钱财
发表于 2025-3-30 08:40:15
Studies in Productivity and Efficiencyains a challenge. One particular reason is that events in long and complex videos can consist of multiple heterogeneous sub-activities (in terms of rhythms, activity variants, composition order, etc.) within quite a long period. This fact brings about two main difficulties: excessive/varying length
insincerity
发表于 2025-3-30 12:41:08
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证实
发表于 2025-3-30 20:33:43
May Peters,Richard Stillman,Agapi Somwaruo existing counting models that directly output count values, we divide one-step estimation into a sequence of much easier and more tractable sub-decision problems. Such sequential decision nature corresponds exactly to a physical process in reality—scale weighing. Inspired by scale weighing, we pro
龙虾
发表于 2025-3-30 21:05:56
The Incidence of US Farm Programsctim model by injecting a backdoor pattern into a small proportion of the training data. At test time, the victim model behaves normally on clean test data, yet consistently predicts a specific (likely incorrect) target class whenever the backdoor pattern is present in a test example. While existing
PTCA635
发表于 2025-3-31 04:28:21
Studies in Productivity and Efficiency we address both problems. We introduce a probabilistic meta-learning model for domain generalization, in which classifier parameters shared across domains are modeled as distributions. This enables better handling of prediction uncertainty on unseen domains. To deal with domain shift, we learn doma
根除
发表于 2025-3-31 07:42:47
The Incidence of US Farm Programsghly aligned 3D shapes based on point coordinates, but suffer from performance drops with shape rotations. Some geometric features, e.g., distances and angles of points as inputs of network, are rotation-invariant but lose positional information of points. In this work, we propose a novel deep netwo
Excise
发表于 2025-3-31 10:32:59
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投票
发表于 2025-3-31 17:12:29
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注意
发表于 2025-3-31 19:17:35
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无可争辩
发表于 2025-4-1 00:27:33
The Economic Importance of Insectsd noisy data is modeled by a graph per class and Graph Convolutional Networks (GCN) are used to predict class relevance of noisy examples. For each class, the GCN is treated as a binary classifier, which learns to discriminate clean from noisy examples using a weighted binary cross-entropy loss func