这篇文章提出了一种名为"Scale-Aware Modulation Transformer"(SMT)的新型Transformer结构,它充分结合CNN和Transformer的优势,减轻了SA的运算负担,同时又解决了浅层的CNN局部特征捕捉能力的痛点。 在文章中,作者设计了一种创新的轻量级尺度感知调制单元Scale-Aware Modulation(SAM),其中包含了两个主要模块: 多头混合卷积Mu...
## Scale Modulation Module (SAM)classScaleAwareModule(nn.Module):def__init__(self,in_dim,out_dim,num_heads,expand_ratio,shortcut=False,act_type='silu',norm_type='BN'):super().__init__()# --- Basic parameters ---self.in_dim=in_dimself.out_dim=out_dimself.num_heads=num_headsse...
However, the 3px size makes the points almost impossible to see when you zoom back to regional scales or beyond. FigureD:At this scale, 3px points are difficult to see. Scale-dependent properties Because icon sizes, line widths, and densities don't display well at all scales, the ArcGIS JS...
To address this issue, in this paper, we propose a new scale-aware hierarchical attention network (SaHAN) for scene text recognition. Inspired by feature pyramid network, we exploit the inherent pyramidal structure of a deep convolutional network to retain multi-scale features for flexible receptive...
We propose Scale-aware Face Detector (SAFD) to handle scale explicitly using CNN, and achieve better performance with less computation cost. Prior to detection, an efficient CNN predicts the scale distribution histogram of the faces. Then the scale histogram guides the zoom-in and zoom-out of ...
Towards the third problem, an attention module is introduced to complement the scale-aware module, which can capture long-range dependencies in the generated scale-aware feature representation. Besides the above three modules, since we have the ground truth depth value of the input RGB image, we...
A novel representation of a triangular mesh surface using a set of scale-invariant measures is proposed. The measures consist of angles of the triangles (triangle angles) and dihedral angles along the edges (edge angles) which are scale and rigidity independent. The vertex coordinates for a mesh...
@misc{lin2023scaleaware, title={Scale-Aware Modulation Meet Transformer}, author={Weifeng Lin and Ziheng Wu and Jiayu Chen and Jun Huang and Lianwen Jin}, year={2023}, eprint={2307.08579}, archivePrefix={arXiv}, primaryClass={cs.CV} } ...
Scale-Aware Domain Adaptive Faster R-CNN 目标检测通常假设训练和测试样本来自一个相同的分布,然而,这在实践中并不总是成立。这样的分布不匹配可能会导致显著的性能下降。在这一工作中,我们提出了尺度感知域自适应Faster R-CNN,旨在提高目标检测的跨域鲁棒性。特别是,我们的模型改进了传统的Faster R-CNN模型,在两...
《AdaZoom: Towards Scale-Aware Large Scene Object Detection》 笔记 1. 研究动机 1.1 挑战与困难 小目标检测和对象尺度差异存在挑战 现有研究方法对于大场景中如此极端尺度变化的物体缺乏灵活性,缺乏对不同尺度物体的适应性。 1.2 解决方案 构建了一个自适应缩放网络(简称AdaZoom),对大场景图像中小物体的区域进行...