作者: HEAVY 時間: 2025-3-22 00:16 作者: 哎呦 時間: 2025-3-22 04:15 作者: 咽下 時間: 2025-3-22 04:40 作者: 很是迷惑 時間: 2025-3-22 10:32
,OGNI-DC: Robust Depth Completion with?Optimization-Guided Neural Iterations,el framework for depth completion. The?key to our method is “.ptimization-.uided .eural .terations” (OGNI). It consists of?a recurrent unit that refines a depth gradient field and?a differentiable depth integrator that integrates the depth gradients into a depth map. OGNI-DC exhibits strong generali作者: Engaged 時間: 2025-3-22 15:41 作者: Engaged 時間: 2025-3-22 17:13
Beta-Tuned Timestep Diffusion Model,cent studies indicate that treating?all distributions equally in diffusion model training is sub-optimal.?In this paper, we conduct an in-depth theoretical analysis of?the forward process of diffusion models. Our findings reveal that?the distribution variations are non-uniform throughout the diffusi作者: 大火 時間: 2025-3-22 22:54
,POA: Pre-training Once for?Models of?All Sizes,gies train a single?model of a certain size at one time. Nevertheless, various computation?or storage constraints in real-world scenarios require substantial efforts to develop a series of models with different sizes?to deploy. Thus, in this study, we propose a novel tri-branch self-supervised train作者: Jacket 時間: 2025-3-23 03:16
,Taming Latent Diffusion Model for?Neural Radiance Field Inpainting, editing a reconstructed NeRF with diffusion prior, they remain struggling to synthesize reasonable geometry in completely uncovered regions. One major reason is the high diversity of synthetic contents from the diffusion model, which hinders the radiance field from converging to a crisp and determi作者: 埋葬 時間: 2025-3-23 08:22 作者: ATOPY 時間: 2025-3-23 13:33
,ByteEdit: Boost, Comply and?Accelerate Generative Image Editing,npainting tasks. Despite these strides, the field grapples with inherent challenges, including: i) inferior quality; ii) poor consistency; iii) insufficient instrcution adherence; iv) suboptimal generation efficiency. To address these obstacles, we present ., an innovative feedback learning framewor作者: 虛弱的神經(jīng) 時間: 2025-3-23 16:05
,ProDepth: Boosting Self-supervised Multi-frame Monocular Depth with?Probabilistic Fusion,scene. However, the presence of moving objects in dynamic scenes introduces inevitable inconsistencies, causing misaligned multi-frame feature matching and misleading self-supervision during training. In this paper, we propose a novel framework called ProDepth, which effectively addresses the mismat作者: 職業(yè)拳擊手 時間: 2025-3-23 19:05 作者: 天文臺 時間: 2025-3-23 23:26
,Accelerating Image Super-Resolution Networks with?Pixel-Level Classification,or DNN-based SISR, decomposing images into overlapping patches is typically necessary due to computational constraints. In such patch-decomposing scheme, one can allocate computational resources differently based on each patch’s difficulty to further improve efficiency while maintaining SR performan作者: 賞心悅目 時間: 2025-3-24 04:44 作者: 讓你明白 時間: 2025-3-24 07:50 作者: SOBER 時間: 2025-3-24 13:41
,Click-Gaussian: Interactive Segmentation to?Any 3D Gaussians,y of 3D Gaussian Splatting. However, the current methods suffer from time-consuming post-processing to deal with noisy segmentation output. Also, they struggle to provide detailed segmentation, which is important for fine-grained manipulation of 3D scenes. In this study, we propose Click-Gaussian, w作者: infinite 時間: 2025-3-24 15:16 作者: CLAMP 時間: 2025-3-24 21:24
,DySeT: A?Dynamic Masked Self-distillation Approach for?Robust Trajectory Prediction,address this is via self-supervised pre-training through masked trajectory prediction. However, the existing models rely on uniform random sampling of tokens, which is sub-optimal because it implies that all components of driving scenes are equally informative. In this paper, to enable more robust r作者: comely 時間: 2025-3-25 00:48 作者: 配偶 時間: 2025-3-25 07:10
Conference proceedings 2025uter Vision, ECCV 2024, held in Milan, Italy, during September 29–October 4, 2024...The 2387 papers presented in these proceedings were carefully reviewed and selected from a total of 8585 submissions. They deal with topics such as computer vision; machine learning; deep neural networks; reinforceme作者: Conducive 時間: 2025-3-25 08:17 作者: 盡責(zé) 時間: 2025-3-25 12:00 作者: 易于出錯 時間: 2025-3-25 15:50
