作者: SLAG 時間: 2025-3-22 00:12 作者: 污穢 時間: 2025-3-22 02:57
AUTO3D: Novel View Synthesis Through Unsupervisely Learned Variational Viewpoint and Global 3D Reprhe relative-pose in a prior distribution. In various applications, we demonstrate that our model can achieve comparable or even better results than pose/3D model-supervised learning-based novel view synthesis (NVS) methods with any number of input views.作者: Panacea 時間: 2025-3-22 08:02 作者: 吞下 時間: 2025-3-22 10:40
Soft Anchor-Point Object Detection,evels, respectively. To evaluate the effectiveness, we train a single-stage anchor-free detector called Soft Anchor-Point Detector (SAPD). Experiments show that our concise SAPD pushes the envelope of speed/accuracy trade-off to a new level, outperforming recent state-of-the-art anchor-free and anch作者: synovitis 時間: 2025-3-22 14:17
Beyond Fixed Grid: Learning Geometric Image Representation with a Deformable Grid, the output layers for the task of object mask annotation, and show that reasoning about object boundaries on our predicted polygonal grid leads to more accurate results over existing pixel-wise and curve-based approaches. We finally showcase . as a standalone module for unsupervised image partition作者: synovitis 時間: 2025-3-22 18:32 作者: probate 時間: 2025-3-23 00:12
Joint Learning of Social Groups, Individuals Action and Sub-group Activities in Videos,-of-the-art results on two widely adopted benchmarks for the traditional group activity recognition task?(assuming individuals of the scene form a single group and predicting a single group activity label for the scene); iii) we introduce new annotations on an existing group activity dataset, re-pur作者: 樹木中 時間: 2025-3-23 01:33 作者: ABIDE 時間: 2025-3-23 07:44
Relative Pose Estimation of Calibrated Cameras with Known , Invariants,ion constrained by . invariants, we also present a comprehensive study of existing polynomial formulations for relative pose estimation and discover their relationship. Different formulations are carefully chosen for each proposed problems to achieve best efficiency. Experiments on synthetic and rea作者: 預(yù)防注射 時間: 2025-3-23 10:17
Sequential Convolution and Runge-Kutta Residual Architecture for Image Compressed Sensing,as a discrete dynamical system. Finally, the implementation of RK-CCSNet achieves state-of-the-art performance on influential benchmarks with respect to prestigious baselines, and all the codes are available at ..作者: 尖 時間: 2025-3-23 16:51
Deep Hough Transform for Semantic Line Detection,o spotting individual points in the parametric domain, making the post-processing steps, .non-maximal suppression, more efficient. Furthermore, our method makes it easy to extract contextual line features, that are critical to accurate line detection. Experimental results on a public dataset demonst作者: Peak-Bone-Mass 時間: 2025-3-23 19:55
Structured Landmark Detection via Topology-Adapting Deep Graph Learning,te-of-the-art approaches across all studied datasets indicating the superior performance in both robustness and accuracy. Qualitative visualizations of the learned graph topologies demonstrate a physically plausible connectivity laying behind the landmarks.作者: optional 時間: 2025-3-24 02:02
3D Human Shape and Pose from a Single Low-Resolution Image with Self-Supervised Learning,mpractical in many realistic applications..To address the above issues, this paper proposes a novel algorithm called RSC-Net, which consists of a Resolution-aware network, a Self-supervision loss, and a Contrastive learning scheme. The proposed network is able to learn the 3D body shape and pose acr作者: BILK 時間: 2025-3-24 04:47 作者: 懦夫 時間: 2025-3-24 10:00
The Econometrics of Demand Systemshus constraining the search only in the feasible domain. In addition, a differentiable Prob-1 regularizer is proposed to ensure learning with NAS is reasonable. A distribution reshaping training strategy is also used to make training more stable. BP-NAS sets new state of the arts on both classificat作者: 審問 時間: 2025-3-24 12:54
The Importance of Socioeconomic Variablesly coherent talking-head videos with natural head movements. Thoughtful experiments on several standard benchmarks demonstrate that our method achieves significantly better results than the state-of-the-art methods in both quantitative and qualitative comparisons. The code is available on ..作者: BAIL 時間: 2025-3-24 16:22
The Econometrics of Demand Systemshe relative-pose in a prior distribution. In various applications, we demonstrate that our model can achieve comparable or even better results than pose/3D model-supervised learning-based novel view synthesis (NVS) methods with any number of input views.作者: CAGE 時間: 2025-3-24 21:54
