In the remote sensing field, Change Detection (CD) aims to identify and
localize the changed regions from dual-phase images over the same places.
Recently, it has achieved great progress with the advances of deep learning.
However, current methods generally deliver incomplete CD regions and irregular
CD boundaries due to the limited representation ability of the extracted visual
features. To relieve these issues, in this work we propose a novel
Transformer-based learning framework named TransY-Net for remote sensing image
CD, which improves the feature extraction from a global view and combines
multi-level visual features in a pyramid manner. More specifically, the
proposed framework first utilizes the advantages of Transformers in long-range
dependency modeling. It can help to learn more discriminative global-level
features and obtain complete CD regions. Then, we introduce a novel pyramid
structure to aggregate multi-level visual features from Transformers for
feature enhancement. The pyramid structure grafted with a Progressive Attention
Module (PAM) can improve the feature representation ability with additional
inter-dependencies through spatial and channel attentions. Finally, to better
train the whole framework, we utilize the deeply-supervised learning with
multiple boundary-aware loss functions. Extensive experiments demonstrate that
our proposed method achieves a new state-of-the-art performance on four optical
and two SAR image CD benchmarks. The source code is released at
this https URL

本研究提出了一种名为 TransY-Net 的新型变化检测学习框架，通过全局视图改善特征提取，以金字塔方式结合多级视觉特征，利用 Transformer 进行长程依赖建模并使用深度监督学习，获得了优于其他方法的性能。

TransY-Net：用于遥感图像变化检测的全 Transformer 网络学习

TransY-Net:Learning Fully Transformer Networks for Change Detection of  Remote Sensing Images

In computation pathology, the pyramid structure of gigapixel Whole Slide
Images (WSIs) has recently been studied for capturing various information from
individual cell interactions to tissue microenvironments. This hierarchical
structure is believed to be beneficial for cancer diagnosis and prognosis
tasks. However, most previous hierarchical WSI analysis works (1) only
characterize local or global correlations within the WSI pyramids and (2) use
only unidirectional interaction between different resolutions, leading to an
incomplete picture of WSI pyramids. To this end, this paper presents a novel
Hierarchical Interaction Graph-Transformer (i.e., HIGT) for WSI analysis. With
Graph Neural Network and Transformer as the building commons, HIGT can learn
both short-range local information and long-range global representation of the
WSI pyramids. Considering that the information from different resolutions is
complementary and can benefit each other during the learning process, we
further design a novel Bidirectional Interaction block to establish
communication between different levels within the WSI pyramids. Finally, we
aggregate both coarse-grained and fine-grained features learned from different
levels together for slide-level prediction. We evaluate our methods on two
public WSI datasets from TCGA projects, i.e., kidney carcinoma (KICA) and
esophageal carcinoma (ESCA). Experimental results show that our HIGT
outperforms both hierarchical and non-hierarchical state-of-the-art methods on
both tumor subtyping and staging tasks.

本文提出了一种用于 WSI 分析的新型 Hierarchical Interaction Graph-Transformer（HIGT），通过学习 WSI 金字塔的局部和全局特征，并建立不同层次之间的双向交互，实现了肿瘤亚型和分期任务上的优于现有方法的性能。

HIGT：用于全幻灯片图像分析的分层交互图变换器

HIGT: Hierarchical Interaction Graph-Transformer for Whole Slide Image  Analysis

Vision transformer (ViT) has recently shown its strong capability in
achieving comparable results to convolutional neural networks (CNNs) on image
classification. However, vanilla ViT simply inherits the same architecture from
the natural language processing directly, which is often not optimized for
vision applications. Motivated by this, in this paper, we propose a new
architecture that adopts the pyramid structure and employ a novel
regional-to-local attention rather than global self-attention in vision
transformers. More specifically, our model first generates regional tokens and
local tokens from an image with different patch sizes, where each regional
token is associated with a set of local tokens based on the spatial location.
The regional-to-local attention includes two steps: first, the regional
self-attention extract global information among all regional tokens and then
the local self-attention exchanges the information among one regional token and
the associated local tokens via self-attention. Therefore, even though local
self-attention confines the scope in a local region but it can still receive
global information. Extensive experiments on four vision tasks, including image
classification, object and keypoint detection, semantics segmentation and
action recognition, show that our approach outperforms or is on par with
state-of-the-art ViT variants including many concurrent works. Our source codes
and models are available at this https URL

本文提出了一种采用金字塔结构和新的区域到局部注意力的视觉 transformer（ViT）架构，可以在图像分类和目标检测等四个任务上优于目前最先进的视觉 transformer（ViT）变体。

RegionViT: 基于区域到局部的视觉转换器注意力机制

RegionViT: Regional-to-Local Attention for Vision Transformers

Video interpolation aims to generate a non-existent intermediate frame given
the past and future frames. Many state-of-the-art methods achieve promising
results by estimating the optical flow between the known frames and then
generating the backward flows between the middle frame and the known frames.
However, these methods usually suffer from the inaccuracy of estimated optical
flows and require additional models or information to compensate for flow
estimation errors. Following the recent development in using deformable
convolution (DConv) for video interpolation, we propose a light but effective
model, called Pyramid Deformable Warping Network (PDWN). PDWN uses a pyramid
structure to generate DConv offsets of the unknown middle frame with respect to
the known frames through coarse-to-fine successive refinements. Cost volumes
between warped features are calculated at every pyramid level to help the
offset inference. At the finest scale, the two warped frames are adaptively
blended to generate the middle frame. Lastly, a context enhancement network
further enhances the contextual detail of the final output. Ablation studies
demonstrate the effectiveness of the coarse-to-fine offset refinement, cost
volumes, and DConv. Our method achieves better or on-par accuracy compared to
state-of-the-art models on multiple datasets while the number of model
parameters and the inference time are substantially less than previous models.
Moreover, we present an extension of the proposed framework to use four input
frames, which can achieve significant improvement over using only two input
frames, with only a slight increase in the model size and inference time.

通过金字塔可变卷积结构和上下文增强网络，我们提出了一种称为 Pyramid Deformable Warping Network（PDWN）的轻量而有效的模型，可用于视频帧插值。该方法使用粗到细的连续细化来生成未知中间帧相对于已知帧的 DConv 偏移，并在每个金字塔级别计算扭曲特性之间的成本卷积以帮助偏移量推断。在最细的尺度上，这两个扭曲帧被自适应地混合以生成中间帧，其准确性可与同类模型相媲美甚至更优。