作者: 冷漠 時(shí)間: 2025-3-21 22:59 作者: 欺騙世家 時(shí)間: 2025-3-22 03:17
Data Collection-Free Masked Video Modeling,cerns related to privacy, licensing, and inherent biases. Synthesizing data is one of the promising ways to solve these issues, yet pre-training solely on synthetic data has its own challenges. In this paper, we introduce an effective self-supervised learning framework for videos that leverages read作者: 人類學(xué)家 時(shí)間: 2025-3-22 06:00
,Protecting NeRFs’ Copyright via?Plug-And-Play Watermarking Base Model,s intellectual property has become increasingly important. In this paper, we propose ., which adopts a plug-and-play strategy to protect NeRF’s copyright during its creation. NeRFProtector utilizes a pre-trained watermarking base model, enabling NeRF creators to embed binary messages directly while 作者: 追蹤 時(shí)間: 2025-3-22 10:44
,Pixel-Aware Stable Diffusion for?Realistic Image Super-Resolution and?Personalized Stylization,pre-trained text-to-image stable diffusion models provide a potential solution to the challenging realistic image super-resolution (Real-ISR) and image stylization problems with their strong generative priors. However, the existing methods along this line often fail to keep faithful pixel-wise image作者: chisel 時(shí)間: 2025-3-22 13:34 作者: chisel 時(shí)間: 2025-3-22 19:30 作者: 情節(jié)劇 時(shí)間: 2025-3-23 00:45
,AEDNet: Adaptive Embedding and?Multiview-Aware Disentanglement for?Point Cloud Completion,l structure of the object and reconstructs local details. To this end, we propose a global perception and local attention network, termed AEDNet, for point cloud completion. The proposed AEDNet utilizes designed adaptive point cloud embedding and disentanglement (AED) module in both the encoder and 作者: 光滑 時(shí)間: 2025-3-23 03:28
,Synergy of?Sight and?Semantics: Visual Intention Understanding with?CLIP,esource-intensive annotation process. Current leading approaches are held back by the limited amount of labeled data. To mitigate the scarcity of annotated data, we leverage the Contrastive Language-Image Pre-training (CLIP) model, renowned for its wealth knowledge in textual and visual modalities. 作者: 樂(lè)章 時(shí)間: 2025-3-23 08:19 作者: 無(wú)法破譯 時(shí)間: 2025-3-23 10:47 作者: 運(yùn)動(dòng)的我 時(shí)間: 2025-3-23 17:40
,Pathology-Knowledge Enhanced Multi-instance Prompt Learning for Few-Shot?Whole?Slide?Image?Classifig but exhibit suboptimal performance in scenarios with limited learning data. In clinical settings, restricted access to pathology slides is inevitable due to patient privacy concerns and the prevalence of rare or emerging diseases. The emergence of the Few-shot Weakly Supervised WSI Classification 作者: 假裝是我 時(shí)間: 2025-3-23 21:50 作者: 賭博 時(shí)間: 2025-3-23 23:05 作者: Compassionate 時(shí)間: 2025-3-24 02:50 作者: Atheroma 時(shí)間: 2025-3-24 06:35 作者: majestic 時(shí)間: 2025-3-24 13:55 作者: 乳汁 時(shí)間: 2025-3-24 16:33
,ABC Easy as?123: A Blind Counter for?Exemplar-Free Multi-Class Class-Agnostic Counting,ess as they require either a set of examples of the type to be counted or that the query image contains only a single type of object. A significant factor in these shortcomings is the lack of a dataset to properly address counting in settings with more than one kind of object present. To address the作者: locus-ceruleus 時(shí)間: 2025-3-24 20:07
,Category Adaptation Meets Projected Distillation in?Generalized Continual Category Discovery,s. Traditional methods depend on feature distillation to prevent forgetting the old knowledge. However, this strategy restricts the model’s ability to adapt and effectively distinguish new categories. To address this, we introduce a novel technique integrating a learnable projector with feature dist作者: 捐助 時(shí)間: 2025-3-24 23:51 作者: Freeze 時(shí)間: 2025-3-25 04:21 作者: 極力證明 時(shí)間: 2025-3-25 09:00 作者: 銼屑 時(shí)間: 2025-3-25 13:35
Altersabh?ngige Makuladegenerationm in the intrinsic domain. The intrinsic model allows us to train separate networks to extend the dynamic range in the shading domain and to recover lost color details in the albedo domain. We show that dividing the problem into two simpler sub-tasks improves performance in a wide variety of photographs.作者: Mangle 時(shí)間: 2025-3-25 16:01 作者: 文藝 時(shí)間: 2025-3-25 22:40
