moribund
发表于 2025-3-25 06:20:59
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窝转脊椎动物
发表于 2025-3-25 08:47:44
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龙虾
发表于 2025-3-25 12:41:25
Visual Tracking via Supervised Similarity Matching main engine for target detection. In addition, our method applies a Support Vector Machine (SVM) based supervised classifier cooperating with the unsupervised detector. Both the proposed tracker and several selected trackers are tested on some well accepted challenging videos; and the experimental
动作谜
发表于 2025-3-25 19:34:35
Robust Online Visual Tracking with a Single Convolutional Neural Networkhin different time periods. Finally, we propose to update the CNN model in a “lazy” style to speed-up the training stage, where the network is updated only when a significant appearance change occurs on the object, without sacrificing tracking accuracy. The CNN tracker outperforms all compared state
填料
发表于 2025-3-25 23:23:48
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mastoid-bone
发表于 2025-3-26 00:40:13
Enhanced Sequence Matching for Action Recognition from 3D Skeletal Data and MSRC-12 gesture dataset and achieves comparable performance to the state-of-the-art on MSR action 3D dataset. Moreover, experimental results show that our method is very intuitive and robust to noise and temporal variation.
cinder
发表于 2025-3-26 07:46:40
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整洁漂亮
发表于 2025-3-26 09:06:16
Fast Inference of Contaminated Data for Real Time Object Tracking updating observation model, we adopt on an online robust PCA during the update of observation model. Our qualitative and quantitative evaluations on challenging dataset demonstrate that the proposed scheme is competitive to several sophisticated state of the art methods, and it is much faster.
无孔
发表于 2025-3-26 13:22:50
The Czech Language in the Digital Ageon of networks with different depth is used to improve the training efficiency and all the convolutional operations and parameters updating are based on the efficient GPU implementation. The experimental results applied to some widely used benchmark outperform the state of the art methods.
Oscillate
发表于 2025-3-26 18:27:43
The Czech and Slovak Experienceo background change which typically occurs in real-world setting. We conduct extensive experiments on several publicly available datasets for anomaly detection, and the results show that our approach can outperform state-of-the-art approaches, which verifies the effectiveness of our approach.