作者: oracle 時間: 2025-3-21 22:29 作者: 爆米花 時間: 2025-3-22 00:55 作者: Gum-Disease 時間: 2025-3-22 05:18
,DGInStyle: Domain-Generalizable Semantic Segmentation with?Image Diffusion Models and?Stylized Semang DGInStyle, we generate a diverse dataset of street scenes, train a domain-agnostic semantic segmentation model on it, and evaluate the model on multiple popular autonomous driving datasets. Our approach consistently increases the performance of several domain generalization methods compared to th作者: Ruptured-Disk 時間: 2025-3-22 09:57 作者: 高度 時間: 2025-3-22 14:46 作者: 高度 時間: 2025-3-22 19:06 作者: 逃避責任 時間: 2025-3-22 21:21 作者: 暖昧關(guān)系 時間: 2025-3-23 01:28 作者: Irrigate 時間: 2025-3-23 07:30
,COSMU: Complete 3D Human Shape from?Monocular Unconstrained Images,e camera, we propose a novel framework to generate complete 3D human shapes. We introduce a novel module to generate 2D multi-view normal maps of the person registered with the target input image. The module consists of body part-based reference selection and body part-based registration. The genera作者: 拒絕 時間: 2025-3-23 12:13
MAP-ADAPT: Real-Time Quality-Adaptive Semantic 3D Maps,different quality based on both the semantic information and the geometric complexity of the scene. Leveraging a semantic SLAM pipeline for pose and semantic estimation, we achieve comparable or superior results to state-of-the-art methods on synthetic and real-world data, while significantly reduci作者: 外表讀作 時間: 2025-3-23 14:54 作者: BILL 時間: 2025-3-23 20:22
,SemiVL: Semi-Supervised Semantic Segmentation with?Vision-Language Guidance, and language. Finally, we propose to handle inherent ambiguities in class labels by instructing the model with language guidance in the form of class definitions. We evaluate SemiVL on 4 semantic segmentation datasets, where it significantly outperforms previous semi-supervised methods. For instanc作者: Alopecia-Areata 時間: 2025-3-24 02:11 作者: pellagra 時間: 2025-3-24 02:33
,Optimal Transport of?Diverse Unsupervised Tasks for?Robust Learning from?Noisy Few-Shot Data,nduct novel auxiliary task selection to ensure the intra-diversity among the unlabeled samples within a task. Domain invariant features are then learned from carefully constructed auxiliary tasks to best recover the original data manifold. We conduct a theoretical analysis to derive novel generaliza作者: dandruff 時間: 2025-3-24 07:41 作者: Gratuitous 時間: 2025-3-24 11:22 作者: Gobble 時間: 2025-3-24 16:37
CO2 Carbon Capture, Storage, and Usespropose a geodesic attention block to effectively incorporate semantic priors into skeletal body deformation to tackle complex body shapes for stylized characters. Since apparel motion can significantly deviate from respective body joints, we propose to model apparel deformation in a non-linear vert作者: 強所 時間: 2025-3-24 19:23
Alternative Energy Sources and Technologies but only lift image features to 3D. More importantly, we demonstrate that . supports arbitrary class prompts, can be easily extended to new datasets, and shows significant potential to improve with increasing amounts of self-labeled data. We release all models and the code.作者: 粗糙 時間: 2025-3-25 03:07
Alternative Energy in the Middle Eastng DGInStyle, we generate a diverse dataset of street scenes, train a domain-agnostic semantic segmentation model on it, and evaluate the model on multiple popular autonomous driving datasets. Our approach consistently increases the performance of several domain generalization methods compared to th作者: EXTOL 時間: 2025-3-25 03:48 作者: subacute 時間: 2025-3-25 10:58
Alternative Energy in the Middle Eastace and the distance of basis scene points to the human mesh. We further introduce a global scene representation learned from a signed distance function (SDF) volume to ensure coherence between the global scene representation and the explicit constraint from the mutual distance. We develop a pipelin作者: Lineage 時間: 2025-3-25 15:15
https://doi.org/10.1057/9781137264589ews are limited. To mitigate these issues, FSGS introduces the synthesis of virtual views to replicate the parallax effect experienced during training, coupled with geometric regularization applied across both actual training and synthesized viewpoints. This strategy ensures that new Gaussians are p作者: 下級 時間: 2025-3-25 19:17 作者: Congregate 時間: 2025-3-25 23:57 作者: 口音在加重 時間: 2025-3-26 04:06
U. Von Schenck,C. Bender-G?tze,B. Koletzkoe camera, we propose a novel framework to generate complete 3D human shapes. We introduce a novel module to generate 2D multi-view normal maps of the person registered with the target input image. The module consists of body part-based reference selection and body part-based registration. The genera作者: 改良 時間: 2025-3-26 08:17
https://doi.org/10.1007/978-3-642-80280-5different quality based on both the semantic information and the geometric complexity of the scene. Leveraging a semantic SLAM pipeline for pose and semantic estimation, we achieve comparable or superior results to state-of-the-art methods on synthetic and real-world data, while significantly reduci作者: Feedback 時間: 2025-3-26 11:57 作者: Statins 時間: 2025-3-26 14:28
Egidio Dansero,Giacomo Pettenati and language. Finally, we propose to handle inherent ambiguities in class labels by instructing the model with language guidance in the form of class definitions. We evaluate SemiVL on 4 semantic segmentation datasets, where it significantly outperforms previous semi-supervised methods. For instanc作者: 苦笑 時間: 2025-3-26 17:45
