作者: A保存的 時間: 2025-3-21 20:53
Exposing Face Forgery Clues via?Retinex-Based Image Enhancementthe RGB feature extractor to concentrate more on forgery traces from an MSR perspective. The feature re-weighted interaction module implicitly learns the correlation between the two complementary modalities to promote feature learning for each other. Comprehensive experiments on several benchmarks s作者: optional 時間: 2025-3-22 03:00 作者: 引水渠 時間: 2025-3-22 06:40 作者: 過濾 時間: 2025-3-22 10:37 作者: etiquette 時間: 2025-3-22 16:41 作者: etiquette 時間: 2025-3-22 18:06
Occluded Facial Expression Recognition Using Self-supervised Learningownstream task. The experimental results on several databases containing both synthesized and realistic occluded facial images demonstrate the superiority of the proposed method over state-of-the-art methods.作者: AVANT 時間: 2025-3-22 21:12
Focal and?Global Spatial-Temporal Transformer for?Skeleton-Based Action Recognitionteractions between the focal joints and body parts are incorporated to enhance the spatial dependencies via mutual cross-attention. (2) FG-TFormer: focal and global temporal transformer. Dilated temporal convolution is integrated into the global self-attention mechanism to explicitly capture the loc作者: Palpable 時間: 2025-3-23 04:30
Spatial-Temporal Adaptive Graph Convolutional Network for?Skeleton-Based Action Recognitioning the direct long-range temporal dependencies adaptively. On three large-scale skeleton action recognition datasets: NTU RGB+D 60, NTU RGB+D 120, and Kinetics Skeleton, the STA-GCN outperforms the existing state-of-the-art methods. The code is available at ..作者: nocturnal 時間: 2025-3-23 08:34 作者: 鎮(zhèn)痛劑 時間: 2025-3-23 13:40
SCOAD: Single-Frame Click Supervision for?Online Action Detection mines pseudo-action instances under the supervision of click labels. Meanwhile, we generate video similarity instances offline by the similarity between video frames and use it to perform finer granularity filtering of error instances generated by AIM. OAD is trained jointly with AIM for online act作者: 含水層 時間: 2025-3-23 14:47
Neural Puppeteer: Keypoint-Based Neural Rendering of?Dynamic Shapesrevious work, we do not perform reconstruction in the 3D domain, but project the 3D features into 2D cameras and perform reconstruction of 2D RGB-D images from these projected features, which is significantly faster than volumetric rendering. Our synthetic dataset will be publicly available, to furt作者: emulsify 時間: 2025-3-23 18:33 作者: 罐里有戒指 時間: 2025-3-24 01:51
Cody Freitag,Ilan Komargodski,Rafael Passthe RGB feature extractor to concentrate more on forgery traces from an MSR perspective. The feature re-weighted interaction module implicitly learns the correlation between the two complementary modalities to promote feature learning for each other. Comprehensive experiments on several benchmarks s作者: cathartic 時間: 2025-3-24 04:11
Jean-Sébastien Coron,Agnese Gini is proved to be a special case of the proposed loss. We analyze and explain the proposed GB-CosFace geometrically. Comprehensive experiments on multiple face recognition benchmarks indicate that the proposed GB-CosFace outperforms current state-of-the-art face recognition losses in mainstream face 作者: Crater 時間: 2025-3-24 08:47 作者: 仔細檢查 時間: 2025-3-24 14:33 作者: 使隔離 時間: 2025-3-24 18:18
https://doi.org/10.1007/978-3-030-56877-1ompared approaches. With extensive subjective, quantitative, and qualitative evaluations, the proposed approach consistently achieves better performance in terms of facial attribute heredity and image generation fidelity than other compared state-of-the-art methods. This demonstrates the effectivene作者: Coeval 時間: 2025-3-24 19:23 作者: Moderate 時間: 2025-3-24 23:50 作者: 符合規(guī)定 時間: 2025-3-25 04:24 作者: Inexorable 時間: 2025-3-25 10:20
Fukang Liu,Takanori Isobe,Willi Meier conditions which are consistent with human silhouette and 2D joint points in the second stage. Selecting and clustering are utilized to eliminate abnormal and redundant human meshes. The number of hypothesis is not unified for each single image, and it is dependent on 2D pose ambiguity. Unlike the 作者: Commonwealth 時間: 2025-3-25 12:48
Christian Badertscher,Yun Lu,Vassilis Zikas mines pseudo-action instances under the supervision of click labels. Meanwhile, we generate video similarity instances offline by the similarity between video frames and use it to perform finer granularity filtering of error instances generated by AIM. OAD is trained jointly with AIM for online act作者: abduction 時間: 2025-3-25 19:44 作者: 懲罰 時間: 2025-3-25 20:58
0302-9743 art VII: generative models for computer vision; segmentation and grouping; motion and tracking; document image analysis; big data, large scale methods. .978-3-031-26315-6978-3-031-26316-3Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 撫慰 時間: 2025-3-26 03:19
0302-9743 China, December 2022...The total of 277 contributions included in the proceedings set was carefully reviewed and selected from 836 submissions during two rounds of reviewing and improvement. The papers focus on the following topics:..Part I: 3D computer vision; optimization methods;.Part II: applic作者: 增強 時間: 2025-3-26 06:41
Advances in Cryptology – CRYPTO 2020ensity estimation based Visual Counter. We evaluate our proposed approach on FSC-147 dataset, and show that it achieves superior performance compared to the existing approaches. Our code and models are available at: ..作者: boisterous 時間: 2025-3-26 11:24
