作者: pineal-gland 時(shí)間: 2025-3-21 22:40 作者: CHASE 時(shí)間: 2025-3-22 03:14 作者: CT-angiography 時(shí)間: 2025-3-22 07:36
,SLEDGE: Synthesizing Driving Environments with?Generative Models and?Rule-Based Traffic, able to generate agent bounding boxes and lane graphs. The model’s outputs serve as an initial state for rule-based traffic simulation. The unique properties of the entities to be generated for SLEDGE, such as their connectivity and variable count per scene, render the naive application of most mod作者: dithiolethione 時(shí)間: 2025-3-22 11:35
,AFreeCA: Annotation-Free Counting for?All,orks to count objects from specific classes (such as humans or penguins), and counting objects from diverse categories remains a challenge. The availability of robust text-to-image latent diffusion models (LDMs) raises the question of whether these models can be utilized to generate counting dataset作者: ANTIC 時(shí)間: 2025-3-22 14:33
,Adversarially Robust Distillation by?Reducing the?Student-Teacher Variance Gap,versarially robust knowledge distillation has emerged as a principle strategy, facilitating the transfer of robustness from a large-scale teacher model to a lightweight student model. However, existing works focus solely on sample-to-sample alignment of features or predictions between the teacher an作者: ANTIC 時(shí)間: 2025-3-22 19:50
,: Scalable Latent Neural Fields Diffusion for?Speedy 3D Generation,h 2D diffusion has achieved success, a unified 3D diffusion pipeline remains unsettled. This paper introduces a novel framework called.to address this gap and enable fast, high-quality, and generic conditional 3D generation. Our approach harnesses a 3D-aware architecture and variational autoencoder 作者: 內(nèi)部 時(shí)間: 2025-3-22 22:13
,Hierarchical Temporal Context Learning for?Camera-Based Semantic Scene Completion,stream solutions generally leverage temporal information by roughly stacking history frames to supplement the current frame, such straightforward temporal modeling inevitably diminishes valid clues and increases learning difficulty. To address this problem, we present ., a novel .ierarchical .empora作者: Detoxification 時(shí)間: 2025-3-23 04:47
,Equi-GSPR: Equivariant SE(3) Graph Network Model for?Sparse Point Cloud Registration,on approaches have succeeded, leveraging the intrinsic symmetry of point cloud data, including rotation equivariance, has received insufficient attention. This prohibits the model from learning effectively, resulting in a requirement for more training data and increased model complexity. To address 作者: 富饒 時(shí)間: 2025-3-23 09:15 作者: 浪費(fèi)時(shí)間 時(shí)間: 2025-3-23 11:11
,PromptCCD: Learning Gaussian Mixture Prompt Pool for?Continual Category Discovery, data while mitigating the challenge of catastrophic forgetting—an open problem that persists even in conventional, fully supervised continual learning. To address this challenge, we propose PromptCCD, a simple yet effective framework that utilizes a Gaussian Mixture Model (GMM) as a prompting metho作者: irreparable 時(shí)間: 2025-3-23 14:45 作者: floodgate 時(shí)間: 2025-3-23 21:14 作者: 鎮(zhèn)壓 時(shí)間: 2025-3-23 22:32 作者: 搬運(yùn)工 時(shí)間: 2025-3-24 05:41
,NOVUM: Neural Object Volumes for?Robust Object Classification,ects. In this work, we show that explicitly integrating 3D compositional object representations into deep networks for image classification leads to a largely enhanced generalization in out-of-distribution scenarios. In particular, we introduce a novel architecture, referred to as NOVUM, that consis作者: harbinger 時(shí)間: 2025-3-24 06:59 作者: 山崩 時(shí)間: 2025-3-24 11:55 作者: Interferons 時(shí)間: 2025-3-24 15:53 作者: 整體 時(shí)間: 2025-3-24 20:17
