作者: 妨礙 時(shí)間: 2025-3-21 22:42
AIM 2020 Challenge on Video Temporal Super-Resolutions paper reports the second AIM challenge on Video Temporal Super-Resolution (VTSR), a.k.a. frame interpolation, with a focus on the proposed solutions, results, and analysis. From low-frame-rate (15?fps) videos, the challenge participants are required to submit higher-frame-rate (30 and 60?fps) sequ作者: 誘惑 時(shí)間: 2025-3-22 01:52
Enhanced Quadratic Video Interpolation upsurge in industry. Many learning-based methods have been proposed and achieved progressive results. Among them, a recent algorithm named quadratic video interpolation (QVI) achieves appealing performance. It exploits higher-order motion information (. acceleration) and successfully models the est作者: Aboveboard 時(shí)間: 2025-3-22 06:50 作者: Hippocampus 時(shí)間: 2025-3-22 09:52 作者: 平庸的人或物 時(shí)間: 2025-3-22 14:31
Multi-objective Reinforced Evolution in Mobile Neural Architecture Searchhitecture search involving evolutionary algorithms (EA) and reinforcement learning (RL), however, they are separately used. In this paper, we present a novel multi-objective algorithm called MoreMNAS (.ulti-.bjective .einforced .volution in .obile .eural .rchitecture .earch) by leveraging good virtu作者: 平庸的人或物 時(shí)間: 2025-3-22 17:23 作者: 哪有黃油 時(shí)間: 2025-3-22 23:45
Deep Adaptive Inference Networks for Single Image Super-Resolution(CNNs). For most existing methods, the computational cost of each SISR model is irrelevant to local image content, hardware platform and application scenario. Nonetheless, content and resource adaptive model is more preferred, and it is encouraging to apply simpler and efficient networks to the easi作者: Feedback 時(shí)間: 2025-3-23 05:10 作者: 濕潤 時(shí)間: 2025-3-23 07:26
Single Image Dehazing for a Variety of Haze Scenarios Using Back Projected Pyramid Networkrk architecture for this problem, namely back projected pyramid network (BPPNet), that gives good performance for a variety of challenging haze conditions, including dense haze and inhomogeneous haze. Our architecture incorporates learning of multiple levels of complexities while retaining spatial c作者: 四指套 時(shí)間: 2025-3-23 12:28
A Benchmark for Inpainting of Clothing Images with Irregular Holeson intelligent fashion analysis systems, clothing image inpainting has not been extensively examined yet. For that matter, we present an extensive benchmark of clothing image inpainting on well-known fashion datasets. Furthermore, we introduce the use of a dilated version of partial convolutions, wh作者: 壕溝 時(shí)間: 2025-3-23 17:16
Learning to Improve Image Compression Without Changing the Standard Decoderompression. However, the existing approaches either train a post-processing DNN on the decoder side, or propose learning for image compression in an end-to-end manner. This way, the trained DNNs are required in the decoder, leading to the incompatibility to the standard image decoders (., JPEG) in p作者: lattice 時(shí)間: 2025-3-23 19:24
Conditional Adversarial Camera Model Anonymizationfic artifacts present within the image. Model anonymization is the process of transforming these artifacts such that the apparent capture model is changed. We propose a conditional adversarial approach for learning such transformations. In contrast to previous works, we cast model anonymization as t作者: 施舍 時(shí)間: 2025-3-23 23:07
Disrupting Deepfakes: Adversarial Attacks Against Conditional Image Translation Networks and Facial rate new images of that same person under different expressions and poses. Some systems can also modify targeted attributes such as hair color or age. This type of manipulated images and video have been coined Deepfakes. In order to prevent a malicious user from generating modified images of a perso作者: 易發(fā)怒 時(shí)間: 2025-3-24 04:26
Efficiently Detecting Plausible Locations for Object Placement Using Masked Convolutions manipulation, the plausible placement and the blending of the new objects in the image are critical. In this paper, we propose a fast method for the automatic selection of plausible locations for object insertion into images. Like previous work, we approach the object placement problem as a detecti作者: habitat 時(shí)間: 2025-3-24 07:44
L2-Constrained RemNet for Camera Model Identification and Image Manipulation Detection L2-constrained Remnant Convolutional Neural Network (L2-constrained RemNet) for performing these two crucial tasks. The proposed network architecture consists of a dynamic preprocessor block and a classification block. An L2 loss is applied to the output of the preprocessor block, and categorical c作者: ENACT 時(shí)間: 2025-3-24 11:49 作者: 四指套 時(shí)間: 2025-3-24 15:01 作者: Dungeon 時(shí)間: 2025-3-24 19:42
Participation and the Nature of the Firm,l attribution classifier, which constrains the generative network to transform the full range of artifacts. Quantitative comparisons demonstrate the efficacy of our framework in a restrictive non-interactive black-box setting.作者: cancellous-bone 時(shí)間: 2025-3-25 02:40
A Benchmark for Inpainting of Clothing Images with Irregular Holesrent masks. Experiments show that dilated partial convolutions (DPConv) improve the quantitative inpainting performance when compared to the other inpainting strategies, especially it performs better when the mask size is 20% or more of the image.作者: frivolous 時(shí)間: 2025-3-25 03:44
Conditional Adversarial Camera Model Anonymizationl attribution classifier, which constrains the generative network to transform the full range of artifacts. Quantitative comparisons demonstrate the efficacy of our framework in a restrictive non-interactive black-box setting.作者: PHAG 時(shí)間: 2025-3-25 09:21 作者: PAD416 時(shí)間: 2025-3-25 12:16
https://doi.org/10.1007/978-3-7908-2078-2petition, and 5 teams (one withdrawn) have competed in the final testing phase. The winning team proposes the enhanced quadratic video interpolation method and achieves state-of-the-art on the VTSR task.作者: chapel 時(shí)間: 2025-3-25 17:56 作者: MILL 時(shí)間: 2025-3-25 21:08
The Latin American Studies Book Seriesthat, by fusing the dense connection mechanism and diversity enhancement devices, our proposed method achieves state-of-the-art accuracy and predicts sharp depth maps that restore reliable object structures.作者: Femine 時(shí)間: 2025-3-26 02:56 作者: 伸展 時(shí)間: 2025-3-26 07:01
DeepGIN: Deep Generative Inpainting Network for Extreme Image Inpaintingng results. Our DeepGIN outperforms the state-of-the-art approaches generally, including two publicly available datasets (FFHQ and Oxford Buildings), both quantitatively and qualitatively. We also demonstrate that our model is capable of completing masked images in the wild.作者: insurgent 時(shí)間: 2025-3-26 10:45 作者: 有常識(shí) 時(shí)間: 2025-3-26 13:29 作者: 真實(shí)的人 時(shí)間: 2025-3-26 19:40
Densely Connecting Depth Maps for Monocular Depth Estimationthat, by fusing the dense connection mechanism and diversity enhancement devices, our proposed method achieves state-of-the-art accuracy and predicts sharp depth maps that restore reliable object structures.作者: FEAS 時(shí)間: 2025-3-27 00:36 作者: obligation 時(shí)間: 2025-3-27 04:04 作者: patella 時(shí)間: 2025-3-27 09:15 作者: Corroborate 時(shí)間: 2025-3-27 13:10
Conference proceedings 2020ere carefully reviewed and selected from a total of 467 submissions. The papers deal with diverse computer vision topics..Part IV focusses on advances in image manipulation; assistive computer vision and robotics; and computer vision for UAVs..作者: 打折 時(shí)間: 2025-3-27 15:46
0302-9743 ceedings were carefully reviewed and selected from a total of 467 submissions. The papers deal with diverse computer vision topics..Part IV focusses on advances in image manipulation; assistive computer vision and robotics; and computer vision for UAVs..978-3-030-66822-8978-3-030-66823-5Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: RACE 時(shí)間: 2025-3-27 21:47
https://doi.org/10.1007/978-3-030-66823-5computer networks; data security; face recognition; image analysis; image coding; image compression; image作者: 我怕被刺穿 時(shí)間: 2025-3-28 00:23 作者: inflate 時(shí)間: 2025-3-28 03:39
Computer Vision – ECCV 2020 Workshops978-3-030-66823-5Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: Synapse 時(shí)間: 2025-3-28 07:32 作者: 裂口 時(shí)間: 2025-3-28 13:19 作者: Sinus-Rhythm 時(shí)間: 2025-3-28 17:48 作者: 無能力之人 時(shí)間: 2025-3-28 19:31 作者: Humble 時(shí)間: 2025-3-29 01:16
