作者: 犬儒主義者 時(shí)間: 2025-3-21 21:08
https://doi.org/10.1007/978-1-349-01379-1efficiency of our method. As shown in experimental results, our method achieves state-of-the-art performance on both flow estimation and object reconstruction while performing much faster than existing methods in both training and inference. Our code and data are available at ..作者: 自傳 時(shí)間: 2025-3-22 02:11
Lester B. Lave,Eugene P. Seskinults. We propose Flow-Fill, a novel two-stage image inpainting framework that utilizes a conditional normalizing flow model to generate diverse structural priors in the first stage. Flow-Fill can directly estimate the joint probability density of the missing regions as a flow-based model without rea作者: Oafishness 時(shí)間: 2025-3-22 06:35 作者: 喊叫 時(shí)間: 2025-3-22 11:16 作者: Musculoskeletal 時(shí)間: 2025-3-22 16:52 作者: Musculoskeletal 時(shí)間: 2025-3-22 17:56 作者: 急性 時(shí)間: 2025-3-22 21:40
Institutions — Council and Courtresolution and diverse imagery at a fraction of the computational expense. In this manner, we can generate image resolutions exceeding that of the original training set samples whilst additionally provisioning per-image likelihood estimates (in a departure from generative adversarial approaches). Ou作者: 護(hù)身符 時(shí)間: 2025-3-23 04:05 作者: 墻壁 時(shí)間: 2025-3-23 09:25 作者: CHECK 時(shí)間: 2025-3-23 12:45 作者: extinguish 時(shí)間: 2025-3-23 14:28
Bernadette Andreosso-O’Callaghan with a surrogate predictor, that iteratively learns to generate samples from increasingly promising latent subspaces. This approach leads to very effective and efficient architecture search, while keeping the query amount low. In addition, our approach allows in a straightforward manner to jointly 作者: oracle 時(shí)間: 2025-3-23 21:32 作者: PAEAN 時(shí)間: 2025-3-24 01:47
https://doi.org/10.1057/9781137348463ges and very large point clouds, and demonstrate that it requires fewer than 25% of the parameters, 33% of the memory footprint, and 10% of the computation time of competing techniques such as ACORN to reach the same representation accuracy. A fast implementation of MINER for images and 3D volumes i作者: Ophthalmoscope 時(shí)間: 2025-3-24 05:37
,Accelerating Score-Based Generative Models with?Preconditioned Diffusion Sampling,iversity validate that PDS consistently accelerates off-the-shelf SGMs whilst maintaining the synthesis quality. In particular, PDS can accelerate by up to . on more challenging high resolution (1024.1024) image generation.作者: labyrinth 時(shí)間: 2025-3-24 09:20 作者: Overthrow 時(shí)間: 2025-3-24 12:06
,Diverse Image Inpainting with?Normalizing Flow,ults. We propose Flow-Fill, a novel two-stage image inpainting framework that utilizes a conditional normalizing flow model to generate diverse structural priors in the first stage. Flow-Fill can directly estimate the joint probability density of the missing regions as a flow-based model without rea作者: HAIL 時(shí)間: 2025-3-24 18:10
,TREND: Truncated Generalized Normal Density Estimation of?Inception Embeddings for?GAN Evaluation,on, which consequently eliminates the risk of faulty evaluation results. Furthermore, the proposed metric significantly improves robustness of evaluation results against variation of the number of image samples.作者: 取之不竭 時(shí)間: 2025-3-24 22:41 作者: vitreous-humor 時(shí)間: 2025-3-25 01:54 作者: 完成才會(huì)征服 時(shí)間: 2025-3-25 05:17 作者: 無(wú)能力之人 時(shí)間: 2025-3-25 11:33 作者: 紅潤(rùn) 時(shí)間: 2025-3-25 11:52 作者: gonioscopy 時(shí)間: 2025-3-25 18:59 作者: 不出名 時(shí)間: 2025-3-25 22:06
Controllable Shadow Generation Using Pixel Height Maps,tor to apply softness to a hard shadow based on a softness input parameter. Qualitative and quantitative evaluations demonstrate that the proposed Pixel Height?significantly improves the quality of the shadow generation while allowing for controllability.作者: Coronary 時(shí)間: 2025-3-26 00:57 作者: 杠桿支點(diǎn) 時(shí)間: 2025-3-26 04:26
,DuelGAN: A Duel Between Two Discriminators Stabilizes the?GAN Training,x game formed among .. We offer convergence behavior of DuelGAN as well as stability of the min-max game. It’s worth mentioning that DuelGAN operates in the unsupervised setting, and the duel between . and . does not need any label supervision. Experiments results on a synthetic dataset and on real-作者: 使厭惡 時(shí)間: 2025-3-26 08:46 作者: 誓言 時(shí)間: 2025-3-26 15:28
