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...
为了解决这个问题,提出了cascaded group attention模块,把特征分成不同的部分送入attention heads,这样不仅节省了计算成本,还提高了attention的多样性。EfficientViT-M5比MobileNetV3-Large精度高1.9%,同时在V100显卡和Intel Xeon CPU上获得了更高的throughput(分别提高了40.4%和45.2%)。和MobileViT-XXS相比,EfficientViT-...
returnm classPatchMerging(torch.nn.Module):def__init__(self,dim,out_dim,input_resolution):# 在初始化函数中,传入三个参数:输入的维度(dim)、输出的维度(out_dim)和输入的分辨率(input_resolution)。然后使用super().__init__()来初始化基类。super().__init__()hid_dim=int(dim*4)# 定义一个名...
Moreover, we discover 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 ...
(a) Architecture of EfficientViT; (b) Sandwich Layout block; (c) Cascaded Group Attention. 3.1. EfficientViT Building Blocks We propose a new efficient building block for vision transformer, as shown in Fig. 6 (b). It is composed of a memory-efficient s...
Group Velocity Mismatch Effects in Ultrafast Optical Modulators Based on Cascaded Second Order NonlinearitiesThe intensity dependent phase and amplitude modulation arising from cascaded second order optical nonlinear processes in frequency doubling crystals are getting growing attention1, 2, 3 for all-optical...
While DML changes flow effortlessly through your cascaded databases,DDLreplication demands special attention. The key lies in the Extract configuration of your central Datawarehouse database. By default, Extract is programmed to exclude all DDL operations executed by Replicat. This means DDL changes ori...
Surgical resection is one of the most effective and common methods to treat solid malignant tumors1,2,3. Despite significant advancements in surgical techniques, the issue of postoperative tumor recurrence and metastasis remains a pressing clinical challenge that requires sustained attention. This problem...
An attention score α∈ [0, 1] is learned for interpreting the importance of the facial region for the object o: α = σ(FC×2([F, O])), (8) where σ is the sigmoid function, and FC×2 stands for two stacked FC layers. • Face-agnostic Attention. The face-aware enhancement ...
EfficientViT: Memory Efficient Vision Transformer with Cascaded Group Attention 代码逐行注释17 赞同 · 0 评论文章 Abstract 这篇论文介绍了一个新的模型家族,叫做EfficientViT,目的是提升Vision Transformers的计算速度和内存效率。通过使用一个新设计的“三明治”构建块和引入级联分组注意力(Cascaded Group Attention)...