作者: recession 時間: 2025-3-22 00:14 作者: 慢跑 時間: 2025-3-22 02:34
,EAGAN: Efficient Two-Stage Evolutionary Architecture Search for?GANs,d adopts the one-to-one training and weight-resetting strategies to enhance the stability of GAN training. Both stages use the non-dominated sorting method to produce Pareto-front architectures under multiple objectives (e.g., model size, Inception Score (IS), and Fréchet Inception Distance (FID)). 作者: antenna 時間: 2025-3-22 07:24 作者: prostate-gland 時間: 2025-3-22 11:56
,Multimodal Conditional Image Synthesis with?Product-of-Experts GANs, art in multimodal conditional image synthesis, PoE-GAN also outperforms the best existing unimodal conditional image synthesis approaches when tested in the unimodal setting. The project website is available at ..作者: 切碎 時間: 2025-3-22 14:46 作者: 切碎 時間: 2025-3-22 18:40 作者: 演講 時間: 2025-3-23 00:27
,CCPL: Contrastive Coherence Preserving Loss for?Versatile Style Transfer,to other tasks, such as image-to-image translation. Besides, to better fuse content and style features, we propose Simple Covariance Transformation (SCT) to effectively align second-order statistics of the content feature with the style feature. Experiments demonstrate the effectiveness of the resul作者: Opponent 時間: 2025-3-23 05:15
,Bi-level Feature Alignment for?Versatile Image Translation and?Manipulation,n be effectively back-propagated. In addition, we design a novel semantic position encoding mechanism that builds up coordinate for each individual semantic region to preserve texture structures while building correspondences. Further, we design a novel confidence feature injection module which miti作者: Cuisine 時間: 2025-3-23 06:04
,High-Fidelity Image Inpainting with?GAN Inversion,stency. To reconstruct faithful and photorealistic images, a simple yet effective Soft-update Mean Latent module is designed to capture more diverse in-domain patterns that synthesize high-fidelity textures for large corruptions. Comprehensive experiments on four challenging dataset, including Place作者: 財政 時間: 2025-3-23 11:22 作者: Minatory 時間: 2025-3-23 17:53
The Economics of Superstars and Celebritiess, are needed to train a high-fidelity unconditional human generation model with a vanilla StyleGAN. 2) A balanced training set helps improve the generation quality with rare face poses compared to the long-tailed counterpart, whereas simply balancing the clothing texture distribution does not effec作者: sacrum 時間: 2025-3-23 18:08 作者: FIS 時間: 2025-3-23 22:24 作者: 專心 時間: 2025-3-24 02:41
The Economics of Superstars and Celebrities addition, we introduce a unified training objective for DynaST, making it a versatile reference-based image translation framework for both supervised and unsupervised scenarios. Extensive experiments on three applications, pose-guided person image generation, edge-based face synthesis, and undistor作者: Subjugate 時間: 2025-3-24 06:34
https://doi.org/10.1007/978-3-8350-5429-5 art in multimodal conditional image synthesis, PoE-GAN also outperforms the best existing unimodal conditional image synthesis approaches when tested in the unimodal setting. The project website is available at ..作者: Urea508 時間: 2025-3-24 14:05 作者: Genome 時間: 2025-3-24 15:34 作者: ARK 時間: 2025-3-24 21:01
Macroeconomic Effects of Oil Price Shocksto other tasks, such as image-to-image translation. Besides, to better fuse content and style features, we propose Simple Covariance Transformation (SCT) to effectively align second-order statistics of the content feature with the style feature. Experiments demonstrate the effectiveness of the resul作者: 艱苦地移動 時間: 2025-3-25 01:51
https://doi.org/10.1007/978-3-540-79883-5n be effectively back-propagated. In addition, we design a novel semantic position encoding mechanism that builds up coordinate for each individual semantic region to preserve texture structures while building correspondences. Further, we design a novel confidence feature injection module which miti作者: 尊敬 時間: 2025-3-25 04:35
The Economics of Symbolic Exchangestency. To reconstruct faithful and photorealistic images, a simple yet effective Soft-update Mean Latent module is designed to capture more diverse in-domain patterns that synthesize high-fidelity textures for large corruptions. Comprehensive experiments on four challenging dataset, including Place作者: 心胸開闊 時間: 2025-3-25 10:35 作者: Intend 時間: 2025-3-25 14:34