The Econometrics of Demand Systemsrnable pose backbone exploiting the topology of human body, and (ii) a coupler to provide joint spatio-temporal attention weights across a video. Experiments (Code/models: .) show that VPN outperforms the state-of-the-art results for action classification on a large scale human activity dataset: ., 作者: 下邊深陷 時間: 2025-3-24 23:43
https://doi.org/10.1057/9780230626317evels, respectively. To evaluate the effectiveness, we train a single-stage anchor-free detector called Soft Anchor-Point Detector (SAPD). Experiments show that our concise SAPD pushes the envelope of speed/accuracy trade-off to a new level, outperforming recent state-of-the-art anchor-free and anch作者: cinder 時間: 2025-3-25 06:01
Régis Bourbonnais,Sophie Méritet the output layers for the task of object mask annotation, and show that reasoning about object boundaries on our predicted polygonal grid leads to more accurate results over existing pixel-wise and curve-based approaches. We finally showcase . as a standalone module for unsupervised image partition作者: 熱烈的歡迎 時間: 2025-3-25 08:03 作者: zonules 時間: 2025-3-25 12:54 作者: 全面 時間: 2025-3-25 18:07
Models with Endogenous Regressors,ts of the same person. ZoomNet is able to significantly outperform existing methods on the proposed COCO-WholeBody dataset. Extensive experiments show that COCO-WholeBody not only can be used to train deep models from scratch for whole-body pose estimation but also can serve as a powerful pre-traini作者: Measured 時間: 2025-3-25 20:27 作者: Mindfulness 時間: 2025-3-26 01:40
Bo Honoré,Francis Vella,Marno Verbeekas a discrete dynamical system. Finally, the implementation of RK-CCSNet achieves state-of-the-art performance on influential benchmarks with respect to prestigious baselines, and all the codes are available at ..作者: 厭食癥 時間: 2025-3-26 04:50
Unit Roots and Cointegration in Panelso spotting individual points in the parametric domain, making the post-processing steps, .non-maximal suppression, more efficient. Furthermore, our method makes it easy to extract contextual line features, that are critical to accurate line detection. Experimental results on a public dataset demonst作者: 結(jié)合 時間: 2025-3-26 08:36 作者: 信條 時間: 2025-3-26 14:17
Panel Data with Measurement Errorsmpractical in many realistic applications..To address the above issues, this paper proposes a novel algorithm called RSC-Net, which consists of a Resolution-aware network, a Self-supervision loss, and a Contrastive learning scheme. The proposed network is able to learn the 3D body shape and pose acr作者: nocturnal 時間: 2025-3-26 19:35
Hashem Pesaran,Ron Smith,Kyung So Imdictive power of specialized features while retaining the universal applicability of domain-invariant features. We demonstrate competitive performance compared to naive baselines and state-of-the-art methods on both PACS and DomainNet (Our code is available at .).作者: 有角 時間: 2025-3-26 21:13 作者: Mawkish 時間: 2025-3-27 03:23
0302-9743 processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation..?..?.978-3-030-58544-0978-3-030-58545-7Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 容易生皺紋 時間: 2025-3-27 08:54 作者: Nomogram 時間: 2025-3-27 10:19
Maurice J. G. Bun,Felix Chan,Mark N. Harrisrinsic supervisions. Also, we develop an effective momentum metric learning scheme with the .-hard negative mining to boost the network generalization ability. We demonstrate the effectiveness of our approach on two standard object recognition benchmarks VLCS and PACS, and show that our EISNet achieves state-of-the-art performance.作者: Osmosis 時間: 2025-3-27 15:39
Hashem Pesaran,Ron Smith,Kyung So Imelf, rather than from the rest of the dataset. We demonstrate that our framework enables one-sided translation in the unpaired image-to-image translation setting, while improving quality and reducing training time. In addition, our method can even be extended to the training setting where each “domain” is only a single image.作者: arrogant 時間: 2025-3-27 20:22
Part-Aware Prototype Network for Few-Shot Semantic Segmentation,. We develop a novel graph neural network model to generate and enhance the proposed part-aware prototypes based on labeled and unlabeled images. Extensive experimental evaluations on two benchmarks show that our method outperforms the prior art with a sizable margin (Code is available at: .).作者: violate 時間: 2025-3-28 00:58 作者: chlorosis 時間: 2025-3-28 06:08
Contrastive Learning for Unpaired Image-to-Image Translation,elf, rather than from the rest of the dataset. We demonstrate that our framework enables one-sided translation in the unpaired image-to-image translation setting, while improving quality and reducing training time. In addition, our method can even be extended to the training setting where each “domain” is only a single image.作者: Keratin 時間: 2025-3-28 09:20 作者: 一再遛 時間: 2025-3-28 14:06