Per-Olov Johansson,Bengt Kristr?m FID of 2.17 on unconditional CIFAR-10—and . the computational cost of inference for the same number of denoising steps. Our framework is fully compatible with continuous-time diffusion and retains its flexible capabilities, including exact log-likelihoods and controllable generation. Code is available at ..作者: 蠟燭 時(shí)間: 2025-3-26 17:38
0302-9743 ruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation..978-3-031-20049-6978-3-031-20050-2Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 薄荷醇 時(shí)間: 2025-3-26 22:33
Environmental Control and Economic Systemsesample. Coupled with Token-Critic, a state-of-the-art generative transformer significantly improves its performance, and outperforms recent diffusion models and GANs in terms of the trade-off between generated image quality and diversity, in the challenging class-conditional ImageNet generation.作者: LATHE 時(shí)間: 2025-3-27 03:03
The Birth of the Common Agricultural Policy the rooted models by averaging their weights and fine-tuning them for each specific domain, using only data generated by the original trained models. We demonstrate that our approach is superior to baseline methods and to existing transfer learning techniques, and investigate several applications. (Code is available at: .).作者: 出沒(méi) 時(shí)間: 2025-3-27 08:48 作者: 進(jìn)入 時(shí)間: 2025-3-27 10:26
GAN Cocktail: Mixing GANs Without Dataset Access, the rooted models by averaging their weights and fine-tuning them for each specific domain, using only data generated by the original trained models. We demonstrate that our approach is superior to baseline methods and to existing transfer learning techniques, and investigate several applications. (Code is available at: .).作者: intercede 時(shí)間: 2025-3-27 16:58 作者: 生命層 時(shí)間: 2025-3-27 18:08
Subspace Diffusion Generative Models, FID of 2.17 on unconditional CIFAR-10—and . the computational cost of inference for the same number of denoising steps. Our framework is fully compatible with continuous-time diffusion and retains its flexible capabilities, including exact log-likelihoods and controllable generation. Code is available at ..作者: 雕鏤 時(shí)間: 2025-3-28 01:00 作者: Anthem 時(shí)間: 2025-3-28 03:02 作者: nutrition 時(shí)間: 2025-3-28 09:39 作者: 使腐爛 時(shí)間: 2025-3-28 10:28
Interaction Networks: An Introduction with an arbitrary number of objects. We evaluate our method on the task of unsupervised scene decomposition. Experimental results demonstrate that . has strong scalability and is capable of detecting and segmenting an unknown number of objects from a point cloud in an unsupervised manner.作者: happiness 時(shí)間: 2025-3-28 15:19
,Learning to?Generate Realistic LiDAR Point Clouds, approach produces more realistic samples than other generative models. Furthermore, LiDARGen can sample point clouds conditioned on inputs without retraining. We demonstrate that our proposed generative model could be directly used to densify LiDAR point clouds. Our code is available at: ..作者: Ischemic-Stroke 時(shí)間: 2025-3-28 21:42
,Spatially Invariant Unsupervised 3D Object-Centric Learning and?Scene Decomposition, with an arbitrary number of objects. We evaluate our method on the task of unsupervised scene decomposition. Experimental results demonstrate that . has strong scalability and is capable of detecting and segmenting an unknown number of objects from a point cloud in an unsupervised manner.作者: intricacy 時(shí)間: 2025-3-29 00:21 作者: bioavailability 時(shí)間: 2025-3-29 03:44
,Learning to?Generate Realistic LiDAR Point Clouds,rages the powerful score-matching energy-based model and formulates the point cloud generation process as a stochastic denoising process in the equirectangular view. This model allows us to sample diverse and high-quality point cloud samples with guaranteed physical feasibility and controllability. 作者: 聾子 時(shí)間: 2025-3-29 08:06
,RFNet-4D: Joint Object Reconstruction and?Flow Estimation from?4D Point Clouds,nstruction from time-varying point clouds (a.k.a. 4D point clouds) is generally overlooked. In this paper, we propose a new network architecture, namely RFNet-4D, that jointly reconstruct objects and their motion flows from 4D point clouds. The key insight is that simultaneously performing both task作者: 琺瑯 時(shí)間: 2025-3-29 12:25
,Diverse Image Inpainting with?Normalizing Flow,he “corrupted region" content consistent with the background and generate a variety of reasonable texture details. However, existing one-stage methods that directly output the inpainting results have to make a trade-off between diversity and consistency. The two-stage methods as the current trend ca作者: jettison 時(shí)間: 2025-3-29 17:12