Gerontologische Einstellungsforschung,nation – dubbed CAMP (Category Adaptation Meets Projected distillation) – significantly improves the balance between learning new information and retaining old. CAMP exhibits superior performance across several GCCD and Class Incremental Learning scenarios. The code is available on ..作者: 債務(wù) 時(shí)間: 2025-3-26 03:16
Intrinsic Single-Image HDR Reconstruction,m in the intrinsic domain. The intrinsic model allows us to train separate networks to extend the dynamic range in the shading domain and to recover lost color details in the albedo domain. We show that dividing the problem into two simpler sub-tasks improves performance in a wide variety of photographs.作者: 草本植物 時(shí)間: 2025-3-26 08:04 作者: 名詞 時(shí)間: 2025-3-26 08:43
,Category Adaptation Meets Projected Distillation in?Generalized Continual Category Discovery,nation – dubbed CAMP (Category Adaptation Meets Projected distillation) – significantly improves the balance between learning new information and retaining old. CAMP exhibits superior performance across several GCCD and Class Incremental Learning scenarios. The code is available on ..作者: Obloquy 時(shí)間: 2025-3-26 14:27 作者: 條約 時(shí)間: 2025-3-26 17:37 作者: 現(xiàn)代 時(shí)間: 2025-3-26 21:23
Conference proceedings 2025orcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; motion estimation..作者: 惰性氣體 時(shí)間: 2025-3-27 03:40
https://doi.org/10.1007/978-1-4684-6945-5creating their NeRF. Our plug-and-play property ensures NeRF creators can flexibly choose NeRF variants without excessive modifications. Leveraging our newly designed progressive distillation, we demonstrate performance on par with several leading-edge neural rendering methods. Our project is available at: ..作者: anagen 時(shí)間: 2025-3-27 09:12 作者: 除草劑 時(shí)間: 2025-3-27 12:59 作者: 積極詞匯 時(shí)間: 2025-3-27 15:14
https://doi.org/10.1007/978-1-4684-6945-5g (CLIP). However, directly incorporating CLIP in forgery detection poses challenges, given its lack of specific prompts and forgery consciousness. To overcome these challenges, we tailor the CLIP model for forgery detection and localization leveraging a noise-assisted prompt learning framework. Thi作者: 小鹿 時(shí)間: 2025-3-27 21:21
Foundations for behaviour logiccerns related to privacy, licensing, and inherent biases. Synthesizing data is one of the promising ways to solve these issues, yet pre-training solely on synthetic data has its own challenges. In this paper, we introduce an effective self-supervised learning framework for videos that leverages read作者: obscurity 時(shí)間: 2025-3-28 01:14 作者: 譏諷 時(shí)間: 2025-3-28 02:10 作者: 假裝是我 時(shí)間: 2025-3-28 09:09 作者: 滲透 時(shí)間: 2025-3-28 11:21 作者: mercenary 時(shí)間: 2025-3-28 16:20 作者: giggle 時(shí)間: 2025-3-28 19:59
Edoardo Midena,Elisabetta Pilottoesource-intensive annotation process. Current leading approaches are held back by the limited amount of labeled data. To mitigate the scarcity of annotated data, we leverage the Contrastive Language-Image Pre-training (CLIP) model, renowned for its wealth knowledge in textual and visual modalities. 作者: choroid 時(shí)間: 2025-3-29 02:32 作者: 草率男 時(shí)間: 2025-3-29 05:42 作者: Melatonin 時(shí)間: 2025-3-29 08:48
https://doi.org/10.1007/978-3-642-20870-6g but exhibit suboptimal performance in scenarios with limited learning data. In clinical settings, restricted access to pathology slides is inevitable due to patient privacy concerns and the prevalence of rare or emerging diseases. The emergence of the Few-shot Weakly Supervised WSI Classification 作者: Folklore 時(shí)間: 2025-3-29 12:00 作者: LEERY 時(shí)間: 2025-3-29 17:41 作者: 使虛弱 時(shí)間: 2025-3-29 20:15 作者: Left-Atrium 時(shí)間: 2025-3-30 02:06 作者: gratify 時(shí)間: 2025-3-30 07:43 作者: carbohydrate 時(shí)間: 2025-3-30 10:36
Frank Berner,Klaus-Peter Schwitzeress as they require either a set of examples of the type to be counted or that the query image contains only a single type of object. A significant factor in these shortcomings is the lack of a dataset to properly address counting in settings with more than one kind of object present. To address the作者: 時(shí)間等 時(shí)間: 2025-3-30 14:18 作者: 豪華 時(shí)間: 2025-3-30 20:13
Computer Vision – ECCV 2024978-3-031-73247-8Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 加花粗鄙人 時(shí)間: 2025-3-31 00:11 作者: detach 時(shí)間: 2025-3-31 02:17
978-3-031-73246-1The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: arboretum 時(shí)間: 2025-3-31 05:33 作者: 控訴 時(shí)間: 2025-3-31 12:54 作者: GROWL 時(shí)間: 2025-3-31 14:54