,ColorMNet: A Memory-Based Deep Spatial-Temporal Feature Propagation Network for?Video Colorization,sion or recurrently propagating estimated features that will accumulate errors or cannot explore information from far-apart frames, we develop a memory-based feature propagation module that can establish reliable connections with features from far-apart frames and alleviate the influence of inaccura作者: 鳥籠 時(shí)間: 2025-3-25 02:29 作者: 石墨 時(shí)間: 2025-3-25 04:18 作者: 閃光你我 時(shí)間: 2025-3-25 08:50 作者: MUMP 時(shí)間: 2025-3-25 12:56 作者: 暴露他抗議 時(shí)間: 2025-3-25 17:08 作者: OFF 時(shí)間: 2025-3-25 20:47 作者: Figate 時(shí)間: 2025-3-26 01:55
Allergy Prevention and Therapy,atures undergo projection into a subspace via our proposed spectral regularization, with each component controlling a distinct aspect of the generated image. Beyond providing fine-grained control over the generative model, our approach achieves state-of-the-art image generation quality on benchmark datasets, including FFHQ, CelebA-HQ, and AFHQ-V2.作者: 無(wú)節(jié)奏 時(shí)間: 2025-3-26 04:23
Therapieplanung an virtuellen Krebspatientenule for HOI synthesis. Besides, an auto-regressive generation pipeline is also designed to obtain smooth transitions between HOI segments. Experimental results demonstrate the generalization ability to unseen object geometries and temporal compositions. Our data, codes, and models will be publicly available for research purposes.作者: somnambulism 時(shí)間: 2025-3-26 10:26 作者: 你敢命令 時(shí)間: 2025-3-26 14:03
,: Scalable Latent Neural Fields Diffusion for?Speedy 3D Generation,eNet and FFHQ for conditional 3D generation. Moreover, it surpasses existing 3D diffusion methods in terms of inference speed, requiring no per-instance optimization. Video demos can be found on our project webpage: ..作者: 懶惰人民 時(shí)間: 2025-3-26 17:19 作者: 傻 時(shí)間: 2025-3-27 00:25 作者: 分發(fā) 時(shí)間: 2025-3-27 04:01 作者: exclusice 時(shí)間: 2025-3-27 06:43 作者: adulterant 時(shí)間: 2025-3-27 12:02
Mary H. Perdue PhD,Derek M. McKay PhDt storage. Meanwhile, the advantage of RAW images lies in their rich physical information under variable real-world challenging lighting conditions. For computer vision tasks directly based on camera RAW data, most existing studies adopt methods of integrating image signal processor (ISP) with backe作者: overshadow 時(shí)間: 2025-3-27 14:06 作者: Diluge 時(shí)間: 2025-3-27 18:18
Redwan Moqbel PhD, FRCPath,Paige Lacy PhDorks to count objects from specific classes (such as humans or penguins), and counting objects from diverse categories remains a challenge. The availability of robust text-to-image latent diffusion models (LDMs) raises the question of whether these models can be utilized to generate counting dataset作者: monopoly 時(shí)間: 2025-3-28 00:21
Chronic Rhinosinusitis and Nasal Polyposisversarially robust knowledge distillation has emerged as a principle strategy, facilitating the transfer of robustness from a large-scale teacher model to a lightweight student model. However, existing works focus solely on sample-to-sample alignment of features or predictions between the teacher an作者: CUB 時(shí)間: 2025-3-28 03:00 作者: 強(qiáng)制性 時(shí)間: 2025-3-28 09:32 作者: 蝕刻術(shù) 時(shí)間: 2025-3-28 10:34 作者: 獸群 時(shí)間: 2025-3-28 18:30
Anne Marie Morse,Sanjeev V. Kotharemulti-modal information, the challenges are posed when extended to various clinical modalities and practical modality-missing setting due to the inherent modality gaps. To tackle these, we propose an innovative Modality-.rompted He.erogeneous .raph . .mni-modal Learning (GTP-4o), which embeds the nu作者: Dawdle 時(shí)間: 2025-3-28 21:52 作者: 落葉劑 時(shí)間: 2025-3-28 22:54 作者: Oligarchy 時(shí)間: 2025-3-29 04:49
Allergy Prevention and Therapy,aditional methods relying on supervision signals or post-processing for latent feature disentanglement, our proposed technique enables unsupervised learning using only image data through contrastive feature categorization and spectral regularization. In our framework, the discriminator constructs ge作者: 郊外 時(shí)間: 2025-3-29 09:09 作者: 沐浴 時(shí)間: 2025-3-29 14:34 作者: Expiration 時(shí)間: 2025-3-29 15:59 作者: 配偶 時(shí)間: 2025-3-29 21:40