Fukang Liu,Takanori Isobe,Willi Meierontent of RT-Net is transferred based on AdaIN controlled by heterogeneous identity embedding. Comprehensive experimental results show that the disentanglement of rendering and topology is beneficial to the HAS task, and our HASNet has comparable performance compared with other state-of-the-art methods.作者: anachronistic 時間: 2025-3-26 16:35
Exemplar Free Class Agnostic Countingensity estimation based Visual Counter. We evaluate our proposed approach on FSC-147 dataset, and show that it achieves superior performance compared to the existing approaches. Our code and models are available at: ..作者: Peculate 時間: 2025-3-26 17:38 作者: 體貼 時間: 2025-3-27 00:41
Conference proceedings 2023ing, and shape representation; datasets and performance analysis;.Part VI: biomedical image analysis; deep learning for computer vision; ..Part VII: generative models for computer vision; segmentation and grouping; motion and tracking; document image analysis; big data, large scale methods. .作者: 鬼魂 時間: 2025-3-27 04:39
Conference proceedings 2023cember 2022...The total of 277 contributions included in the proceedings set was carefully reviewed and selected from 836 submissions during two rounds of reviewing and improvement. The papers focus on the following topics:..Part I: 3D computer vision; optimization methods;.Part II: applications of 作者: 松緊帶 時間: 2025-3-27 07:25
A Rational Protocol Treatment of 51% Attacksg of a 2D to 3D human pose lifter neural network. Our results show that we can achieve 3D pose estimation performance comparable to methods using real data from specialized datasets but in a zero-shot setup, showing the generalization potential of our framework.作者: 鉗子 時間: 2025-3-27 11:12 作者: 落葉劑 時間: 2025-3-27 17:00
Decanus to?Legatus: Synthetic Training for?2D-3D Human Pose Liftingg of a 2D to 3D human pose lifter neural network. Our results show that we can achieve 3D pose estimation performance comparable to methods using real data from specialized datasets but in a zero-shot setup, showing the generalization potential of our framework.作者: Anonymous 時間: 2025-3-27 20:41 作者: jaunty 時間: 2025-3-27 23:28 作者: entreat 時間: 2025-3-28 02:21 作者: preservative 時間: 2025-3-28 07:47 作者: Fierce 時間: 2025-3-28 12:02
Learning Video-Independent Eye Contact Segmentation from?In-the-Wild Videosact targets vary across different videos, learning a generic video-independent eye contact detector is still a challenging task. In this work, we address the task of one-way eye contact detection for videos in the wild. Our goal is to build a unified model that can identify when a person is looking 作者: 疾馳 時間: 2025-3-28 14:41 作者: craven 時間: 2025-3-28 20:14 作者: 繼而發(fā)生 時間: 2025-3-29 02:26 作者: 刺耳 時間: 2025-3-29 05:21
Occluded Facial Expression Recognition Using Self-supervised Learning consuming and expensive to collect a large number of facial images with various occlusions and expression annotations. To address this problem, we propose an occluded facial expression recognition method through self-supervised learning, which leverages the profusion of available unlabeled facial i作者: 護航艦 時間: 2025-3-29 09:36
Heterogeneous Avatar Synthesis Based on?Disentanglement of?Topology and?Renderingavatar synthesis (HAS) task considering topology and rendering transfer. HAS transfers the topology as well as rendering styles of the referenced face to the source face, to produce high-fidelity heterogeneous avatars. Specifically, first, we utilize a Rendering Transfer Network (RT-Net) to render t作者: 火海 時間: 2025-3-29 12:41 作者: obsolete 時間: 2025-3-29 18:49
Spatial-Temporal Adaptive Graph Convolutional Network for?Skeleton-Based Action Recognition graphs to extract discriminative features. However, due to the fixed topology shared among different poses and the lack of direct long-range temporal dependencies, it is not trivial to learn the robust spatial-temporal feature. Therefore, we present a spatial-temporal adaptive graph convolutional n作者: 商業(yè)上 時間: 2025-3-29 23:18 作者: addict 時間: 2025-3-30 02:53 作者: Arroyo 時間: 2025-3-30 07:37 作者: 有害處 時間: 2025-3-30 10:17 作者: 含鐵 時間: 2025-3-30 16:03
Social Aware Multi-modal Pedestrian Crossing Behavior Predictionnment. Previous methods ignored the inherent uncertainty of pedestrian future actions and the temporal correlations of spatial interactions. To solve the aforementioned problems, we propose a novel social aware multi-modal pedestrian crossing behavior prediction network. In this research field, our 作者: 真繁榮 時間: 2025-3-30 20:04 作者: 寄生蟲 時間: 2025-3-30 22:49
Christopher Patton,Thomas ShrimptonUnsupervised self-rehabilitation exercises and physical training can cause serious injuries if performed incorrectly. We introduce a learning-based framework that identifies the mistakes made by a user and proposes corrective measures for easier and safer individual training.作者: 半身雕像 時間: 2025-3-31 04:23 作者: 柏樹 時間: 2025-3-31 06:09
https://doi.org/10.1007/978-3-031-26316-3computer vision; image processing; artificial intelligence; machine learning; image analysis; pattern rec作者: Hemiparesis 時間: 2025-3-31 12:35
978-3-031-26315-6The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: Lacunar-Stroke 時間: 2025-3-31 15:32
Computer Vision – ACCV 2022978-3-031-26316-3Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 牲畜欄 時間: 2025-3-31 18:45 作者: 廢止 時間: 2025-3-31 21:40
