imageprocessing.is a very fundamental technique in the field of low-level vision. However, with the development of deep learning over the past five years, most low-level vision methods tend to ignore this technique. ...
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We introduce a deep learning methodology that combines the Inception-ResNet-V2 architecture with a hybrid attention mechanism. This fusion enables the extraction of crucial information from both channel and spatial di...
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Transformer architectures have become state-of-the-art models in computer vision and natural language processing. To a significant degree, their success can be attributed to self-supervised pre-training on large scale...
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In the last few years,with the development of generative adversarial networks (GAN), Significant technical updates have been made in the field of face attribute editing. A new method is proposed in this paper for edit...
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The watermark detection method under Android system combines the technology of computer vision, imageprocessing.and pattern matching, aiming to provide an effective and automatic watermark detection solution. Through...
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In the field of video action recognition, the challenge of efficiently extracting video features while ensuring computational efficiency has been addressed in our *** propose a novel video action recognition model nam...
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Recent progress in 3D display technologies has raised the demand for stylized 3D digital content. Previous approaches either perform style transfer on stereoscopic image pairs or reconstruct 3D environment with multip...
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ISBN:
(数字)9781665487399
ISBN:
(纸本)9781665487399
Recent progress in 3D display technologies has raised the demand for stylized 3D digital content. Previous approaches either perform style transfer on stereoscopic image pairs or reconstruct 3D environment with multiple view images. In this paper, we propose a novel view stylization framework that can convert a single 2D image into multiple stylized views. It is a two-stage solution that contains view synthesis and neural style transfer. We estimate dense optical flow between the source and novel views so that the style transfer model can produce consistent results. Experimental results show that our method significantly improves the consistency among views compared to the baseline method.
Mobile game play can be a prime use case where an efficient SR network can lead to both performance boosts and power savings. In this paper, we present RenderSR (RSR), a bandwidth aware super-resolution network design...
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ISBN:
(数字)9781665487399
ISBN:
(纸本)9781665487399
Mobile game play can be a prime use case where an efficient SR network can lead to both performance boosts and power savings. In this paper, we present RenderSR (RSR), a bandwidth aware super-resolution network designed for use in mobile game upscaling. We explore how different factors affect the resulting image quality: color space, the inclusion of the depth channel, sharpening. With a 40K parameter size, RenderSR without sharpening achieves a PSNR value difference ranging -0.41 to 0.36dB from several much larger SR models. RenderSR with sharpening super resolved large objects such as rocks, buildings, tree trunks are almost identical to the ground truth. Based on our performance experiment, we propose that RenderSR upscales the GPU rendered image on NPU or DSP on the mobile SoC.
Face detection is the most basic processing.in the face recognition process. If the detection method is not appropriate, it can cause the recognition process to collapse. Face detection is very important in face recog...
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Recognizing multiple faces within a single frame or image presents a significant challenge in facial recognition tasks. This challenge demands robust algorithms capable of handling variations in facial position, unsta...
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