,Improved Masked Image Generation with?Token-Critic, their autoregressive counterparts. However, optimal parallel sampling from the true joint distribution of visual tokens remains an open challenge. In this paper we introduce Token-Critic, an auxiliary model to guide the sampling of a non-autoregressive generative transformer. Given a masked-and-rec作者: 和平 時(shí)間: 2025-3-29 20:59
,TREND: Truncated Generalized Normal Density Estimation of?Inception Embeddings for?GAN Evaluation,butions of the set of ground truth images and the set of generated test images. The Frechét Inception distance is one of the most widely used metrics for evaluation of GANs, which assumes that the features from a trained Inception model for a set of images follow a normal distribution. In this paper作者: 鞭子 時(shí)間: 2025-3-30 00:46
,Exploring Gradient-Based Multi-directional Controls in?GANs, the structure of the latent space in GANs largely remains as a black-box, leaving its controllable generation an open problem, especially when spurious correlations between different semantic attributes exist in the image distributions. To address this problem, previous methods typically learn line作者: 食料 時(shí)間: 2025-3-30 07:44 作者: palliate 時(shí)間: 2025-3-30 08:51
,Neural Scene Decoration from?a?Single Photograph,d a 3D model of the space, decorate, and then perform rendering. Although the task is important, it is tedious and requires tremendous effort. In this paper, we introduce a new problem of domain-specific indoor scene image synthesis, namely neural scene decoration. Given a photograph of an empty ind作者: 拱形面包 時(shí)間: 2025-3-30 16:03 作者: Projection 時(shí)間: 2025-3-30 16:32 作者: Bravura 時(shí)間: 2025-3-30 23:17
,ChunkyGAN: Real Image Inversion via?Segments,al latent representation of the input image, our approach subdivides the input image into a set of smaller components (chunks) specified either manually or automatically using a pre-trained segmentation network. For each chunk, the latent code of a generative network is estimated locally with greate作者: patriot 時(shí)間: 2025-3-31 04:43
GAN Cocktail: Mixing GANs Without Dataset Access,ed for model merging: combining two or more pretrained generative models into a single unified one. In this work we tackle the problem of model merging, given two constraints that often come up in the real world: (1) no access to the original training data, and (2) without increasing the network siz作者: 本土 時(shí)間: 2025-3-31 08:29
,Geometry-Guided Progressive NeRF for?Generalizable and?Efficient Neural Human Rendering,mera views. Though existing NeRF-based methods can synthesize rather realistic details for human body, they tend to produce poor results when the input has self-occlusion, especially for unseen humans under sparse views. Moreover, these methods often require a large number of sampling points for ren作者: oxidize 時(shí)間: 2025-3-31 11:44
Controllable Shadow Generation Using Pixel Height Maps,ot always available. Deep learning-based shadow synthesis methods learn a mapping from the light information to an object’s shadow without explicitly modeling the shadow geometry. Still, they lack control and are prone to visual artifacts. We introduce “Pixel Height", a novel geometry representation作者: 無(wú)聊的人 時(shí)間: 2025-3-31 15:28 作者: Orthodontics 時(shí)間: 2025-3-31 19:38
Subspace Diffusion Generative Models,sary to run this entire process at high dimensionality and incur all the inconveniences thereof. Instead, we restrict the diffusion via projections onto . as the data distribution evolves toward noise. When applied to state-of-the-art models, our framework simultaneously . sample quality—reaching an作者: 貿(mào)易 時(shí)間: 2025-3-31 21:56
,DuelGAN: A Duel Between Two Discriminators Stabilizes the?GAN Training, mode collapse. Built upon the Vanilla GAN’s two-player game between the discriminator . and the generator ., we introduce a peer discriminator . to the min-max game. Similar to previous work using two discriminators, the first role of both ., . is to distinguish between generated samples and real o作者: 拍下盜公款 時(shí)間: 2025-4-1 03:44 作者: entail 時(shí)間: 2025-4-1 08:34
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/c/image/234242.jpg作者: nominal 時(shí)間: 2025-4-1 10:48 作者: HOWL 時(shí)間: 2025-4-1 15:54 作者: 滲入 時(shí)間: 2025-4-1 19:57
