作者: CHOIR 時間: 2025-3-21 21:54
https://doi.org/10.1007/978-3-031-19772-7action recognition; artificial intelligence; computer networks; computer vision; data security; Human-Com作者: 紅潤 時間: 2025-3-22 02:31 作者: FLAG 時間: 2025-3-22 08:24 作者: Bph773 時間: 2025-3-22 09:12 作者: BALK 時間: 2025-3-22 13:12 作者: BALK 時間: 2025-3-22 20:13
Second Language Learning and Teachingxed and the use of non-self positive is yet to be explored. In this paper, a Contrastive Positive Mining (CPM) framework is proposed for unsupervised skeleton 3D action representation learning. The CPM identifies non-self positives in a contextual queue to boost learning. Specifically, the siamese e作者: 歡樂中國 時間: 2025-3-22 21:35
Second Language Learning and Teachingvision models have been developed to predict the fixations made by people as they search for target objects. But what about when the target is not in the image? Equally important is to know how people search when they cannot find a target, and when they would stop searching. In this paper, we propos作者: urethritis 時間: 2025-3-23 02:25 作者: 胖人手藝好 時間: 2025-3-23 09:28
https://doi.org/10.1007/978-3-319-31658-1etailed scene understanding task involving a sequential process of human/object detection and interaction recognition. Iwin Transformer is a hierarchical Transformer which progressively performs token representation learning and token agglomeration within .rregular .dows. The irregular windows, achi作者: 頭盔 時間: 2025-3-23 11:39
The Ecotoxicology of Aquatic Macrophytesme a thriving direction. ZSAR requires models to recognize actions that never appear in training set through bridging visual features and semantic representations. However, due to the complexity of actions, it remains challenging to transfer knowledge learned from source to target action domains. Pr作者: Cosmopolitan 時間: 2025-3-23 14:51 作者: Vertical 時間: 2025-3-23 22:01 作者: Hyperalgesia 時間: 2025-3-24 00:53
https://doi.org/10.1007/3-540-28527-Xe-art deep-learning video understanding architectures are biased toward static information available in single frames. Presently, a methodology and corresponding dataset to isolate the effects of dynamic information in video are missing. Their absence makes it difficult to understand how well contem作者: Analogy 時間: 2025-3-24 02:49 作者: Facet-Joints 時間: 2025-3-24 08:53 作者: 流動才波動 時間: 2025-3-24 13:59 作者: ENNUI 時間: 2025-3-24 17:06
Subtropics with year-round rain majority of computation to a task-relevant subset of frames or the most valuable image regions of each frame. However, in most existing works, either type of redundancy is typically modeled with another absent. This paper explores the unified formulation of spatial-temporal dynamic computation on t作者: Mortal 時間: 2025-3-24 23:01
Subtropics with year-round raintion (PAR), which aims to simultaneously achieve the recognition of individual actions, social group activities, and global activities. This is a challenging yet practical problem in real-world applications. To track this problem, we develop a novel hierarchical graph neural network to progressively作者: theta-waves 時間: 2025-3-25 02:40
Subtropics with year-round rains fall on the mis-classifications among very similar actions (such as high kick .. side kick) that need a capturing of fine-grained discriminative details. To solve this problem, we propose synopsis-to-detail networks for video action recognition. Firstly, a synopsis network is introduced to predict作者: Herbivorous 時間: 2025-3-25 05:19
Subtropics with year-round rainworks rely on assigning hard labels, and performance rapidly collapses under subtle violations of the annotation assumptions. We propose a novel Expectation-Maximization (EM) based approach that leverages the label uncertainty of unlabelled frames and is robust enough to accommodate possible annotat作者: ligature 時間: 2025-3-25 10:19
https://doi.org/10.1007/3-540-28527-X project videos into a metric space and classify videos via nearest neighboring. They mainly measure video similarities using global or temporal alignment alone, while an optimum matching should be multi-level. However, the complexity of learning coarse-to-fine matching quickly rises as we focus on 作者: 共同給與 時間: 2025-3-25 13:32 作者: 死亡 時間: 2025-3-25 17:17
0302-9743 puter Vision, ECCV 2022, held in Tel Aviv, Israel, during October 23–27, 2022..?.The 1645 papers presented in these proceedings were carefully reviewed and selected from a total of 5804 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforc作者: 缺陷 時間: 2025-3-25 23:33 作者: Nostalgia 時間: 2025-3-26 00:26 作者: 乞丐 時間: 2025-3-26 04:53 作者: fertilizer 時間: 2025-3-26 11:06 作者: Palpitation 時間: 2025-3-26 14:22 作者: 一美元 時間: 2025-3-26 18:33 作者: 推測 時間: 2025-3-26 23:52
Panoramic Human Activity Recognition,he proposed method and other related methods. Experimental results verify the rationality of the proposed PAR problem, the effectiveness of our method and the usefulness of the benchmark. We have released the source code and benchmark to the public for promoting the study on this problem.作者: IRATE 時間: 2025-3-27 01:37
Conference proceedings 2022on, ECCV 2022, held in Tel Aviv, Israel, during October 23–27, 2022..?.The 1645 papers presented in these proceedings were carefully reviewed and selected from a total of 5804 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement lear作者: Sinus-Rhythm 時間: 2025-3-27 06:49
0302-9743 ruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation..978-3-031-19771-0978-3-031-19772-7Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 發(fā)起 時間: 2025-3-27 09:44 作者: NIL 時間: 2025-3-27 13:58