0302-9743 ruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation..978-3-031-19786-4978-3-031-19787-1Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: optional 時間: 2025-3-25 18:56
Environmental Productivity and Kuznets Curveot be trained end-to-end and struggle to edit encoded images precisely, VecGAN is end-to-end trained for image translation task and successful at editing an attribute while preserving the others. Our extensive experiments show that VecGAN achieves significant improvements over state-of-the-arts for both local and global edits.作者: 縱欲 時間: 2025-3-25 21:41 作者: hedonic 時間: 2025-3-26 03:27 作者: 深陷 時間: 2025-3-26 07:07 作者: 他去就結(jié)束 時間: 2025-3-26 10:03
,VecGAN: Image-to-Image Translation with?Interpretable Latent Directions,ot be trained end-to-end and struggle to edit encoded images precisely, VecGAN is end-to-end trained for image translation task and successful at editing an attribute while preserving the others. Our extensive experiments show that VecGAN achieves significant improvements over state-of-the-arts for both local and global edits.作者: heartburn 時間: 2025-3-26 14:23 作者: 中子 時間: 2025-3-26 17:13 作者: gout109 時間: 2025-3-26 23:34 作者: 案發(fā)地點 時間: 2025-3-27 02:05
0302-9743 puter Vision, ECCV 2022, held in Tel Aviv, Israel, during October 23–27, 2022..?.The 1645 papers presented in these proceedings were carefully reviewed and selected from a total of 5804 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforc作者: GOAD 時間: 2025-3-27 06:07
The Economics of Superstars and Celebritiesan equirectangular projection format. In particular, our model consists of color consistency corrections, warping, and blending, and is trained by perceptual and SSIM losses. The effectiveness of the proposed algorithm is verified on two real-world stitching datasets.作者: bypass 時間: 2025-3-27 13:11
Environmental Productivity and Kuznets Curveeferences (say, animal faces) successfully. Furthermore, one can control what aspects of the style are used and how much of the style is applied. Qualitative and quantitative evaluation show that JoJoGAN produces high quality high resolution images that vastly outperform the current state-of-the-art.作者: 較早 時間: 2025-3-27 13:41 作者: 爆炸 時間: 2025-3-27 18:18 作者: Nonflammable 時間: 2025-3-28 00:30
,Weakly-Supervised Stitching Network for?Real-World Panoramic Image Generation,an equirectangular projection format. In particular, our model consists of color consistency corrections, warping, and blending, and is trained by perceptual and SSIM losses. The effectiveness of the proposed algorithm is verified on two real-world stitching datasets.作者: 保全 時間: 2025-3-28 03:33 作者: absorbed 時間: 2025-3-28 07:13
,DeltaGAN: Towards Diverse Few-Shot Image Generation with?Sample-Specific Delta,t image, which is combined with this input image to generate a new image within the same category. Besides, an adversarial delta matching loss is designed to link the above two subnetworks together. Extensive experiments on six benchmark datasets demonstrate the effectiveness of our proposed method. Our code is available at ..作者: 機(jī)械 時間: 2025-3-28 11:33
,Contrastive Learning for?Diverse Disentangled Foreground Generation,ols the remaining factors (“unknown”). The sampled latent codes from the two sets jointly bi-modulate the convolution kernels to guide the generator to synthesize diverse results. Experiments demonstrate the superiority of our method over state-of-the-arts in result diversity and generation controllability.作者: 盟軍 時間: 2025-3-28 16:17
Conference proceedings 2022on, ECCV 2022, held in Tel Aviv, Israel, during October 23–27, 2022..?.The 1645 papers presented in these proceedings were carefully reviewed and selected from a total of 5804 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement lear作者: 返老還童 時間: 2025-3-28 22:48 作者: PATHY 時間: 2025-3-28 23:43
,StyleGAN-Human: A Data-Centric Odyssey of?Human Generation,dies in this field mainly focus on “network engineering” such as designing new components and objective functions. This work takes a data-centric perspective and investigates multiple critical aspects in “data engineering”, which we believe would complement the current practice. To facilitate a comp作者: Goblet-Cells 時間: 2025-3-29 06:49 作者: commune 時間: 2025-3-29 08:21
