作者: 一瞥 時間: 2025-3-21 20:15 作者: 施舍 時間: 2025-3-22 01:34
https://doi.org/10.1007/978-3-031-72627-9artificial intelligence; computer networks; computer systems; computer vision; education; Human-Computer 作者: aggrieve 時間: 2025-3-22 08:32 作者: CAND 時間: 2025-3-22 10:20 作者: Exaggerate 時間: 2025-3-22 13:44 作者: Exaggerate 時間: 2025-3-22 20:06 作者: 好忠告人 時間: 2025-3-22 23:50 作者: conceal 時間: 2025-3-23 05:24
Therapy with high-energy heavy particles numerous 2D anomaly detection methods have been proposed and have achieved promising results, however, using only the 2D RGB data as input is not sufficient to identify imperceptible geometric surface anomalies. Hence, in this work, we focus on multi-modal anomaly detection. Specifically, we invest作者: 貪婪性 時間: 2025-3-23 08:18
https://doi.org/10.1007/978-3-322-95633-03D scene. However, the quality of its results largely depends on the 2D segmentations, which could be noisy and error-prone, so its performance often drops significantly for complex scenes. In this work, we design a new pipeline coined . based on our .robabilis-tic .ontrastive .usion (PCF) to learn 作者: Flirtatious 時間: 2025-3-23 09:56
Historische Dimensionen des Systembegriffs for grasp generation confines the applications of prior methods in downstream tasks. This paper presents a novel semantic-based grasp generation method, termed., which generates a static human grasp pose by incorporating semantic information into the grasp representation. We introduce a discrete re作者: Pastry 時間: 2025-3-23 14:21 作者: Discrete 時間: 2025-3-23 19:05 作者: 乞丐 時間: 2025-3-24 00:20 作者: Amnesty 時間: 2025-3-24 05:59 作者: 有害處 時間: 2025-3-24 07:58 作者: Diskectomy 時間: 2025-3-24 10:51 作者: Critical 時間: 2025-3-24 17:16 作者: 最低點 時間: 2025-3-24 22:51 作者: 消極詞匯 時間: 2025-3-24 23:44 作者: 混雜人 時間: 2025-3-25 03:42
Allgemeine Untersuchungsmethodenut the need for calibrated lighting or sensors, a notable advancement in the field traditionally hindered by stringent prerequisites and spectral ambiguity. By embracing spectral ambiguity as an advantage, our technique enables the generation of training data without specialized multispectral render作者: tariff 時間: 2025-3-25 10:21 作者: 很是迷惑 時間: 2025-3-25 15:25 作者: Alveolar-Bone 時間: 2025-3-25 18:43
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: 移動 時間: 2025-3-25 23:07 作者: Psychogenic 時間: 2025-3-26 01:06
https://doi.org/10.1007/978-3-642-80605-6d on a single indoor dataset, the improvement is transferable to a variety of indoor datasets and out-of-domain datasets. We hope our study encourages the community to consider injecting 3D awareness when training 2D foundation models. Project page: ..作者: 不滿分子 時間: 2025-3-26 05:02
Historische Dimensionen des Systembegriffse the training of., we compile a large-scale, grasp-text-aligned dataset named., featuring over 300k detailed captions and 50k diverse grasps. Experimental findings demonstrate that.efficiently generates natural human grasps in alignment with linguistic intentions. Our code, models, and dataset are available publicly at: ..作者: Eructation 時間: 2025-3-26 09:32
Behandlungsprinzipien bei akuter Vergiftung,NR allow us to ingenuously exploit the semantic information within and across generalized superpixels. Extensive experiments on various applications validate the effectiveness and efficacy of our S-INR compared to state-of-the-art INR methods.作者: 歡笑 時間: 2025-3-26 13:53
E. Waldschmidt-Leitz,A. K. Ballsenerative model. The proposed model, VFusion3D, trained on nearly 3M synthetic multi-view data, can generate a 3D asset from a single image in seconds and achieves superior performance when compared to current SOTA feed-forward 3D generative models, with users preferring our results over . of the time.作者: BRUNT 時間: 2025-3-26 18:47 作者: 最高峰 時間: 2025-3-27 00:33
, , : Semantic Grasp Generation via?Language Aligned Discretization,e the training of., we compile a large-scale, grasp-text-aligned dataset named., featuring over 300k detailed captions and 50k diverse grasps. Experimental findings demonstrate that.efficiently generates natural human grasps in alignment with linguistic intentions. Our code, models, and dataset are available publicly at: ..作者: 精確 時間: 2025-3-27 03:31 作者: outer-ear 時間: 2025-3-27 05:39
,VFusion3D: Learning Scalable 3D Generative Models from?Video Diffusion Models,enerative model. The proposed model, VFusion3D, trained on nearly 3M synthetic multi-view data, can generate a 3D asset from a single image in seconds and achieves superior performance when compared to current SOTA feed-forward 3D generative models, with users preferring our results over . of the time.作者: 樸素 時間: 2025-3-27 11:37
https://doi.org/10.1007/978-3-642-52015-0encoding for the drags and dataset randomization, the model generalizes well to real images and different categories. Compared to prior motion-controlled generators, we demonstrate much better part-level motion understanding.作者: OASIS 時間: 2025-3-27 16:28 作者: 配置 時間: 2025-3-27 18:32
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: 憎惡 時間: 2025-3-28 01:18