Subtropics with year-round rainulation’s robustness, we introduce the new challenging annotation setup of SkipTag Supervision. This setup relaxes constraints and requires annotations of any fixed number of random frames in a video, making it more flexible than Timestamp Supervision while remaining competitive.作者: 感情 時間: 2025-3-27 20:40 作者: 使無效 時間: 2025-3-28 01:21
,A Generalized and Robust Framework for?Timestamp Supervision in?Temporal Action Segmentation,ulation’s robustness, we introduce the new challenging annotation setup of SkipTag Supervision. This setup relaxes constraints and requires annotations of any fixed number of random frames in a video, making it more flexible than Timestamp Supervision while remaining competitive.作者: VOK 時間: 2025-3-28 03:27 作者: 起皺紋 時間: 2025-3-28 08:15 作者: hedonic 時間: 2025-3-28 14:30 作者: 使成整體 時間: 2025-3-28 16:29 作者: 針葉 時間: 2025-3-28 19:40
The Ecotoxicology of Aquatic Macrophytest names may still share the same atomic action components. It enables humans to quickly understand an unseen action given bunch of atomic actions learned from seen actions. Inspired by this, we propose Jigsaw Network (JigsawNet) which recognizes complex actions through unsupervisedly decomposing the作者: 擴大 時間: 2025-3-29 00:35
https://doi.org/10.1007/3-540-28527-X We construct body-part saliency maps based on self-attention to mine cross-person informative cues and learn the holistic relationships between . the body-parts. We evaluate the proposed method on widely-used benchmarks HICO-DET and V-COCO. With our new perspective, the holistic global-local body-p作者: 自作多情 時間: 2025-3-29 04:56
Subtropics with year-round raino label generation and feature clustering. Furthermore, to leverage the complementarity of domain-shared features and target-specific features, we propose a novel collaborative clustering strategy to enforce pair-wise relationship consistency between the two branches. We conduct extensive experiment作者: escalate 時間: 2025-3-29 11:14
https://doi.org/10.1007/3-540-28527-X its related RGB video. Our results show a notable decrease in performance for all architectures on AFD compared to RGB. We also conducted a complimentary study with humans that shows their recognition accuracy on AFD and RGB is very similar and much better than the evaluated architectures on AFD. O作者: 無所不知 時間: 2025-3-29 12:04 作者: 赤字 時間: 2025-3-29 15:52
Subtropics with year-round rainen, the snippet-level uncertainty is further deduced for progressive learning, which gradually focuses on the entire action instances in an “easy-to-hard” manner. Extensive experiments show that DELU achieves state-of-the-art performance on THUMOS14 and ActivityNet1.2 benchmarks. Our code is availab作者: 樹木中 時間: 2025-3-29 23:18
https://doi.org/10.1007/3-540-28527-Xranges. The proposed model successfully learns local dynamics of the joints and captures global context from the motion sequences. Our model outperforms state-of-the-art models by notable margins in the representative benchmarks. Codes are available at ..作者: enhance 時間: 2025-3-30 03:38
Subtropics with year-round rainime, the number of the cubes corresponding to each video is dynamically configured, ., video cubes are processed sequentially until a sufficiently reliable prediction is produced. Notably, AdaFocusV3 can be effectively trained by approximating the non-differentiable cropping operation with the inter作者: 合同 時間: 2025-3-30 06:26 作者: implore 時間: 2025-3-30 11:59
https://doi.org/10.1007/3-540-28527-Xtilizes cycle consistency as weak supervision to align discriminative temporal clips or spatial patches. Our model achieves state-of-the-art performance on four benchmarks especially under the most challenging 1-shot recognition setting.作者: 流眼淚 時間: 2025-3-30 14:04 作者: HAUNT 時間: 2025-3-30 19:26 作者: 吵鬧 時間: 2025-3-31 00:05
Target-Absent Human Attention, that produces an in-network feature pyramid, all with minimal computational overhead. Our method integrates FFMs as the state representation in inverse reinforcement learning. Experimentally, we improve the state of the art in predicting human target-absent search behavior on the COCO-Search18 data作者: 畢業(yè)典禮 時間: 2025-3-31 02:09
,Iwin: Human-Object Interaction Detection via?Transformer with?Irregular Windows,I detection benchmark datasets, HICO-DET and V-COCO. Results show our method outperforms existing Transformers-based methods by large margins (3.7 mAP gain on HICO-DET and 2.0 mAP gain on V-COCO) with fewer training epochs (.).作者: 松軟 時間: 2025-3-31 06:08 作者: glomeruli 時間: 2025-3-31 09:35 作者: Projection 時間: 2025-3-31 13:28
,Collaborating Domain-Shared and?Target-Specific Feature Clustering for?Cross-domain 3D Action Recogo label generation and feature clustering. Furthermore, to leverage the complementarity of domain-shared features and target-specific features, we propose a novel collaborative clustering strategy to enforce pair-wise relationship consistency between the two branches. We conduct extensive experiment作者: 發(fā)現(xiàn) 時間: 2025-3-31 18:59
Is Appearance Free Action Recognition Possible?, its related RGB video. Our results show a notable decrease in performance for all architectures on AFD compared to RGB. We also conducted a complimentary study with humans that shows their recognition accuracy on AFD and RGB is very similar and much better than the evaluated architectures on AFD. O作者: orient 時間: 2025-3-31 21:43 作者: 厚顏無恥 時間: 2025-4-1 03:11
,Dual-Evidential Learning for?Weakly-supervised Temporal Action Localization,en, the snippet-level uncertainty is further deduced for progressive learning, which gradually focuses on the entire action instances in an “easy-to-hard” manner. Extensive experiments show that DELU achieves state-of-the-art performance on THUMOS14 and ActivityNet1.2 benchmarks. Our code is availab作者: glans-penis 時間: 2025-4-1 08:38 作者: Infect 時間: 2025-4-1 10:42