Therapieplanung an virtuellen Krebspatientenmans interacting with a single object while neglecting the ubiquitous manipulation of multiple objects. Thus, we propose ., a large-scale MoCap dataset of full-body human interacting with multiple objects, containing . 4D HOI sequences and . 3D HOI frames. We also annotate . with detailed textual de作者: Project 時(shí)間: 2025-3-30 02:21
Mathematik und intelligente Materialiencapability for image reconstruction. Nevertheless, existing implicit representation approaches only focus on building continuous appearance mapping, ignoring the continuities of the semantic information across pixels. Consequently, achieving the desired reconstruction results becomes challenging whe作者: 笨拙的你 時(shí)間: 2025-3-30 06:07 作者: PATHY 時(shí)間: 2025-3-30 09:25 作者: 誤傳 時(shí)間: 2025-3-30 13:53
https://doi.org/10.1007/978-3-031-73235-5artificial intelligence; computer networks; computer systems; computer vision; education; Human-Computer 作者: MAPLE 時(shí)間: 2025-3-30 17:49
978-3-031-73234-8The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: ethnology 時(shí)間: 2025-3-30 21:24
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/d/image/242308.jpg作者: adduction 時(shí)間: 2025-3-31 01:26
,Mahalanobis Distance-Based Multi-view Optimal Transport for?Multi-view Crowd Localization,hose long-axis and short-axis directions are guided by the view ray direction. Second, the object-to-camera distance in each view is used to adjust the optimal transport cost of each location further, where the wrong predictions far away from the camera are more heavily penalized. Finally, we propos作者: Living-Will 時(shí)間: 2025-3-31 08:14
,RAW-Adapter: Adapting Pre-trained Visual Model to?Camera RAW Images,ubsequent high-level networks. Additionally, RAW-Adapter is a general framework that could be used in various computer vision frameworks. Abundant experiments under different lighting conditions have shown our algorithm’s state-of-the-art (SOTA) performance, demonstrating its effectiveness and effic作者: Generalize 時(shí)間: 2025-3-31 12:32 作者: 不能平靜 時(shí)間: 2025-3-31 15:13
,AFreeCA: Annotation-Free Counting for?All,y classifier-guided method for dividing an image into patches containing objects that can be reliably counted. Consequently, we can generate counting data for any type of object and count them in an unsupervised manner. Our approach outperforms unsupervised and few-shot alternatives and is not restr作者: 連詞 時(shí)間: 2025-3-31 17:55
,Adversarially Robust Distillation by?Reducing the?Student-Teacher Variance Gap,nder an increasing perturbation radius) correlates negatively with the gap between the feature variance evaluated on testing adversarial samples and testing clean samples. Such a negative correlation exhibits a strong linear trend, suggesting that aligning the feature covariance of the student model作者: Alienated 時(shí)間: 2025-3-31 23:42
,Hierarchical Temporal Context Learning for?Camera-Based Semantic Scene Completion,ne-grained contextual correspondence modeling. Subsequently, to dynamically compensate for incomplete observations, we adaptively refine the feature sampling locations based on initially identified locations with high affinity and their neighboring relevant regions. Our method ranks . on the Semanti作者: 虛度 時(shí)間: 2025-4-1 04:43
,Equi-GSPR: Equivariant SE(3) Graph Network Model for?Sparse Point Cloud Registration,ure descriptors easily. Experiments conducted on the 3DMatch and KITTI datasets exhibit the compelling and robust performance of our model compared to state-of-the-art approaches, while the model complexity remains relatively low at the same time.作者: thyroid-hormone 時(shí)間: 2025-4-1 06:12 作者: bibliophile 時(shí)間: 2025-4-1 10:20
,PromptCCD: Learning Gaussian Mixture Prompt Pool for?Continual Category Discovery,alized Category Discovery (GCD) to CCD and benchmark state-of-the-art methods on diverse public datasets. PromptCCD significantly outperforms existing methods, demonstrating its effectiveness. Project page: ..作者: Aqueous-Humor 時(shí)間: 2025-4-1 17:20
