作者: 有特色 時間: 2025-3-21 20:55
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/d/image/242301.jpg作者: 支架 時間: 2025-3-22 01:25 作者: 廚房里面 時間: 2025-3-22 05:38
978-3-031-73015-3The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: 和平主義 時間: 2025-3-22 10:09
Crittografia e Interazioni affidabiling splines in computer animation, our Spline-based Transformers embed an input sequence of elements as a smooth trajectory in latent space. Overcoming drawbacks of positional encoding such as sequence length extrapolation, Spline-based Transformers also provide a novel way for users to interact with作者: 博識 時間: 2025-3-22 16:43
Non computabilità e indecidibilitàtext-to-image generation ability of 2D diffusion model has significantly promoted this task, by converting it into a texture optimization process guided by multi-view synthesized images, where the generation of high-quality and multi-view consistency images becomes the key issue. State-of-the-art me作者: 博識 時間: 2025-3-22 17:39 作者: semiskilled 時間: 2025-3-23 00:32
https://doi.org/10.1007/978-3-540-33979-3en contains excessive noise, whereas radar point clouds retain only limited information. In this work, we holistically treat the sparse nature of radar data by introducing an adaptive subsampling method together with a tailored network architecture that exploits the sparsity patterns to discover glo作者: crescendo 時間: 2025-3-23 02:31
https://doi.org/10.1007/978-3-319-24061-9ffusion models often struggle to produce images that accurately reflect the intended semantics of the associated text prompts. We examine cross-attention layers in diffusion models and observe a propensity for these layers to disproportionately focus on certain tokens during the generation process, 作者: 大喘氣 時間: 2025-3-23 06:41
Second Language Learning and Teachingels in just 1–4 steps while maintaining high image quality. We use score distillation to leverage large-scale off-the-shelf image diffusion models as a teacher signal in combination with an adversarial loss to ensure high image fidelity even in the low-step regime of one or two sampling steps. Our a作者: Estrogen 時間: 2025-3-23 09:43 作者: 逢迎春日 時間: 2025-3-23 14:16 作者: neurologist 時間: 2025-3-23 21:07 作者: Externalize 時間: 2025-3-24 00:01
We’re All Mad Here: Alice Goes to Gothamredominantly been anchored in text-based interactions. The evolution of multimodal conversational AI, leveraging vast amounts of image-text data from diverse sources, marks a significant stride forward. However, the application of such advanced vision-language models in the agricultural domain, part作者: Morose 時間: 2025-3-24 04:54 作者: Magnificent 時間: 2025-3-24 10:03 作者: 遭受 時間: 2025-3-24 14:00 作者: 必死 時間: 2025-3-24 17:48 作者: Facilities 時間: 2025-3-24 21:23 作者: homeostasis 時間: 2025-3-25 00:16 作者: 單調女 時間: 2025-3-25 06:42 作者: multiply 時間: 2025-3-25 10:07
Mayank Gautam,Xian-hong Ge,Zai-yun Liusing style transfer techniques. To protect styles, some researchers use adversarial attacks to safeguard artists’ artistic style images. Prior methods only considered defending against all style transfer models, but artists may allow specific models to transfer their artistic styles properly. To me作者: VALID 時間: 2025-3-25 15:30
C. Toker,B. Uzun,F. O. Ceylan,C. Iktene for many settings, as they compute self-attention in each layer which suffers from quadratic computational complexity in the number of tokens. On the other hand, spatial information in images and spatio-temporal information in videos is usually sparse and redundant. In this work, we introduce Look作者: DRAFT 時間: 2025-3-25 15:55
Adversarial Diffusion Distillation,nalyses show that our model clearly outperforms existing few-step methods (GANs, Latent Consistency Models) in a single step and reaches the performance of state-of-the-art diffusion models (SDXL) in only four steps. ADD is the first method to unlock single-step, real-time image synthesis with foundation models.作者: GROWL 時間: 2025-3-25 20:23
Conference proceedings 2025nt 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: Substance-Abuse 時間: 2025-3-26 03:15
