ĭense light field sampling is an important basis for refocusing, depth estimation and 3-D imaging. The dataset and code are available at this. Furthermore, because our model does not generate a text stroke mask explicitly, there is no need for additional refinement steps or sub-models, making our model extremely fast with fewer parameters. Experimental results on the benchmark dataset show that our method significantly outperforms existing state-of-the-art methods in almost all metrics with remarkably higher-quality results. RoIG is applied to focus on only the region with text instead of the entire image to train the model more efficiently. GA uses attention to focus on the text stroke as well as the textures and colors of the surrounding regions to remove text from the input image much more precisely. We also introduce a simple yet extremely effective Gated Attention (GA) and Region-of-Interest Generation (RoIG) methodology in this paper. We use the same standardized training/testing dataset to evaluate the performance of several previous methods after standardized re-implementation. While there are a variety of different methods for STR actively being researched, it is difficult to evaluate superiority because previously proposed methods do not use the same standardized training/evaluation dataset. Scene text removal (STR), a task of erasing text from natural scene images, has recently attracted attention as an important component of editing text or concealing private information such as ID, telephone, and license plate numbers. The experimental results suggest that GradMix is able to improve the performance of nuclei segmentation and classification in imbalanced pathology image datasets. We employed two datasets to evaluate the effectiveness of GradMix. This allows us to generate realistic rare-class nuclei with varying environments. As it combines two nuclei, GradMix considers both nuclei and the neighboring environment by using the customized mixing mask. GradMix takes a pair of a major-class nucleus and a rare-class nucleus, creates a customized mixing mask, and combines them using the mask to generate a new rare-class nucleus. In this paper, we propose a simple but effective data augmentation technique, termed GradMix, that is specifically designed for nuclei segmentation and classification. However, the existing datasets are imbalanced among different types of nuclei in general, leading to a substantial performance degradation. The current deep learning-based approaches require a vast amount of annotated datasets by pathologists. The excellent experimental results demonstrate the authenticity and reliability of the STTTN.Īn automated segmentation and classification of nuclei is an essential task in digital pathology. To demonstrate the advancedness and effectiveness of the proposed model, we conduct comprehensive ablation learning and qualitative and quantitative experiments on multiple datasets by using standard stationary masks and more realistic moving object masks. Such a design encourages joint feature learning across the input and reference frames. STTTN consists of six closely related modules optimized for video inpainting tasks: feature similarity measure for more accurate frame pre-repair, an encoder with strong information extraction ability, embedding module for finding a correlation, coarse low-frequency feature transfer, refinement high-frequency feature transfer, and decoder with accurate content reconstruction ability. In this paper, we propose a novel and effective spatial-temporal texture transformer network (STTTN) for video inpainting. However, existing video inpainting approaches neglect the ability of the model to extract information and reconstruct the content, resulting in the inability to reconstruct the textures that should be transferred accurately. Recent progress has been made by taking other frames as references so that relevant textures can be transferred to damaged frames. We study video inpainting, which aims to recover realistic textures from damaged frames.
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