Spatio-temporal attention networksSpatial transformer networkFeature fusionHuman action recognitionIn the study of human action recognition, two-stream networks have made excellent progress recently. However, there remain challenges in distinguishing similar human actions in videos. This paper proposes a ...
self-attention没有捕获target ad跟辅助广告之间的关系,也就是每个辅助广告序列内部算出来的权重是跟target ad无关的。还有个问题就是每个辅助广告序列权重是内部做归一化的,导致就算一个辅助广告序列中广告都跟target ad不相关,但是因为做了归一化,权重还是会很大的。 接下来论文提出了Interactive Attention,其实就是...
Meanwhile, the spatial context and temporal information were not fully utilized and processed in some networks. In this paper, a novel three-stream network spatiotemporal attention enhanced features fusion network for action recognition is proposed. Firstly, features fusion stream which includes multi-...
We build our networks based on the recurrent neural networks with long short-term memory units. The learned model is capable of selectively focusing on discriminative joints of skeletons within each input frame and paying different levels of attention to the outputs of different frames. To ensure ...
一.概览: 大体上仍然是emb+mlp之类的架构,在一些细节上有所不同. 1.embed部分 常规的处理下,multHotFea用pooling扔进去 2.主体架构部分 类似DIN的思想,与target做attention,attention使用的是mlp作为小分类器 二.个人认为比较有趣的地方 时间:history click seq&unclick seq-->pctr 空间:item pos:pos1->click...
To learn spatiotemporal representations and anomaly predictions from geophysical data, we propose STANet, a spatiotemporal neural network with a trainable attention mechanism, and apply it to El Niño predictions for long-lead forecasts. The STANet mak
This paper proposes a novel framework with spatiotemporal attention networks (STAN) for wind power forecasting. This model captures spatial correlations among wind farms and temporal dependencies of wind power time series. First of all, we employ a multi-head self-attention mechanism to extract ...
encoding for providing positional correlation to the traffic sequence; and (c) a self-attention module for capturing long-term dependence. These components work together to enhance long-term prediction performance in complex networks effectively. Extensive experiments on three publicly available datasets ...
[骨架动作识别]STA-LSTM: Spatio-Temporal Attention Model for Human Action Recognition from Skeleton Data,程序员大本营,技术文章内容聚合第一站。
attention adjustment, urban computing, airquality, traff i c f l ow, bike demandI. I NTRODUCTIONWith the technologies of sensor networks and the Internetof Things, sensors are widely and geographically deployed inmodern urban areas. Sensors established for different purposescollectively monitor the ...