首先与各位分享一下目前基于Transformer在时序上应用的一些文章 Papers with Code - Adversarial Sparse Transformer for Time Series Forecastingpaperswithcode.com/paper/adversarial-sparse-transformer-for-time Papers with Code - Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting...
(NMT), in this paper, we introduce and adapt the multi-head attention mechanism to replace the RNN structures and also the original attention mechanism in Tacotron2. With the help of multi-head self-attention, the hidden states in the encoder and decoder are constructed in parallel, which ...
https://paperswithcode.com/sota/semantic-segmentation-on-ade20k-val 最近Transformer的文章眼花缭乱,但是精度和速度相较于CNN而言还是差点意思,直到Swin Transformer的出现,让人感觉到了一丝丝激动,Swin Transformer可能是CNN的完美替代方案。 作者分析表明,Transformer从NLP迁移到CV上没有大放异彩主要有两点原因: ...
而 Transformer 诞生伊始就完全舍弃了 RNN，在 LSTM 占优势的 NLP 领域逐渐站稳脚跟。现在，许多研究又将它应用于时序预测、音乐生成、图像分类等跨界任务中。在 Papers with Code 最近发布的 Transformer 应用十大新任务中，过去都有着 LSTM 的活跃身影。Transformer 是新的 LSTM 吗？从模型应用领域的多样性来看，这...
而Transformer 诞生伊始就完全舍弃了 RNN,在 LSTM 占优势的 NLP 领域逐渐站稳脚跟。现在,许多研究又将它应用于时序预测、音乐生成、图像分类等跨界任务中。在 Papers with Code 最近发布的 Transformer 应用十大新任务中,过去都有着 LSTM 的活跃身影。 Transformer 是新的 LSTM 吗?从模型应用领域的多样性来看,这似乎...
https://github.com/amusi/CVPR2021-Papers-with-Code CVPR 2021 视觉Transformer论文(43篇) Amusi 一共搜集了43篇Vision Transformer论文,应用涉及:图像分类、目标检测、实例分割、语义分割、行为识别、自动驾驶、关键点匹配、目标跟踪、NAS、low-level视觉、HoI、可解释性、布局生成、检索、文本检测等方向。PS:待"染...
来自paperswithcode 2023-06-02 12:41:02 0 0 102作者邀请 论文作者还没有讲解视频 邀请直播讲解Sequential models that encode user activity for next action prediction have become a popular design choice for building web-scale personalized recommendation systems. Traditional methods of sequential ...
来自paperswithcode2024-01-08 09:28:32 0 0 1661 作者邀请 论文作者还没有讲解视频 邀请直播讲解 Video moment retrieval (MR) and highlight detection (HD) based on natural language queries are two highly related tasks, which aim to obtain relevant moments within videos and highlight scores of eac...
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Although Transformer has achieved great successes on many NLP tasks, its heavy structure with fully-connected attention connections leads to dependencies on large training data. In this paper, we present Star-T