In this paper, we adopt image style migration technique based on deep learning, use Vgg19 network for content and style feature extraction, combine an image with art design style, and realise the generation of Van Gog...
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The gaming industry has predominately exhibited a significant gender disparity, resulting in the portrayal of game characters, including avatars, in a manner that reinforces gender stereotypes. This study aims to inve...
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images acquired in hazy conditions have degradations induced in them. Dehazing such images is a vexed and ill-posed problem. Scores of prior-based and learning-based approaches have been proposed to mitigate the effec...
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ISBN:
(数字)9783031581816
ISBN:
(纸本)9783031581809;9783031581816
images acquired in hazy conditions have degradations induced in them. Dehazing such images is a vexed and ill-posed problem. Scores of prior-based and learning-based approaches have been proposed to mitigate the effect of haze and generate haze-free images. Many conventional methods are constrained by their lack of awareness regarding scene depth and their incapacity to capture long-range dependencies. In this paper, a method that uses residual learning and vision transformers in an attention module is proposed. It essentially comprises two networks: In the first one, the network takes the ratio of a hazy image and the approximated transmission matrix to estimate a residual map. The second network takes this residual image as input and passes it through convolution layers before superposing it on the generated feature maps. It is then passed through global context and depth-aware transformer encoders to obtain channel attention. The attention module then infers the spatial attention map before generating the final haze-free image. Experimental results including several quantitative metrics demonstrate the efficiency and scalability of the suggested methodology.
The aim of this study is to investigate an approach for evaluating the user experience of mobile applications in remote environments. The proposed approach includes both thinking aloud and a user experience questionna...
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To satisfy the needs of thermal power plants for monitoring the boiler air preheater distance between the bottom edge of the fan plate and the rotor ring shroud in high-temperature environments, an intelligent measure...
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Due to the strong reflective properties of the spacecraft surface coatings, there are significant challenges in processingimages from outer space. Furthermore, the volume of data for image feature processing and matc...
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RAW files are the initial measurement of scene radiance widely used in most cameras, and the ubiquitously-used RGB images are converted from RAW data through image Signal processing (ISP) pipelines. Nowadays, digital ...
ISBN:
(纸本)9798350307184
RAW files are the initial measurement of scene radiance widely used in most cameras, and the ubiquitously-used RGB images are converted from RAW data through image Signal processing (ISP) pipelines. Nowadays, digital images are risky of being nefariously manipulated. Inspired by the fact that innate immunity is the first line of body defense, we propose DRAW, a novel scheme of defending images against manipulation by protecting their sources, i.e., camera-shooted RAWs. Specifically, we design a lightweight Multi-frequency Partial Fusion Network (MPF-Net) friendly to devices with limited computing resources by frequency learning and partial feature fusion. It introduces invisible watermarks as protective signal into the RAW data. The protection capability can not only be transferred into the rendered RGB images regardless of the applied ISP pipeline, but also is resilient to post-processing operations such as blurring or compression. Once the image is manipulated, we can accurately identify the forged areas with a localization network. Extensive experiments on several famous RAW datasets, e.g., RAISE, FiveK and SIDD, indicate the effectiveness of our method. We hope that this technique can be used in future cameras as an option for image protection, which could effectively restrict image manipulation at the source.
This paper presents a novel framework called HST for semi-supervised video object segmentation (VOS). HST extracts image and video features using the latest Swin Transformer and Video Swin Transformer to inherit their...
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ISBN:
(纸本)9798350307443
This paper presents a novel framework called HST for semi-supervised video object segmentation (VOS). HST extracts image and video features using the latest Swin Transformer and Video Swin Transformer to inherit their inductive bias for the spatiotemporal locality, which is essential for temporally coherent VOS. To take full advantage of the image and video features, HST casts image and video features as a query and memory, respectively. By applying efficient memory read operations at multiple scales, HST produces hierarchical features for the precise reconstruction of object masks. HST shows effectiveness and robustness in handling challenging scenarios with occluded and fast-moving objects under cluttered backgrounds. In particular, HST-B outperforms the state-of-the-art competitors on multiple popular benchmarks, i.e., YouTube-VOS (85.0%), DAVIS 2017 (85.9%), and DAVIS 2016 (94.0%).
Remote sensing images are useful for a wide variety of planet monitoring applications, from tracking deforestation to tackling illegal fishing. The Earth is extremely diversethe amount of potential tasks in remote sen...
ISBN:
(纸本)9798350307184
Remote sensing images are useful for a wide variety of planet monitoring applications, from tracking deforestation to tackling illegal fishing. The Earth is extremely diversethe amount of potential tasks in remote sensing images is massive, and the sizes of features range from several kilometers to just tens of centimeters. However, creating generalizable computervision methods is a challenge in part due to the lack of a large-scale dataset that captures these diverse features for many tasks. In this paper, we present SATLASPRETRAIN, a remote sensing dataset that is large in both breadth and scale, combining Sentinel-2 and NAIP images with 302M labels under 137 categories and seven label types. We evaluate eight baselines and a proposed method on SATLASPRETRAIN, and find that there is substantial room for improvement in addressing research challenges specific to remote sensing, including processingimage time series that consist of images from very different types of sensors, and taking advantage of long-range spatial context. Moreover, we find that pre-training on SATLASPRETRAIN substantially improves performance on downstream tasks, increasing average accuracy by 18% over imageNet and 6% over the next best baseline. The dataset, pre-trained model weights, and code are available at https://***/.
Machine drawing has gradually become a hot research topic in computervision and robotics domains recently. However, decomposing a given target image from raster space into an ordered sequence and reconstructing those...
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ISBN:
(纸本)9781728198354
Machine drawing has gradually become a hot research topic in computervision and robotics domains recently. However, decomposing a given target image from raster space into an ordered sequence and reconstructing those strokes is a challenging task. In this work, we focus on the drawing task for the images in various styles where the distribution of stroke parameters differs. We propose a multi-stage environment model based reinforcement learning (RL) drawing framework with fine-grained perceptual reward to guide the agent under this framework to draw details and an overall outline of the target image accurately. The experiments show that the visual quality of our method slightly outperforms SOTAmethod in nature and doodle style, while it outperforms the SOTA approaches by a large margin with high efficiency in sketch style.
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