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

• URL: http://arxiv.org/abs/2404.11104v1
• Date: Wed, 17 Apr 2024 06:40:47 GMT
• Title: Object Remover Performance Evaluation Methods using Class-wise Object Removal Images
• Authors: Changsuk Oh, Dongseok Shim, Taekbeom Lee, H. Jin Kim,
• Abstract summary: The performance of an object remover is quantitatively evaluated by measuring the quality of object removal results. We create a dataset with object removal ground truth and compare the evaluations made by the current methods using original images. We propose new evaluation methods tailored to gauge the performance of an object remover.
• Score: 29.28862692741578
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Object removal refers to the process of erasing designated objects from an image while preserving the overall appearance, and it is one area where image inpainting is widely used in real-world applications. The performance of an object remover is quantitatively evaluated by measuring the quality of object removal results, similar to how the performance of an image inpainter is gauged. Current works reporting quantitative performance evaluations utilize original images as references. In this letter, to validate the current evaluation methods cannot properly evaluate the performance of an object remover, we create a dataset with object removal ground truth and compare the evaluations made by the current methods using original images to those utilizing object removal ground truth images. The disparities between two evaluation sets validate that the current methods are not suitable for measuring the performance of an object remover. Additionally, we propose new evaluation methods tailored to gauge the performance of an object remover. The proposed methods evaluate the performance through class-wise object removal results and utilize images without the target class objects as a comparison set. We confirm that the proposed methods can make judgments consistent with human evaluators in the COCO dataset, and that they can produce measurements aligning with those using object removal ground truth in the self-acquired dataset.

Related papers

• Task-Decoupled Image Inpainting Framework for Class-specific Object Remover [26.81218265405809]
  We propose a task-decoupled image inpainting framework which generates two separate inpainting models. We obtain a class-specific object remover which focuses on removing objects of a target class. Experiments show that the proposed class-specific object remover can better remove target class objects than object removers based on image inpainting networks.
  arXiv  Detail & Related papers  (2024-10-03T18:32:33Z)
• Retrieval Robust to Object Motion Blur [54.34823913494456]
  We propose a method for object retrieval in images that are affected by motion blur. We present the first large-scale datasets for blurred object retrieval. Our method outperforms state-of-the-art retrieval methods on the new blur-retrieval datasets.
  arXiv  Detail & Related papers  (2024-04-27T23:22:39Z)
• Counterfactual Reasoning for Multi-Label Image Classification via Patching-Based Training [84.95281245784348]
  Overemphasizing co-occurrence relationships can cause the overfitting issue of the model. We provide a causal inference framework to show that the correlative features caused by the target object and its co-occurring objects can be regarded as a mediator.
  arXiv  Detail & Related papers  (2024-04-09T13:13:24Z)
• AURA : Automatic Mask Generator using Randomized Input Sampling for Object Removal [26.81218265405809]
  In this paper, we focus on generating the input mask to better remove objects using the off-the-shelf image inpainting network. We propose an automatic mask generator inspired by the explainable AI (XAI) method, whose output can better remove objects than a semantic segmentation mask. Experiments confirm that our method shows better performance in removing target class objects than the masks generated from the semantic segmentation maps.
  arXiv  Detail & Related papers  (2023-05-13T07:51:35Z)
• Unsupervised Object Localization: Observing the Background to Discover Objects [4.870509580034194]
  In this work, we take a different approach and propose to look for the background instead. This way, the salient objects emerge as a by-product without any strong assumption on what an object should be. We propose FOUND, a simple model made of a single $conv1times1$ with coarse background masks extracted from self-supervised patch-based representations.
  arXiv  Detail & Related papers  (2022-12-15T13:43:11Z)
• Discovering Objects that Can Move [55.743225595012966]
  We study the problem of object discovery -- separating objects from the background without manual labels. Existing approaches utilize appearance cues, such as color, texture, and location, to group pixels into object-like regions. We choose to focus on dynamic objects -- entities that can move independently in the world.
  arXiv  Detail & Related papers  (2022-03-18T21:13:56Z)
• Contrastive Object Detection Using Knowledge Graph Embeddings [72.17159795485915]
  We compare the error statistics of the class embeddings learned from a one-hot approach with semantically structured embeddings from natural language processing or knowledge graphs. We propose a knowledge-embedded design for keypoint-based and transformer-based object detection architectures.
  arXiv  Detail & Related papers  (2021-12-21T17:10:21Z)
• Text-driven object affordance for guiding grasp-type recognition in multimodal robot teaching [18.529563816600607]
  This study investigates how text-driven object affordance affects image-based grasp-type recognition in robot teaching. They created labeled datasets of first-person hand images to examine the impact of object affordance on recognition performance.
  arXiv  Detail & Related papers  (2021-02-27T17:03:32Z)
• A Simple and Effective Use of Object-Centric Images for Long-Tailed Object Detection [56.82077636126353]
  We take advantage of object-centric images to improve object detection in scene-centric images. We present a simple yet surprisingly effective framework to do so. Our approach can improve the object detection (and instance segmentation) accuracy of rare objects by 50% (and 33%) relatively.
  arXiv  Detail & Related papers  (2021-02-17T17:27:21Z)
• Slender Object Detection: Diagnoses and Improvements [74.40792217534]
  In this paper, we are concerned with the detection of a particular type of objects with extreme aspect ratios, namely textbfslender objects. For a classical object detection method, a drastic drop of $18.9%$ mAP on COCO is observed, if solely evaluated on slender objects.
  arXiv  Detail & Related papers  (2020-11-17T09:39:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.