难听的声音 发表于 2025-3-26 20:56:26
http://reply.papertrans.cn/67/6637/663640/663640_31.pngstroke 发表于 2025-3-27 03:22:51
http://reply.papertrans.cn/67/6637/663640/663640_32.pngImmobilize 发表于 2025-3-27 08:15:13
More Than One-Hot: Chinese Macro Discourse Relation Recognition on Joint Relation Embeddingy considered the loss of prediction and ground truth before back-propagation in the form of a one-hot vector, which cannot reflect the relation coherence. To remedy this deficiency, we propose a macro discourse relation recognition model based on the Joint Relation Embedding (JRE). This model contaiallude 发表于 2025-3-27 09:41:23
Abstracting Inter-instance Relations and Inter-label Correlation Simultaneously for Sparse Multi-lababel learning (GNN-SML) is proposed. More specifically, latent representation for sparse multi-label instance sets is constructed, both involving inter-instance and inter-label relations. The attacking problem is that instance features or label sets are too sparse to be extracted effectively hidden盘旋 发表于 2025-3-27 14:08:34
http://reply.papertrans.cn/67/6637/663640/663640_35.pngFECT 发表于 2025-3-27 21:39:36
Multi-scale Feature Fusion Network with Positional Normalization for Single Image Dehazing MSFFP-Net which does not rely on the physical atmosphere scattering model, and the backbone of the proposed network is a multi-scale network (MSNet). The MSNet uses a up-sampling and down-sampling block to connect different scales, so the information in the net can be exchanged efficiently. The bas得罪人 发表于 2025-3-27 22:04:26
http://reply.papertrans.cn/67/6637/663640/663640_37.pnggrenade 发表于 2025-3-28 03:39:14
Dependency Learning Graph Neural Network for Multivariate Forecastingltivariate forecasting. However, most existing models fail to learn the dependencies between different time series. Lately, studies have shown that implementations of Graph Neural Networks in the field of Natural Language, Computer Vision, and Time Series have achieved exceptional performance. In thHomocystinuria 发表于 2025-3-28 06:25:07
Research on Flame Detection Based on Anchor-Free Algorithm FCOStting of anchor hyper-parameters and is insensitive to the change of object shape. Therefore, the improved anchor-free algorithm FCOS is introduced. Firstly, the Center-ness branch is replaced by the IoU prediction branch to make the bounding box location more accurate; then the random copy-pastingBIAS 发表于 2025-3-28 11:01:48
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