Second Language Learning and Teachingnalyses show that our model clearly outperforms existing few-step methods (GANs, Latent Consistency Models) in a single step and reaches the performance of state-of-the-art diffusion models (SDXL) in only four steps. ADD is the first method to unlock single-step, real-time image synthesis with foundation models.作者: TEN 時間: 2025-3-26 07:35
Crittografia e Interazioni affidabilirior performance of our approach in comparison to conventional positional encoding on a variety of datasets, ranging from synthetic 2D to large-scale real-world datasets of images, 3D shapes, and animations.作者: anniversary 時間: 2025-3-26 11:52 作者: motivate 時間: 2025-3-26 13:34
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: Exaggerate 時間: 2025-3-26 19:01 作者: 憤憤不平 時間: 2025-3-26 22:50
We’re All Mad Here: Alice Goes to Gothamcy and the ability to maintain semantic coherence across objects. Experiments show that we are 22.3% ahead of CLIP on average on 9 segmentation benchmarks, outperforming existing state-of-the-art training-free methods. The code are made publicly available at ..作者: Monocle 時間: 2025-3-27 05:09 作者: Jingoism 時間: 2025-3-27 07:58 作者: photophobia 時間: 2025-3-27 12:18
,Explore the?Potential of?CLIP for?Training-Free Open Vocabulary Semantic Segmentation,cy and the ability to maintain semantic coherence across objects. Experiments show that we are 22.3% ahead of CLIP on average on 9 segmentation benchmarks, outperforming existing state-of-the-art training-free methods. The code are made publicly available at ..作者: Pcos971 時間: 2025-3-27 13:48
,Learning Where to?Look: Self-supervised Viewpoint Selection for?Active Localization Using Geometricrk tailored for real-world robotics applications. Our results demonstrate that our method performs better than the existing one, targeting similar problems and generalizing on synthetic and real data. We also release an open-source implementation to benefit the community at ..作者: 使?jié)M足 時間: 2025-3-27 19:00 作者: 哥哥噴涌而出 時間: 2025-3-27 22:42
0302-9743 ce on Computer 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; r作者: Aviary 時間: 2025-3-28 05:21
Non computabilità e indecidibilità on learned .seudo .D .uidance. The key idea of P3G is to first learn a coarse but consistent texture, to serve as a global semantics guidance for encouraging the consistency between images generated on different views. To this end, we incorporate pre-trained text-to-image diffusion models and multi作者: Hypomania 時間: 2025-3-28 08:48
Introduzione e revisione storicaep-wise action labels are costly and tedious to obtain in practice. We mitigate this problem by leveraging synthetic-to-real transfer learning. Specifically, our model is first pre-trained on synthetic data with full supervision from the available action labels. We then circumvent the requirement fo作者: COLON 時間: 2025-3-28 14:27 作者: expound 時間: 2025-3-28 15:44
https://doi.org/10.1007/978-3-319-24061-9approaches across various datasets, evaluation metrics, and diffusion models. Experiment results show that our method consistently outperforms other baselines, yielding images that more faithfully reflect the desired concepts with reduced computation overhead. Code is available at ..作者: 集聚成團 時間: 2025-3-28 20:39 作者: Blanch 時間: 2025-3-29 00:57 作者: 保守黨 時間: 2025-3-29 05:22 作者: 矛盾 時間: 2025-3-29 10:50
,Children’s Books, Childhood and Modernism,cts the missing embedding through prompt tuning, leveraging information from available modalities. We evaluate our approach on several multimodal benchmark datasets and demonstrate its effectiveness and robustness across various scenarios of missing modalities.作者: 步兵 時間: 2025-3-29 13:26 作者: Hyperalgesia 時間: 2025-3-29 19:29 作者: 武器 時間: 2025-3-29 21:30 作者: Overstate 時間: 2025-3-30 01:16 作者: arthrodesis 時間: 2025-3-30 05:49
Mayank Gautam,Xian-hong Ge,Zai-yun LiASPS requires training only once; during usage, there is no need to see any style transfer models again. Meanwhile, it ensures that the visual quality of the authorized model is unaffected by perturbations. Experimental results demonstrate that our method effectively defends against unauthorized mod作者: affluent 時間: 2025-3-30 09:24
