Recently, Transformer-based methods, which predict polygon points or Bezier
curve control points for localizing texts, are popular in scene text detection.
However, these methods built upon detection transformer framework might achieve
sub-optimal training efficiency and performance due to coarse positional query
modeling.In addition, the point label form exploited in previous works implies
the reading order of humans, which impedes the detection robustness from our
observation. To address these challenges, this paper proposes a concise Dynamic
Point Text DEtection TRansformer network, termed DPText-DETR. In detail,
DPText-DETR directly leverages explicit point coordinates to generate position
queries and dynamically updates them in a progressive way. Moreover, to improve
the spatial inductive bias of non-local self-attention in Transformer, we
present an Enhanced Factorized Self-Attention module which provides point
queries within each instance with circular shape guidance. Furthermore, we
design a simple yet effective positional label form to tackle the side effect
of the previous form. To further evaluate the impact of different label forms
on the detection robustness in real-world scenario, we establish an
Inverse-Text test set containing 500 manually labeled images. Extensive
experiments prove the high training efficiency, robustness, and
state-of-the-art performance of our method on popular benchmarks. The code and
the Inverse-Text test set are available at
this https URL

本文提出了一种 DPText-DETR 算法，利用明确的点坐标直接生成位置查询，并动态地以渐进的方式更新它们。同时，提出了一种增强的分解自我注意力模块，为每个实例提供具有圆形形状指导的点查询，以及一种简单而有效的位置标签形式来解决之前形式的副作用，在 500 个手动标记的图像上进行了实验证明了本方法在各种基准测试中的高训练效率、鲁棒性和最先进性能。

DPText-DETR: 基于 Transformer 与动态点的更好的场景文本检测

DPText-DETR: Towards Better Scene Text Detection with Dynamic Points in  Transformer

Recent non-local self-attention methods have proven to be effective in
capturing long-range dependencies for semantic segmentation. These methods
usually form a similarity map of RC*C (by compressing spatial dimensions) or
RHW*HW (by compressing channels) to describe the feature relations along either
channel or spatial dimensions, where C is the number of channels, H and W are
the spatial dimensions of the input feature map. However, such practices tend
to condense feature dependencies along the other dimensions,hence causing
attention missing, which might lead to inferior results for small/thin
categories or inconsistent segmentation inside large objects. To address this
problem, we propose anew approach, namely Fully Attentional Network (FLANet),to
encode both spatial and channel attentions in a single similarity map while
maintaining high computational efficiency. Specifically, for each channel map,
our FLANet can harvest feature responses from all other channel maps, and the
associated spatial positions as well, through a novel fully attentional module.
Our new method has achieved state-of-the-art performance on three challenging
semantic segmentation datasets,i.e., 83.6%, 46.99%, and 88.5% on the Cityscapes
test set,the ADE20K validation set, and the PASCAL VOC test set,respectively.

该研究提出了 FLANet 方法，可以通过单个相似度图来同时编码空间和通道注意力，并在三个具有挑战性的语义分割数据集上最先进的性能达到 83.6％，46.99％和 88.5％。

全注意力网络用于语义分割

Fully Attentional Network for Semantic Segmentation

Context information plays an indispensable role in the success of semantic
segmentation. Recently, non-local self-attention based methods are proved to be
effective for context information collection. Since the desired context
consists of spatial-wise and channel-wise attentions, 3D representation is an
appropriate formulation. However, these non-local methods describe 3D context
information based on a 2D similarity matrix, where space compression may lead
to channel-wise attention missing. An alternative is to model the contextual
information directly without compression. However, this effort confronts a
fundamental difficulty, namely the high-rank property of context information.
In this paper, we propose a new approach to model the 3D context
representations, which not only avoids the space compression but also tackles
the high-rank difficulty. Here, inspired by tensor canonical-polyadic
decomposition theory (i.e, a high-rank tensor can be expressed as a combination
of rank-1 tensors.), we design a low-rank-to-high-rank context reconstruction
framework (i.e, RecoNet). Specifically, we first introduce the tensor
generation module (TGM), which generates a number of rank-1 tensors to capture
fragments of context feature. Then we use these rank-1 tensors to recover the
high-rank context features through our proposed tensor reconstruction module
(TRM). Extensive experiments show that our method achieves state-of-the-art on
various public datasets. Additionally, our proposed method has more than 100
times less computational cost compared with conventional non-local-based
methods.

本文提出了一种新的方法来建模三维上下文表示，该方法不仅避免了空间压缩，而且还解决了高秩困难问题。通过引入张量典范 - 解析分解理论，设计了一种低秩到高秩的上下文重构框架，实现了比传统的非局部方法更少百倍计算成本的最新技术。