Die Eigenschaften der Staatsgewaltt can faithfully reconstruct an input image. These elements can be intuitively edited by a user, and are decoded by a diffusion model into realistic images. We show the effectiveness of our representation on various image editing tasks, such as object resizing, rearrangement, dragging, de-occlusion, removal, variation, and image composition.作者: 圖表證明 時間: 2025-3-28 03:53 作者: 鄙視讀作 時間: 2025-3-28 08:23
,Editable Image Elements for?Controllable Synthesis,t can faithfully reconstruct an input image. These elements can be intuitively edited by a user, and are decoded by a diffusion model into realistic images. We show the effectiveness of our representation on various image editing tasks, such as object resizing, rearrangement, dragging, de-occlusion, removal, variation, and image composition.作者: 變化無常 時間: 2025-3-28 12:10
,P2P-Bridge: Diffusion Bridges for?3D Point Cloud Denoising,RKitScenes, P2P-Bridge improves by a notable margin over existing methods. Although our method demonstrates promising results utilizing solely point coordinates, we demonstrate that incorporating additional features like RGB information and point-wise DINOV2 features further improves the results.Code and pretrained networks are available at ..作者: CRAMP 時間: 2025-3-28 15:47
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: 終端 時間: 2025-3-28 20:18
Entstehung und Untergang des Staatesoposed to continuously update and refine the interaction between 2D and 3D results, in a cyclic 3D-2D-3D manner. Additionally, Query-group Attention is utilized to strengthen the interaction among 2D queries within each camera group. In the experiments, we evaluate our method on the nuScenes dataset作者: HERE 時間: 2025-3-29 02:19 作者: membrane 時間: 2025-3-29 06:24
Therapy with high-energy heavy particlesse a Local-to-global Self-supervised Feature Adaptation (LSFA) method to finetune the adaptors and learn task-oriented representation toward anomaly detection. Both intra-modal adaptation and cross-modal alignment are optimized from a local-to-global perspective in LSFA to ensure the representation 作者: Pseudoephedrine 時間: 2025-3-29 10:21 作者: enumaerate 時間: 2025-3-29 12:18
https://doi.org/10.1007/978-3-322-95633-0a bio.echanically .ccurate .eural .nverse .ematics solver (MANIKIN) for full-body motion tracking. MANIKIN is based on swivel angle prediction and perfectly matches input poses while avoiding ground penetration. We evaluate MANIKIN in extensive experiments on motion capture datasets and demonstrate 作者: Hectic 時間: 2025-3-29 17:56
https://doi.org/10.1007/978-3-642-52015-0tures. Subsequently, we propose a simple objective to capture the lost information due to normalisation. Our proposed loss component, termed ., motivates each dimension of a student’s feature space to be similar to the corresponding dimension of its teacher. We perform extensive experiments demonstr作者: LAITY 時間: 2025-3-29 19:48
https://doi.org/10.1007/978-3-322-84064-6al-exposure images to guide illuminant estimators, referred to as the dual-exposure feature (DEF). To validate the efficiency of DEF, we employed two illuminant estimators using the proposed DEF: 1) a multilayer perceptron network (MLP), referred to as exposure-based MLP (EMLP), and 2) a modified ve作者: artless 時間: 2025-3-30 01:16 作者: enterprise 時間: 2025-3-30 04:27 作者: Arteriography 時間: 2025-3-30 10:36 作者: fibula 時間: 2025-3-30 12:40
Allgemeine Untersuchungsmethodented methods. Our contributions significantly enhance the capabilities for dynamic surface recovery, particularly in uncalibrated setups, marking a pivotal step forward in the application of photometric stereo across various domains.作者: 怕失去錢 時間: 2025-3-30 18:21
E. Waldschmidt-Leitz,A. K. Balls issue, we propose an optimization strategy that effectively regularizes splat features by modeling them as the outputs of a corresponding implicit neural field. This results in a consistent enhancement of reconstruction quality across various scenarios. Our approach effectively handles static and d作者: GIBE 時間: 2025-3-30 21:44
,SimPB: A Single Model for?2D and?3D Object Detection from?Multiple Cameras,oposed to continuously update and refine the interaction between 2D and 3D results, in a cyclic 3D-2D-3D manner. Additionally, Query-group Attention is utilized to strengthen the interaction among 2D queries within each camera group. In the experiments, we evaluate our method on the nuScenes dataset作者: implore 時間: 2025-3-31 02:00 作者: 事情 時間: 2025-3-31 05:58 作者: 骨 時間: 2025-3-31 11:49 作者: GNAW 時間: 2025-3-31 15:57 作者: CAB 時間: 2025-3-31 19:45 作者: nullify 時間: 2025-4-1 00:41 作者: 征稅 時間: 2025-4-1 03:16
,BAM-DETR: Boundary-Aligned Moment Detection Transformer for?Temporal Sentence Grounding in?Videos, allows the model to focus on desirable regions, enabling precise refinement of moment predictions. Further, we propose a quality-based ranking method, ensuring that proposals with high localization qualities are prioritized over incomplete ones. Experiments on three benchmarks validate the effectiv作者: Counteract 時間: 2025-4-1 07:56
