作者: 變形詞 時(shí)間: 2025-3-21 21:25
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/d/image/242352.jpg作者: Ataxia 時(shí)間: 2025-3-22 03:20
https://doi.org/10.1007/978-3-031-72992-8artificial intelligence; computer networks; computer systems; computer vision; education; Human-Computer 作者: crumble 時(shí)間: 2025-3-22 08:11 作者: ARENA 時(shí)間: 2025-3-22 11:59 作者: indifferent 時(shí)間: 2025-3-22 13:35
Wenceslau G. Teixeira,Gilvan C. Martins detectors, leveraging the precise geometric information in LiDAR point clouds. However, existing cross-modal knowledge distillation methods tend to overlook the inherent imperfections of LiDAR, such as the ambiguity of measurements on distant or occluded objects, which should not be transferred to 作者: indifferent 時(shí)間: 2025-3-22 20:31
https://doi.org/10.1007/978-90-481-8725-6tend to be highly imbalanced, with a bias towards the “going straight” maneuver. Consequently, learning and accurately predicting turning maneuvers pose significant challenges. In this study, we propose a novel two-stage maneuver learning method that can overcome such strong biases by leveraging two作者: agitate 時(shí)間: 2025-3-23 01:07 作者: 搖曳的微光 時(shí)間: 2025-3-23 03:36
https://doi.org/10.1007/978-90-481-8725-6tically fine-tune a pre-existing object detector while exploring and acquiring images in a new environment without relying on human intervention, i.e., a fully self-supervised approach. In our setting, an agent initially learns to explore?the environment using a pre-trained off-the-shelf detector to作者: 假裝是我 時(shí)間: 2025-3-23 08:52 作者: Offensive 時(shí)間: 2025-3-23 10:43 作者: 物質(zhì) 時(shí)間: 2025-3-23 17:13 作者: itinerary 時(shí)間: 2025-3-23 20:41 作者: 漂浮 時(shí)間: 2025-3-23 22:30
Neue Perspektiven der Medien?sthetik. Distinct from traditional compression techniques, dynamic methods like TinySaver can leverage the difficulty differences to allow certain inputs to complete?their inference processes early, thereby conserving computational resources. Most existing early exit designs are implemented?by attaching ad作者: N防腐劑 時(shí)間: 2025-3-24 05:51 作者: 古董 時(shí)間: 2025-3-24 09:51 作者: 摻和 時(shí)間: 2025-3-24 13:59 作者: 侵蝕 時(shí)間: 2025-3-24 15:16
Reiner Wichert,Birgid Eberhardtriven techniques, accurately mapping between audio signals and 3D facial meshes remains challenging. Direct regression of the entire sequence often leads to over-smoothed results due to the ill-posed nature of the problem. To this end, we propose a progressive learning mechanism that generates 3D fa作者: FIG 時(shí)間: 2025-3-24 19:38 作者: attenuate 時(shí)間: 2025-3-25 01:07 作者: 不足的東西 時(shí)間: 2025-3-25 06:05
Reiner Wichert,Birgid Eberhardt controllable LiDAR data generation is urgently needed, controlling with text is a common practice, but there is little research in this field. To this end, we propose Text2LiDAR, the first efficient, diverse, and text-controllable LiDAR data generation model. Specifically, we design an equirectangu作者: Evacuate 時(shí)間: 2025-3-25 09:07
Advanced Technologies and Societal Changeploiting the benefits of RGB images in the existing vision-based joint perception and prediction (PnP) networks is limited in the perception stage, we delve into how the explicit utilization of the visual semantics in motion forecasting can enhance its performance. Specifically, this work proposes .作者: 眨眼 時(shí)間: 2025-3-25 11:49
Thomas Linner,Bernhard Ellmann,Thomas Bocking-based techniques for image compression and analysis. Previous studies often require training separate codecs to support various bitrate levels, machine tasks, and networks, thus lacking both flexibility and practicality. To address these challenges, we propose a rate-distortion-cognition control作者: 迫擊炮 時(shí)間: 2025-3-25 18:56
0302-9743 reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; motion estimation..978-3-031-72991-1978-3-031-72992-8Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: jet-lag 時(shí)間: 2025-3-25 21:05 作者: Unsaturated-Fat 時(shí)間: 2025-3-26 03:55 作者: Herd-Immunity 時(shí)間: 2025-3-26 06:26 作者: Anal-Canal 時(shí)間: 2025-3-26 11:22
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: CAMP 時(shí)間: 2025-3-26 15:29
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: STAT 時(shí)間: 2025-3-26 18:33
https://doi.org/10.1007/1-4020-2597-1o capture?the contextual information beyond one single sentence. We further prompt the LLM to generate timestamps for each produced caption based?on the timestamps of the subtitles and finally align the generated captions to the video temporally. In this way, we obtain human-style video captions at 作者: 協(xié)奏曲 時(shí)間: 2025-3-27 00:11 作者: moratorium 時(shí)間: 2025-3-27 03:58
https://doi.org/10.1007/978-90-481-8725-6el. To facilitate this, we constructed an in-house intersection-centric trajectory dataset with a well-balanced maneuver distribution. By harnessing the power of heterogeneous datasets, our framework significantly improves maneuver prediction performance, particularly for minority maneuver classes s作者: travail 時(shí)間: 2025-3-27 05:52 作者: Misgiving 時(shí)間: 2025-3-27 13:14