https://doi.org/10.1007/978-3-663-00209-3ffusion Model (B-TTDM),?which devises a timestep sampling strategy based on the beta distribution. By choosing the correct parameters, B-TTDM aligns the timestep sampling distribution with the properties of the forward diffusion process. Extensive experiments on different benchmark datasets validate the effectiveness of B-TTDM.作者: Juvenile 時間: 2025-3-25 22:44
https://doi.org/10.1007/978-3-658-16556-7o refine the pixel embeddings.The refined pixel embeddings alleviate the distortion of manifolds, improving the accuracy of anomaly scores. Our extensive experiments show that RWPM consistently improves the performance of the existing anomaly segmentation methods and achieves the best results. Code is available at: ..作者: 持久 時間: 2025-3-26 01:21
,Motion-Prior Contrast Maximization for?Dense Continuous-Time Motion Estimation,lly trained model on the real-world dataset EVIMO2 by 29%. In optical flow estimation, our method elevates a simple UNet to achieve state-of-the-art performance among self-supervised methods on the DSEC optical flow benchmark. Our code is available at ..作者: surrogate 時間: 2025-3-26 07:32
Beta-Tuned Timestep Diffusion Model,ffusion Model (B-TTDM),?which devises a timestep sampling strategy based on the beta distribution. By choosing the correct parameters, B-TTDM aligns the timestep sampling distribution with the properties of the forward diffusion process. Extensive experiments on different benchmark datasets validate the effectiveness of B-TTDM.作者: SEVER 時間: 2025-3-26 11:35
,Random Walk on?Pixel Manifolds for?Anomaly Segmentation of?Complex Driving Scenes,o refine the pixel embeddings.The refined pixel embeddings alleviate the distortion of manifolds, improving the accuracy of anomaly scores. Our extensive experiments show that RWPM consistently improves the performance of the existing anomaly segmentation methods and achieves the best results. Code is available at: ..作者: 狂熱語言 時間: 2025-3-26 15:55 作者: HERE 時間: 2025-3-26 18:15 作者: 令人作嘔 時間: 2025-3-26 21:47 作者: 不近人情 時間: 2025-3-27 01:45 作者: RAFF 時間: 2025-3-27 09:21
Allgemeine Betriebswirtschaftslehreing visual conditions, but state-of-the-art frame-based methods cannot be easily adapted to event data due to the limitations of current event simulators. We introduce a novel self-supervised loss combining the Contrast Maximization framework with a non-linear motion prior in the form of pixel-level作者: agonist 時間: 2025-3-27 13:20
Allgemeine Betriebswirtschaftslehreognition). However, the trainable overhead of ensuring that the domain alignment of CLIP and FSAR is often unbearable. To mitigate this issue, we present an .fficient .ulti-Level .ost-Reasoning .work, namely .. By design, a post-reasoning mechanism is proposed for domain adaptation, which avoids mos作者: 凈禮 時間: 2025-3-27 15:24
https://doi.org/10.1007/978-3-322-88965-2nces poses a significant challenge, mirroring the intuitive reasoning abilities of humans. This work tackles the problem of realistic human insertion in a given background scene termed as .. This task is extremely challenging given the diverse backgrounds, scale, and pose of the generated person and作者: 腐敗 時間: 2025-3-27 19:51 作者: MORPH 時間: 2025-3-28 01:31 作者: 單純 時間: 2025-3-28 04:08
https://doi.org/10.1007/978-3-663-00209-3cent studies indicate that treating?all distributions equally in diffusion model training is sub-optimal.?In this paper, we conduct an in-depth theoretical analysis of?the forward process of diffusion models. Our findings reveal that?the distribution variations are non-uniform throughout the diffusi作者: Alcove 時間: 2025-3-28 08:22 作者: craving 時間: 2025-3-28 11:26 作者: intercede 時間: 2025-3-28 17:15 作者: 小爭吵 時間: 2025-3-28 21:12
Investition und Unternehmensbewertungnpainting tasks. Despite these strides, the field grapples with inherent challenges, including: i) inferior quality; ii) poor consistency; iii) insufficient instrcution adherence; iv) suboptimal generation efficiency. To address these obstacles, we present ., an innovative feedback learning framewor作者: malign 時間: 2025-3-29 01:04 作者: 仔細(xì)閱讀 時間: 2025-3-29 04:19 作者: 受傷 時間: 2025-3-29 10:54