https://doi.org/10.1007/978-3-319-90409-2ecognize novel classes. Second, we integrate a temporal modeling module into CLIP’s vision encoder to effectively model the spatio-temporal dynamics of video concepts as well as propose a novel regularized finetuning technique to ensure strong open vocabulary classification performance in the video 作者: EVEN 時間: 2025-3-26 23:31 作者: 堅毅 時間: 2025-3-27 03:18
The Work of Seeing Mathematically representation. To aid this process, we propose a feature fusion module to improve both global as well as local information sharing while being robust to errors in the depth predictions. We show that our method can be plugged into various recent UDA methods and consistently improve results across s作者: endocardium 時間: 2025-3-27 07:49 作者: Intuitive 時間: 2025-3-27 11:27 作者: LINE 時間: 2025-3-27 17:36
Life Cycle Algal Biorefinery Designe editing at varying levels of granularity. Capable of interpreting texts ranging from simple labels to detailed narratives, . generates detailed geometries and textures that outperform existing methods in both quantitative and qualitative measures.作者: Slit-Lamp 時間: 2025-3-27 21:12
https://doi.org/10.1007/978-3-319-90409-2ionally, we control the motion trajectory based on rigidity equations formed with the predicted kinematic quantities. In experiments, our method outperforms the state-of-the-arts by capturing physical motion patterns within challenging real-world monocular videos.作者: agnostic 時間: 2025-3-28 00:24
,Nuvo: Neural UV Mapping for?Unruly 3D Representations,valid and well-behaved mapping for just the set of visible points, .. only points that affect the scene’s appearance. We show that our model is robust to the challenges posed by ill-behaved geometry, and that it produces editable UV mappings that can represent detailed appearance.作者: MITE 時間: 2025-3-28 05:42 作者: 大笑 時間: 2025-3-28 06:15 作者: Redundant 時間: 2025-3-28 11:28 作者: Commodious 時間: 2025-3-28 17:09 作者: amphibian 時間: 2025-3-28 20:53
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. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinf作者: 瑣碎 時間: 2025-3-29 02:33 作者: Psychogenic 時間: 2025-3-29 04:09 作者: 吸引力 時間: 2025-3-29 10:55
,Towards High-Quality 3D Motion Transfer with?Realistic Apparel Animation,cus on rigid deformations of characters’ body, neglecting local deformations on the . driven by physical dynamics. They deform apparel the same way as the body, leading to results with limited details and unrealistic artifacts, . body-apparel penetration. In contrast, we present a novel method aimin作者: 疲憊的老馬 時間: 2025-3-29 13:00
,: Open-Vocabulary Generation of?Structured and?Textured 3D Homes, By prompting Large Language Models (LLMs) with designed templates, our approach converts provided textual narratives into amodal structured representations. These representations guarantee consistent and realistic spatial layouts by directing the synthesis of a geometry mesh within defined constrai作者: insincerity 時間: 2025-3-29 15:33 作者: 通便 時間: 2025-3-29 22:44
,DGInStyle: Domain-Generalizable Semantic Segmentation with?Image Diffusion Models and?Stylized Semaugh few-shot fine-tuning, and condition their output on other modalities, such as semantic maps. However, are they usable as large-scale data generators, e.g., to improve tasks in the perception stack, like semantic segmentation? We investigate this question in the context of autonomous driving, and作者: 確定無疑 時間: 2025-3-30 00:44 作者: 沐浴 時間: 2025-3-30 07:56 作者: 過分自信 時間: 2025-3-30 12:00 作者: 不滿分子 時間: 2025-3-30 13:03 作者: Exterior 時間: 2025-3-30 18:27
iMatching: Imperative Correspondence Learning,try and 3D reconstruction. Despite recent progress in data-driven models, feature correspondence learning is still limited by the lack of accurate per-pixel correspondence labels. To overcome this difficulty, we introduce a new self-supervised scheme, imperative learning (IL), for training feature c作者: 或者發(fā)神韻 時間: 2025-3-30 21:12
,COSMU: Complete 3D Human Shape from?Monocular Unconstrained Images,tive of this work is to reproduce high-quality details in regions of the reconstructed human body that are not visible in the input target. The proposed methodology addresses the limitations of existing approaches for reconstructing 3D human shapes from a single image, which cannot reproduce shape d作者: 節(jié)省 時間: 2025-3-31 04:32
MAP-ADAPT: Real-Time Quality-Adaptive Semantic 3D Maps,oal-oriented navigation or object interaction and manipulation). Commonly, 3D semantic reconstruction systems capture the entire scene in the same level of detail. However, certain tasks (.., object interaction) require a fine-grained and high-resolution map, particularly if the objects to interact 作者: 可憎 時間: 2025-3-31 07:33 作者: flamboyant 時間: 2025-3-31 09:16 作者: 瑪瑙 時間: 2025-3-31 16:20
Open Vocabulary Multi-label Video Classification,ect detection and image segmentation. Some recent works have focused on extending VLMs to open vocabulary . action classification in videos. However, previous methods fall short in holistic video understanding which requires the ability to . e.g., . in the video in an open vocabulary setting. We for作者: etiquette 時間: 2025-3-31 20:12
,Optimal Transport of?Diverse Unsupervised Tasks for?Robust Learning from?Noisy Few-Shot Data,ansing offers a viable solution to address noisy labels in the general learning settings, it exacerbates information loss in FSL due to limited training data, resulting in inadequate model training. To best recover the underlying task manifold corrupted by the noisy labels, we resort to learning fro
