group attention controlfield trialhuman-robot interactionA humanoid robot can support people in a real environment by interacting with them through human-like body movements, such as shaking hands, greeting, and pointing. In real environments, a robot often interacts with groups of people to provide...
MQA的原理很简单，它将原生Transformer每一层多头注意力的Key线性映射矩阵、Value线性映射矩阵改为该层下所有头共享，也就是说K、V矩阵每层只有一个。举例来说，以ChatGLM2-6B为例，一共28层，32个注意力头，输入维度从4096经过Q、K、V矩阵映射维度为128，若采用原生多头注意力机制，则Q、K、V矩阵各有28×32...
简介:YOLO目标检测专栏探讨了Transformer在视觉任务中的效能与计算成本问题,提出EfficientViT,一种兼顾速度和准确性的模型。EfficientViT通过创新的Cascaded Group Attention(CGA)模块减少冗余,提高多样性,节省计算资源。在保持高精度的同时,与MobileNetV3-Large相比,EfficientViT在速度上有显著提升。论文和代码已公开。CGA通过...
多查询注意力(MultiQuery Attention,MQA)和分组查询注意力(GroupQueryAttention,GQA)是在近年来对Transformer模型的改进中引起关注的新技术。MQA最早于2019年的论文《FastTransformer Decoding: One Write-Head is All YouNeed》中提出,旨在解决Transformer增量推理阶段效率低下的问题。虽然当时并没有引起广泛关注,但随着近...
多查询注意力(Multi Query Attention,MQA)和分组查询注意力(Group Query Attention,GQA)是在近年来对Transformer模型的改进中引起关注的新技术。MQA最早于2019年的论文《Fast Transformer Decoding: One Write-Head is All You Need》中提出,旨在解决Transformer增量推理阶段效率低下的问题。虽然当时并没有引起广泛关注,...
Video Super-resolution with Temporal Group Attention Takashi Isobe1,2†, Songjiang Li2, Xu Jia2∗, Shanxin Yuan2, Gregory Slabaugh2, Chunjing Xu2, Ya-Li Li1, Shengjin Wang1∗, Qi Tian2 1Department of Electronic Engineering, Tsinghua University 2Noah's Ark Lab, Huawei Techn...
Group attention dynamically clusters the objects based on their similarity into a small number of groups and approximately computes the attention at the coarse group granularity. It thus significantly reduces the time and space complexity, yet provides a theoretical guarantee on the quality of the ...
Divide and Conquer:Question-Guided Spatio-Temporal Contextual Attention for Video Question Answering 动机 理解问题和寻找答案的线索是视频问答的关键。 VQA任务主要分为图像问答(Image QA)和视频问答(Video QA)两种,针对不同视觉材料的自然语言问题进行回答。通常,理解问题并在给定的视觉材料中找到问题答案的线索是...
that the attention maps share high similarities across heads, leading to computational redundancy. To address this, we present a cascaded group attention module feeding attention heads with different splits of the full feature, which not only saves computation cost but also...
Then, we propose a new cascaded group attention (CGA) module to improve computation ef- ficiency. The core idea is to enhance the diversity of the fea- tures fed into the attention heads. In contrast to prior self- attention using the same feature for al...