Investition und Unternehmensbewertungor DNN-based SISR, decomposing images into overlapping patches is typically necessary due to computational constraints. In such patch-decomposing scheme, one can allocate computational resources differently based on each patch’s difficulty to further improve efficiency while maintaining SR performan作者: 深淵 時間: 2025-3-29 13:40
Investition und Unternehmensbewertung segmentation methods have been proposed in literature. However, two issues remain to be tackled: 1)?The utilization of large language-vision models (LVM) in semi-supervised 3D semantic segmentation remains under-explored. 2) The unlabeled points with low-confidence predictions are directly discarde作者: 材料等 時間: 2025-3-29 18:50
Investition und Unternehmensbewertung notably transformer-based methods,?have advanced the field, infrared image SR remains a formidable challenge. Due to the inherent characteristics of infrared sensors, such as limited resolution, temperature sensitivity, high?noise levels, and environmental impacts, existing deep learning methods re作者: 幾何學(xué)家 時間: 2025-3-29 23:22 作者: AND 時間: 2025-3-29 23:59
https://doi.org/10.1007/978-3-658-16556-7se functions, accurately predicting the logits of inlier classes for each pixel is crucial for precisely inferring the anomaly score. However, in real-world driving scenarios, the diversity of scenes often results in distorted manifolds of pixel embeddings in the space. This effect is not conducive 作者: Conduit 時間: 2025-3-30 06:18 作者: blight 時間: 2025-3-30 11:02
Computer Vision – ECCV 2024978-3-031-72646-0Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 防止 時間: 2025-3-30 15:33
https://doi.org/10.1007/978-3-031-72646-0artificial intelligence; computer networks; computer systems; computer vision; education; Human-Computer 作者: 違反 時間: 2025-3-30 20:12 作者: 名詞 時間: 2025-3-30 20:58
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/d/image/242314.jpg作者: 富饒 時間: 2025-3-31 01:08 作者: Abrade 時間: 2025-3-31 06:12
,Efficient Few-Shot Action Recognition via?Multi-level Post-reasoning,evel. The ensuing spatiotemporal reasoning module operates on multi-level representations to generate discriminative features. As for matching, the contrasts between text-visual and support-query are integrated to provide comprehensive guidance. The experimental results demonstrate that EMP-Net can 作者: 虛情假意 時間: 2025-3-31 09:54 作者: nominal 時間: 2025-3-31 14:47 作者: DEBT 時間: 2025-3-31 17:42
,POA: Pre-training Once for?Models of?All Sizes,r downstream tasks. Remarkably, the elastic student facilitates the simultaneous pre-training of multiple models with different sizes, which?also acts as an additional ensemble of models of various sizes to enhance representation learning. Extensive experiments, including k-nearest neighbors, linear作者: 織物 時間: 2025-3-31 23:10
,Taming Latent Diffusion Model for?Neural Radiance Field Inpainting,also found the commonly used pixel and perceptual losses are harmful in the NeRF inpainting task. Through rigorous experiments, our framework yields state-of-the-art NeRF inpainting results on various real-world scenes.作者: 綠州 時間: 2025-4-1 03:25 作者: 你敢命令 時間: 2025-4-1 07:14
,ByteEdit: Boost, Comply and?Accelerate Generative Image Editing,rge-scale user evaluations, we demonstrate that ByteEdit surpasses leading generative image editing products, including Adobe, Canva, and MeiTu, in both generation quality and consistency. ByteEdit-Outpainting exhibits a remarkable enhancement of . and . in quality and consistency, respectively, whe作者: Slit-Lamp 時間: 2025-4-1 12:34
,ProDepth: Boosting Self-supervised Multi-frame Monocular Depth with?Probabilistic Fusion, probability distributions of depth candidates from both single-frame and multi-frame cues, modulating the cost volume by adaptively fusing those distributions based on the inferred uncertainty. Additionally, we present a self-supervision loss reweighting strategy that not only masks out incorrect s
