We propose a multi-modal and Temporal Cross-attention Framework (\\modelName) for audio-visual generalised zero-shot learning. Its inputs are temporally aligned audio and visual features that are obtained from pre-trained networks. Encouraging the framework to focus on cross-modal correspondence ...
Spatial Cross-Attention Temporal Self-Attention TSA的motivation 参考网址 来源 ECCV 2022 开源代码 https://github.com/fundamentalvision/BEVFormergithub.com/fundamentalvision/BEVFormer 摘要 3D视觉感知任务,包括基于多摄像头图像的3D检测和地图分割,对于自动驾驶系统至关重要。 在这项工作中,我们提出了一个名...
Cross-attention是什么? Cross-attention(交叉注意力)是注意力机制的一种变体,用于在处理序列数据时,通过将不同部分之间的关联性引入到注意力机制中。通常,注意力机制关注输入序列中不同位置的信息,而交叉注意力则引入了多个序列之间的关联。在交叉注意力中,通常有两个输入序列(例如,源序列和目标序列),每个序列都有...
3D Human Pose Estimation with Spatio-Temporal Criss-cross Attention* Zhenhua Tang, Zhaofan Qiu, Yanbin Hao, Richang Hong, Ting Yao Hefei University of Technology, Anhui, China HiDream.ai Inc University of Science and Technology of China, Anhui, China zhenhuat@foxm...
this work proposes to use non-aggregated temporal information. This is done by adding an attention based method that leverages spatio-temporal interactions between elements in the scene along the clip. The main contribution of this work is the introduction of two cross attention blocks to effectively...
We propose an attention-based multi-component spatiotemporal cross-domain neural network model (att-MCSTCNet) to predict wireless cellular network traffic. The model uses the conv-LSTM or conv-GRU structure to model three temporal properties of wireless cellular network traffic (i.e., recent, dail...
In this regard, this work proposes a model featuring a dual-path cross-attention framework for spatial and temporal patterns, named STDCformer, aiming to enhance the accuracy of ASD identification. STDCformer can preserve both temporal-specific patterns and spatial-specific patterns while explicitly ...
设计了通过在空间上的cross-attention,和时间上的self-attention,设计 learnable BEV queries 去做时域上的结合,然后加到Unified BEV 特征中 做nuScenes和Waymo的detection任务重取得了不错的效果相关工作中介绍了 基于transformer-based 2D perception,和基于相机的 3D Perception问题区:cross...
解码器部分将 search patch feature S 作为其输入。首先将其 reshape 为 S',然后再利用 self-attention 机制进行特征增强: Mask Transformation:基于 search feature 和 编码后的 template feature,作者计算了这两者之间的 cross-attention matrix: 这种cross-attention map 建立了 pixel-to-pixel 的一致性。 在视觉...
视频时域解码器定义为一组和空间域编码器结构相同的self-attention layer,对每个单层 关于时空变换器STTran训练的损失函数,它包括两个:基于可信度的multi-label margin loss和标准的cross entropy loss 一般有两种典型的策略来生成具有推理关系分布的场景图: