Deep learning-based methods have been extensively explored for automatic
building mapping from high-resolution remote sensing images over recent years.
While most building mapping models produce vector polygons of buildings for
geographic and mapping systems, dominant methods typically decompose polygonal
building extraction in some sub-problems, including segmentation,
polygonization, and regularization, leading to complex inference procedures,
low accuracy, and poor generalization. In this paper, we propose a simple and
novel building mapping method with Hierarchical Transformers, called HiT,
improving polygonal building mapping quality from high-resolution remote
sensing images. HiT builds on a two-stage detection architecture by adding a
polygon head parallel to classification and bounding box regression heads. HiT
simultaneously outputs building bounding boxes and vector polygons, which is
fully end-to-end trainable. The polygon head formulates a building polygon as
serialized vertices with the bidirectional characteristic, a simple and elegant
polygon representation avoiding the start or end vertex hypothesis. Under this
new perspective, the polygon head adopts a transformer encoder-decoder
architecture to predict serialized vertices supervised by the designed
bidirectional polygon loss. Furthermore, a hierarchical attention mechanism
combined with convolution operation is introduced in the encoder of the polygon
head, providing more geometric structures of building polygons at vertex and
edge levels. Comprehensive experiments on two benchmarks (the CrowdAI and Inria
datasets) demonstrate that our method achieves a new state-of-the-art in terms
of instance segmentation and polygonal metrics compared with state-of-the-art
methods. Moreover, qualitative results verify the superiority and effectiveness
of our model under complex scenes.

深度学习方法在遥感影像中自动建筑物映射方面得到广泛研究。本文提出了一种简单且新颖的基于 Hierarchical Transformers 的建筑物映射方法 HiT，通过增加一个多边形头并行于分类和边界框回归头的两阶段检测结构实现了从高分辨率遥感影像中提取多边形建筑物的质量改进。经过包括分割、多边形化和正则化在内的一系列问题的分解，传统的建筑物映射方法常常导致复杂的推断过程、低准确率和差泛化能力。HiT 同时输出建筑物边界框和矢量多边形，并进行端到端的训练。多边形头采用编码 - 解码的 Transformer 架构来预测带有双向特点的序列化顶点，采用设计的双向多边形损失进行监督。此外，多边形头的编码器引入了分层注意机制和卷积操作，提供了顶点和边界级别的更多几何结构。在 CrowdAI 和 Inria 两个基准数据集上的综合实验表明，与现有技术方法相比，我们的方法在实例分割和多边形度量方面达到了最新的良好结果。此外，定性结果验证了我们的模型在复杂场景下的优越性和有效性。

HiT: 基于分层 Transformers 的建筑物映射

HiT: Building Mapping with Hierarchical Transformers

Transformer-based visual trackers have demonstrated significant progress
owing to their superior modeling capabilities. However, existing trackers are
hampered by low speed, limiting their applicability on devices with limited
computational power. To alleviate this problem, we propose HiT, a new family of
efficient tracking models that can run at high speed on different devices while
retaining high performance. The central idea of HiT is the Bridge Module, which
bridges the gap between modern lightweight transformers and the tracking
framework. The Bridge Module incorporates the high-level information of deep
features into the shallow large-resolution features. In this way, it produces
better features for the tracking head. We also propose a novel dual-image
position encoding technique that simultaneously encodes the position
information of both the search region and template images. The HiT model
achieves promising speed with competitive performance. For instance, it runs at
61 frames per second (fps) on the Nvidia Jetson AGX edge device. Furthermore,
HiT attains 64.6% AUC on the LaSOT benchmark, surpassing all previous efficient
trackers.

引入 HiT 模型和 Bridge Module 以及双图像位置编码技术，实现高速且性能优越的追踪模型。

探索轻量级分层视觉转换器用于高效视觉跟踪

Exploring Lightweight Hierarchical Vision Transformers for Efficient  Visual Tracking

Source code representation with deep learning techniques is an important
research field. There have been many studies that learn sequential or
structural information for code representation. But sequence-based models and
non-sequence-models both have their limitations. Researchers attempt to
incorporate structural information to sequence-based models, but they only mine
part of token-level hierarchical structure information. In this paper, we
analyze how the complete hierarchical structure influences the tokens in code
sequences and abstract this influence as a property of code tokens called
hierarchical embedding. The hierarchical embedding is further divided into
statement-level global hierarchy and token-level local hierarchy. Furthermore,
we propose the Hierarchy Transformer (HiT), a simple but effective sequence
model to incorporate the complete hierarchical embeddings of source code into a
Transformer model. We demonstrate the effectiveness of hierarchical embedding
on learning code structure with an experiment on variable scope detection task.
Further evaluation shows that HiT outperforms SOTA baseline models and show
stable training efficiency on three source code-related tasks involving
classification and generation tasks across 8 different datasets.

本文探讨了代码序列中层次结构对令牌的影响，并将其抽象为代码令牌的一个属性。我们提出了一种简单但有效的模型 HiT，将完整的层次结构嵌入源代码序列。实验证明，层次嵌入可用于学习代码结构，并在多个数据集上的分类和生成任务中显示出 HiT 性能的稳定训练效率。

将全局和局部层次信息植入基于序列的代码表示模型

Implant Global and Local Hierarchy Information to Sequence based Code Representation Models

Attention-based models, exemplified by the Transformer, can effectively model
long range dependency, but suffer from the quadratic complexity of
self-attention operation, making them difficult to be adopted for
high-resolution image generation based on Generative Adversarial Networks
(GANs). In this paper, we introduce two key ingredients to Transformer to
address this challenge. First, in low-resolution stages of the generative
process, standard global self-attention is replaced with the proposed
multi-axis blocked self-attention which allows efficient mixing of local and
global attention. Second, in high-resolution stages, we drop self-attention
while only keeping multi-layer perceptrons reminiscent of the implicit neural
function. To further improve the performance, we introduce an additional
self-modulation component based on cross-attention. The resulting model,
denoted as HiT, has a nearly linear computational complexity with respect to
the image size and thus directly scales to synthesizing high definition images.
We show in the experiments that the proposed HiT achieves state-of-the-art FID
scores of 30.83 and 2.95 on unconditional ImageNet $128 \times 128$ and FFHQ
$256 \times 256$, respectively, with a reasonable throughput. We believe the
proposed HiT is an important milestone for generators in GANs which are
completely free of convolutions. Our code is made publicly available at
this https URL

本文介绍了将多维块自注意力与多层感知机结合的 Transformer 模型 HiT，该模型在高清图像生成领域的表现优于传统基于卷积的方法。