Alessandra F. D. Nava,Sergio L. Mendesrtite matching algorithm, we extend the method to semi-supervised 3D instance segmentation, and finally, with the same building blocks, to dense 3D visual grounding. We demonstrate state-of-the-art results for our semi-supervised method on SemanticKITTI and ScribbleKITTI for 3D semantic segmentation作者: SPASM 時(shí)間: 2025-3-27 17:30 作者: 領(lǐng)袖氣質(zhì) 時(shí)間: 2025-3-27 18:34 作者: 不安 時(shí)間: 2025-3-27 23:09
Alessandra F. D. Nava,Sergio L. MendesComprehensive evaluations across real-world and synthesized datasets demonstrate LECODU’s superior performance compared to state-of-the-art HAI-CC methods. Remarkably, even when relying on unreliable users with?high rates of label noise, LECODU exhibits significant improvement?over both human decisi作者: VAN 時(shí)間: 2025-3-28 05:02
Neue Perspektiven der Medien?sthetikd generic method to model compression. This finding will help?the research community in exploring new compression methods to address the escalating computational demands posed by rapidly evolving?AI models. Our evaluation of this approach in ImageNet-1k classification demonstrates its potential to r作者: 白楊魚 時(shí)間: 2025-3-28 07:29 作者: Conduit 時(shí)間: 2025-3-28 13:52 作者: Motilin 時(shí)間: 2025-3-28 17:12 作者: 克制 時(shí)間: 2025-3-28 21:51
Reiner Wichert,Birgid Eberhardt into a full sequence of 3D talking faces guided by audio features, improving temporal coherence and audio-visual consistency. Extensive experimental comparisons against existing state-of-the-art methods demonstrate the superiority of our approach in generating more vivid and consistent talking face作者: 神圣不可 時(shí)間: 2025-3-29 02:08 作者: 不溶解 時(shí)間: 2025-3-29 04:09
Sebastian Chiriac,Bruno Rosalesher, we design a novel appearance-debiased temporal loss to mitigate the influence of appearance on the temporal training objective. Experimental results show the proposed method can generate videos of diverse appearances for the customized motions. Our method also supports various downstream applic作者: Minatory 時(shí)間: 2025-3-29 09:41
Reiner Wichert,Birgid Eberhardter development in the field and optimize text-controlled generation performance, we construct nuLiDARtext which offers diverse text descriptors for 34,149 LiDAR point clouds from 850 scenes. Experiments on uncontrolled and text-controlled generation in various forms on KITTI-360 and nuScenes dataset作者: Communal 時(shí)間: 2025-3-29 12:39 作者: drusen 時(shí)間: 2025-3-29 16:20 作者: nutrients 時(shí)間: 2025-3-29 22:39 作者: Coronary 時(shí)間: 2025-3-30 03:23
,LabelDistill: Label-Guided Cross-Modal Knowledge Distillation for?Camera-Based 3D Object Detection,f the image detector.Additionally, we introduce feature partitioning, which effectively transfers knowledge from the teacher modality while preserving the distinctive features of the student, thereby maximizing the potential of both modalities. Experimental results demonstrate that our approach impr作者: cardiac-arrest 時(shí)間: 2025-3-30 04:10 作者: 負(fù)擔(dān) 時(shí)間: 2025-3-30 08:22 作者: 鈍劍 時(shí)間: 2025-3-30 15:02
,Bayesian Self-training for?Semi-supervised 3D Segmentation,rtite matching algorithm, we extend the method to semi-supervised 3D instance segmentation, and finally, with the same building blocks, to dense 3D visual grounding. We demonstrate state-of-the-art results for our semi-supervised method on SemanticKITTI and ScribbleKITTI for 3D semantic segmentation作者: 美學(xué) 時(shí)間: 2025-3-30 19:57
,Motion and?Structure from?Event-Based Normal Flow,ometric error term, as an alternative to the full (optical)?flow in solving a family of geometric problems that involve instantaneous first-order kinematics and scene geometry. Furthermore, we develop?a fast linear solver and a continuous-time nonlinear solver on top?of the proposed geometric error 作者: slipped-disk 時(shí)間: 2025-3-30 23:28 作者: dura-mater 時(shí)間: 2025-3-31 02:55
,Learning to?Complement and?to Defer to?Multiple Users,Comprehensive evaluations across real-world and synthesized datasets demonstrate LECODU’s superior performance compared to state-of-the-art HAI-CC methods. Remarkably, even when relying on unreliable users with?high rates of label noise, LECODU exhibits significant improvement?over both human decisi作者: Admonish 時(shí)間: 2025-3-31 07:45 作者: Ornament 時(shí)間: 2025-3-31 09:19 作者: 萬(wàn)靈丹 時(shí)間: 2025-3-31 17:08
,Multi-sentence Grounding for?Long-Term Instructional Video,ltaneously, as a result, the model shows superior performance on a series of multi-sentence grounding tasks, surpassing existing state-of-the-art methods by a significant margin on three public benchmarks, namely, 9.0% on HT-Step, 5.1% on HTM-Align and 1.9% on CrossTask. All codes, models, and the r作者: 擁護(hù) 時(shí)間: 2025-3-31 20:27
,Do Generalised Classifiers , on?Human Drawn Sketches?,straction levels. This is achieved by learning?a codebook of abstraction-specific prompt biases, a weighted combination of which facilitates the representation of sketches across abstraction levels – low abstract edge-maps, medium abstract sketches in TU-Berlin, and highly abstract doodles in QuickD作者: KEGEL 時(shí)間: 2025-3-31 21:50 作者: Radiculopathy 時(shí)間: 2025-4-1 05:11