The Economics of Climate Change Policieseasingly more attention in recent years. Most existing approaches opt to use deformable convolution to temporally align neighboring frames and apply traditional spatial attention mechanism (convolution based) to enhance reconstructed features. However, such spatial-only strategies cannot fully utili作者: NIP 時(shí)間: 2025-3-29 06:49 作者: cutlery 時(shí)間: 2025-3-29 08:12 作者: 種子 時(shí)間: 2025-3-29 13:53
María Priscila Ramos,Omar Osvaldo Chisari(CNNs). For most existing methods, the computational cost of each SISR model is irrelevant to local image content, hardware platform and application scenario. Nonetheless, content and resource adaptive model is more preferred, and it is encouraging to apply simpler and efficient networks to the easi作者: 健談 時(shí)間: 2025-3-29 16:15 作者: OWL 時(shí)間: 2025-3-29 20:57
Maria Elisa Belfiori,Mariano Javier Rabassark architecture for this problem, namely back projected pyramid network (BPPNet), that gives good performance for a variety of challenging haze conditions, including dense haze and inhomogeneous haze. Our architecture incorporates learning of multiple levels of complexities while retaining spatial c作者: Herd-Immunity 時(shí)間: 2025-3-30 01:00
The Firm in Illyria: Market Syndicalism,on intelligent fashion analysis systems, clothing image inpainting has not been extensively examined yet. For that matter, we present an extensive benchmark of clothing image inpainting on well-known fashion datasets. Furthermore, we introduce the use of a dilated version of partial convolutions, wh作者: ATRIA 時(shí)間: 2025-3-30 06:14
The Economics of Co-Determinationompression. However, the existing approaches either train a post-processing DNN on the decoder side, or propose learning for image compression in an end-to-end manner. This way, the trained DNNs are required in the decoder, leading to the incompatibility to the standard image decoders (., JPEG) in p作者: 高歌 時(shí)間: 2025-3-30 11:11 作者: Antigen 時(shí)間: 2025-3-30 14:08 作者: LAVE 時(shí)間: 2025-3-30 19:33 作者: LAPSE 時(shí)間: 2025-3-30 23:56
Modelling the Intensity of Competition, L2-constrained Remnant Convolutional Neural Network (L2-constrained RemNet) for performing these two crucial tasks. The proposed network architecture consists of a dynamic preprocessor block and a classification block. An L2 loss is applied to the output of the preprocessor block, and categorical c作者: 極少 時(shí)間: 2025-3-31 01:48 作者: capsule 時(shí)間: 2025-3-31 05:17 作者: Bumptious 時(shí)間: 2025-3-31 09:29 作者: packet 時(shí)間: 2025-3-31 14:36
https://doi.org/10.1007/978-3-7908-2078-2er quality can be achieved in trade-off for fidelity by generating plausible high-frequency content. Track 2 therefore aims at generating visually pleasing results, which are ranked according to human perception, evaluated by a user study. In contrast to single image super-resolution (SISR), VSR can作者: 諄諄教誨 時(shí)間: 2025-3-31 18:09
The Economics of Climate Change PoliciesDKSAN on AIM2020 Video Extreme Super-Resolution Challenge to super-resolve videos with a scale factor as large as 16. Experimental results demonstrate that our proposed DKSAN can achieve both better subjective and objective performance compared with the existing state-of-the-art EDVR on Vid3oC and I作者: 朦朧 時(shí)間: 2025-3-31 22:11 作者: GLOSS 時(shí)間: 2025-4-1 01:59
María Priscila Ramos,Omar Osvaldo Chisarirmed with the support of efficient sparse convolution, where only a fraction of the layers in the backbone is performed at a given position according to its predicted depth. The network learning can be formulated as joint optimization of reconstruction and network depth losses. In the inference stag作者: goodwill 時(shí)間: 2025-4-1 08:09
The Economics of Co-Determinationnot modify the JPEG decoder and therefore our approach is applicable when viewing images with the widely used standard JPEG decoder. The experiments validate that our approach successfully improves the rate-distortion performance of JPEG in terms of various quality metrics, such as PSNR, MS-SSIM and作者: 橫條 時(shí)間: 2025-4-1 11:54
Context: What Is Codetermination?,ch means that the attacker does not need to have knowledge about the conditioning class, and (2) adversarial training for generative adversarial networks (GANs) as a first step towards robust image translation networks. Finally, in our scenario, the deepfaker can adaptively blur the image and potent作者: 遠(yuǎn)足 時(shí)間: 2025-4-1 17:29