Projections of Future Consumption in Finlandnd segmentation module which helps to involve relevant points for foreground masking. Extensive experiments on KITTI dataset demonstrate that our simple yet effective framework outperforms other state-of-the-arts by a large margin.作者: 匍匐 時間: 2025-3-28 15:47
Monocular 3D Object Detection via Feature Domain Adaptation,nd segmentation module which helps to involve relevant points for foreground masking. Extensive experiments on KITTI dataset demonstrate that our simple yet effective framework outperforms other state-of-the-arts by a large margin.作者: 放縱 時間: 2025-3-28 20:48
0302-9743 uter Vision, ECCV 2020, which was planned to be held in Glasgow, UK, during August 23-28, 2020. The conference was held virtually due to the COVID-19 pandemic..The 1360 revised papers presented in these proceedings were carefully reviewed and selected from a total of 5025 submissions. The papers dea作者: Outspoken 時間: 2025-3-28 23:01 作者: PANIC 時間: 2025-3-29 04:37
Monocular 3D Object Detection via Feature Domain Adaptation, approaches. In this paper, we propose a novel domain adaptation based monocular 3D object detection framework named DA-3Ddet, which adapts the feature from unsound image-based pseudo-LiDAR domain to the accurate real LiDAR domain for performance boosting. In order to solve the overlooked problem of作者: Mingle 時間: 2025-3-29 11:05 作者: 宣傳 時間: 2025-3-29 11:47
AUTO3D: Novel View Synthesis Through Unsupervisely Learned Variational Viewpoint and Global 3D Reprdinates, we construct an end-to-end trainable conditional variational framework to disentangle the unsupervisely learned relative-pose/rotation and implicit global 3D representation (shape, texture and the origin of viewer-centered coordinates, etc.). The global appearance of the 3D object is given 作者: eucalyptus 時間: 2025-3-29 16:45
VPN: Learning Video-Pose Embedding for Activities of Daily Living,tio-temporal patterns and (ii) similar visual patterns varying with time. Therefore, ADL may look very similar and often necessitate to look at their fine-grained details to distinguish them. Because the recent spatio-temporal 3D ConvNets are too rigid to capture the subtle visual patterns across an作者: aesthetician 時間: 2025-3-29 21:24
Soft Anchor-Point Object Detection,at opposite edges of the speed-accuracy trade-off, with anchor-point detectors having the speed advantage. In this work, we boost the performance of the anchor-point detector over the key-point counterparts while maintaining the speed advantage. To achieve this, we formulate the detection problem fr作者: 細(xì)胞 時間: 2025-3-30 03:50 作者: 鄙視讀作 時間: 2025-3-30 08:07
Soft Expert Reward Learning for Vision-and-Language Navigation,ominant methods based on supervised learning clone expert’s behaviours and thus perform better on seen environments, while showing restricted performance on unseen ones. Reinforcement Learning (RL) based models show better generalisation ability but have issues as well, requiring large amount of man作者: COLIC 時間: 2025-3-30 12:17 作者: 大喘氣 時間: 2025-3-30 13:52 作者: 譏諷 時間: 2025-3-30 19:35 作者: 平躺 時間: 2025-3-30 23:48 作者: Indigence 時間: 2025-3-31 03:24
Relative Pose Estimation of Calibrated Cameras with Known , Invariants,pose estimation problem for a calibrated camera constrained by known . invariant, which involves 5 minimal problems in?total. These problems reduces the minimal number of point pairs for relative pose estimation and improves the estimation efficiency and robustness. The . invariant constraints can c作者: adipose-tissue 時間: 2025-3-31 06:49 作者: 膽大 時間: 2025-3-31 10:29
Deep Hough Transform for Semantic Line Detection, take line detection as a special case of object detection, while neglect the inherent characteristics of lines, leading to less efficient and suboptimal results. We propose a one-shot end-to-end framework by incorporating the classical Hough transform into deeply learned representations. By paramet作者: 使堅(jiān)硬 時間: 2025-3-31 13:28
Structured Landmark Detection via Topology-Adapting Deep Graph Learning,ered structural modeling to capture implicit or explicit relationships among anatomical landmarks has not been adequately exploited. In this work, we present a new topology-adapting deep graph learning approach for accurate anatomical facial and medical (e.g., hand, pelvis) landmark detection. The p作者: Exonerate 時間: 2025-3-31 20:52 作者: Breach 時間: 2025-4-1 00:08 作者: 悠然 時間: 2025-4-1 04:54
Contrastive Learning for Unpaired Image-to-Image Translation,e a straightforward method for doing so – maximizing mutual information between the two, using a framework based on contrastive learning. The method encourages two elements (corresponding patches) to map to a similar point in a learned feature space, relative to other elements (other patches) in the作者: Solace 時間: 2025-4-1 08:17 作者: 騙子 時間: 2025-4-1 12:06 作者: STIT 時間: 2025-4-1 15:17
978-3-030-58544-0Springer Nature Switzerland AG 2020
