, the authors generate a dataset with object removal ground truth (GT) using virtual environments and compare the evaluations made by current full-reference (FR) methods using original images to those utilizing the object removal GT images. The disparities between the two evaluation sets confirm that the current FR methods are not suitable for measuring object remover performance.

The authors propose new unpaired evaluation methods that assess the performance of an object remover using class-wise object removal tasks and images without the target class objects as a comparison set. These proposed methods, FID* and U-IDS*, can produce evaluations consistent with human judgments on the COCO dataset and align with evaluations using object removal GT in the self-acquired virtual environment dataset.

Experiments show that the proposed methods can properly evaluate object remover performance regardless of the input images' style, enabling their application to model selection during object remover training or performance assessment of off-the-shelf object removers.

Stats

"Object removal is one area where image inpainting is extensively used in real-world applications."
"We generate a dataset with object removal ground truth (GT) using virtual environments."
"We build six object removers using an inpainting model and six different types of masks."

Quotes

"The disparities between the two evaluation sets validate that the current methods are not suitable for measuring the performance of an object remover."
"The proposed methods measure the performance of an object remover using class-wise object removal results and the comparison set composed of images without target class objects."
"Experiments on the images from various environments demonstrate that the proposed methods can properly evaluate the object remover performance regardless of the input images' style."

Key Insights Distilled From

Object Remover Performance Evaluation Methods using Class-wise Object Removal Images

by Changsuk Oh,... at arxiv.org 04-18-2024

https://arxiv.org/pdf/2404.11104.pdf

Deeper Inquiries

How can the proposed evaluation methods be extended to handle more complex object removal scenarios, such as removing multiple objects of different classes simultaneously

To extend the proposed evaluation methods to handle more complex object removal scenarios involving multiple objects of different classes simultaneously, a hierarchical approach can be adopted. This approach would involve segmenting the image into different object classes and then evaluating the object removal performance for each class individually. By generating class-wise object removal results and utilizing images without the target class objects as a comparison set for each class, the performance of the object remover can be assessed comprehensively. This hierarchical evaluation method would provide insights into how well the object remover handles the removal of multiple objects of different classes simultaneously, allowing for a more nuanced and detailed assessment of its performance.

What are the potential limitations or drawbacks of using class-wise object removal and images without target class objects as the basis for evaluating object remover performance

While the proposed class-wise object removal evaluation methods offer a novel approach to assessing object remover performance, there are potential limitations and drawbacks to consider. One limitation is the reliance on semantic segmentation annotations to designate the target class objects for removal. This dependency may introduce biases or inaccuracies in the evaluation process, especially if the segmentation annotations are not precise or if there are ambiguities in object boundaries. Additionally, using images without target class objects as a comparison set may not fully capture the complexity of real-world scenarios where multiple object classes coexist. This could lead to challenges in evaluating the object remover's performance in more diverse and cluttered environments accurately. Furthermore, the proposed methods may require a large number of samples to ensure reliable evaluations, which could be resource-intensive and time-consuming.

How might the insights from this work on object remover evaluation be applied to the broader field of image generation and manipulation tasks beyond just object removal

The insights gained from this work on object remover evaluation can be applied to a broader range of image generation and manipulation tasks beyond just object removal. For instance, the concept of utilizing class-wise evaluations and comparison sets without specific target classes can be extended to tasks like image inpainting, where the goal is to fill in missing or damaged regions of an image. By adapting the proposed evaluation methods to assess the performance of inpainting models in a class-specific manner, researchers and practitioners can gain a deeper understanding of how well these models handle different types of inpainting tasks. This approach can lead to more targeted improvements in model training and development, ultimately enhancing the quality and realism of generated images across various applications in computer vision and image processing.