C. Toker,B. Uzun,F. O. Ceylan,C. Iktennabled through a bidirectional cross-attention mechanism. The approach offers multiple advantages - (a) easy to implement on standard ML accelerators (GPUs/TPUs) via standard high-level operators, (b) applicable to standard ViT and its variants, thus generalizes to various tasks, (c) can handle diff作者: Adulate 時間: 2025-3-30 13:40
,Learning Pseudo 3D Guidance for?View-Consistent Texturing with?2D Diffusion, on learned .seudo .D .uidance. The key idea of P3G is to first learn a coarse but consistent texture, to serve as a global semantics guidance for encouraging the consistency between images generated on different views. To this end, we incorporate pre-trained text-to-image diffusion models and multi作者: Subdue 時間: 2025-3-30 17:41 作者: Ventilator 時間: 2025-3-30 22:06
,SparseRadNet: Sparse Perception Neural Network on?Subsampled Radar Data,o combine features from both branches. Experiments on the RADIal dataset show that our SparseRadNet exceeds state-of-the-art (SOTA) performance in object detection and achieves close to SOTA accuracy in freespace segmentation, meanwhile using sparse subsampled input data.作者: endoscopy 時間: 2025-3-31 04:19 作者: 血統(tǒng) 時間: 2025-3-31 08:56 作者: ALTER 時間: 2025-3-31 13:00
,Explain via?Any Concept: Concept Bottleneck Model with?Open Vocabulary Concepts,ifier on the downstream dataset; (3) Reconstructing the trained classification head via any set of user-desired textual concepts encoded by CLIP’s text encoder. To reveal potentially missing concepts from users, we further propose to iteratively find the closest concept embedding to the residual par作者: 使閉塞 時間: 2025-3-31 15:31 作者: 捐助 時間: 2025-3-31 19:57
,Missing Modality Prediction for?Unpaired Multimodal Learning via?Joint Embedding of?Unimodal Modelscts the missing embedding through prompt tuning, leveraging information from available modalities. We evaluate our approach on several multimodal benchmark datasets and demonstrate its effectiveness and robustness across various scenarios of missing modalities.作者: metropolitan 時間: 2025-4-1 00:13
,Improving Diffusion Models for?Authentic Virtual Try-on in?the?Wild, layer. In addition, we provide detailed textual prompts for both garment and person images to enhance the authenticity of the generated visuals. Finally, we present a customization method using a pair of person-garment images, which significantly improves fidelity and authenticity. Our experimental作者: follicular-unit 時間: 2025-4-1 05:35 作者: 受辱 時間: 2025-4-1 08:46
LISO: Lidar-Only Self-supervised 3D Object Detection,erate, track, and iteratively refine pseudo ground truth. We demonstrate the effectiveness of our approach for multiple SOTA?object detection networks across multiple real-world datasets. Code will be released (.).作者: cumulative 時間: 2025-4-1 12:09 作者: 實現(xiàn) 時間: 2025-4-1 16:44
Using My Artistic Style? You Must Obtain My Authorization,ASPS requires training only once; during usage, there is no need to see any style transfer models again. Meanwhile, it ensures that the visual quality of the authorized model is unaffected by perturbations. Experimental results demonstrate that our method effectively defends against unauthorized mod作者: Recessive 時間: 2025-4-1 21:55 作者: 大炮 時間: 2025-4-2 02:04
Spline-Based Transformers,ng splines in computer animation, our Spline-based Transformers embed an input sequence of elements as a smooth trajectory in latent space. Overcoming drawbacks of positional encoding such as sequence length extrapolation, Spline-based Transformers also provide a novel way for users to interact with作者: 反復拉緊 時間: 2025-4-2 06:07
,Learning Pseudo 3D Guidance for?View-Consistent Texturing with?2D Diffusion,text-to-image generation ability of 2D diffusion model has significantly promoted this task, by converting it into a texture optimization process guided by multi-view synthesized images, where the generation of high-quality and multi-view consistency images becomes the key issue. State-of-the-art me作者: 招致 時間: 2025-4-2 07:26