,EAGAN: Efficient Two-Stage Evolutionary Architecture Search for?GANs,orks try to stabilize it by manually modifying GAN architecture, it requires much expertise. Neural architecture search (NAS) has become an attractive solution to search GANs automatically. The early NAS-GANs search only generators to reduce search complexity but lead to a sub-optimal GAN. Some rece作者: EXTOL 時間: 2025-3-29 13:28
,Weakly-Supervised Stitching Network for?Real-World Panoramic Image Generation,based stitching is to obtain pairs of input images with a narrow field of view and ground truth images with a wide field of view captured from real-world scenes. To overcome this difficulty, we develop a weakly-supervised learning mechanism to train the stitching model without requiring genuine grou作者: fleeting 時間: 2025-3-29 16:01 作者: 搖晃 時間: 2025-3-29 21:21 作者: 詞匯 時間: 2025-3-30 02:39
,Auto-regressive Image Synthesis with?Integrated Quantization,yet high-fidelity images remains a grand challenge in conditional image generation. This paper presents a versatile framework for conditional image generation which incorporates the inductive bias of CNNs and powerful sequence modeling of auto-regression that naturally leads to diverse image generat作者: SHRIK 時間: 2025-3-30 06:32
JoJoGAN: One Shot Face Stylization,earn a style mapper from a single example of the style. JoJoGAN uses a GAN inversion procedure and StyleGAN’s style-mixing property to produce a substantial paired dataset from a single example style. The paired dataset is then used to fine-tune a StyleGAN. An image can then be style mapped by GAN-i作者: 構(gòu)成 時間: 2025-3-30 12:04
,VecGAN: Image-to-Image Translation with?Interpretable Latent Directions, task faces the challenges of precise attribute editing with controllable strength and preservation of the other attributes of an image. For this goal, we design the attribute editing by latent space factorization and for each attribute, we learn a linear direction that is orthogonal to the others. 作者: 無法解釋 時間: 2025-3-30 14:22
,Any-Resolution Training for?High-Resolution Image Synthesis,and low-resolution images are discarded altogether, precious supervision is lost. We argue that every pixel matters and create datasets with variable-size images, collected at their native resolutions. To take advantage of varied-size data, we introduce . training, a process that samples . at random作者: dissolution 時間: 2025-3-30 19:04 作者: 雕鏤 時間: 2025-3-30 20:51
,CANF-VC: Conditional Augmented Normalizing Flows for?Video Compression,st learned video compression systems adopt the same hybrid-based coding architecture as the traditional codecs. Recent research on conditional coding has shown the sub-optimality of the hybrid-based coding and opens up opportunities for deep generative models to take a key role in creating new codin作者: PRE 時間: 2025-3-31 04:36
,Bi-level Feature Alignment for?Versatile Image Translation and?Manipulation, faithful style control remains a grand challenge in computer vision. This paper presents a versatile image translation and manipulation framework that achieves accurate semantic and style guidance in image generation by explicitly building a correspondence. To handle the quadratic complexity incurr作者: Humble 時間: 2025-3-31 07:21
,High-Fidelity Image Inpainting with?GAN Inversion,reuse the well-trained GAN as effective prior to generate realistic patches for missing holes with GAN inversion. Nevertheless, the ignorance of hard constraint in these algorithms may yield the gap between GAN inversion and image inpainting. Addressing this problem, in this paper we devise a novel 作者: indoctrinate 時間: 2025-3-31 09:17 作者: Predigest 時間: 2025-3-31 15:21 作者: 辯論 時間: 2025-3-31 17:42 作者: 蜿蜒而流 時間: 2025-4-1 00:31
,Video Extrapolation in?Space and?Time,s to observe the spatial-temporal world: NVS aims to synthesize a scene from a new point of view, while VP aims to see a scene from a new point of time. These two tasks provide complementary signals to obtain a scene representation, as viewpoint changes from spatial observations inform depth, and te作者: 手工藝品 時間: 2025-4-1 05:01
,Contrastive Learning for?Diverse Disentangled Foreground Generation,eration methods often struggle to generate diverse results and rarely allow users to explicitly control specific factors of variation (e.g., varying the facial identity or expression for face inpainting results). We leverage contrastive learning with latent codes to generate diverse foreground resul作者: Encoding 時間: 2025-4-1 07:18
Computer Vision – ECCV 2022978-3-031-19787-1Series ISSN 0302-9743 Series E-ISSN 1611-3349
