In this paper, we present a light-weight detection transformer, LW-DETR,
which outperforms YOLOs for real-time object detection. The architecture is a
simple stack of a ViT encoder, a projector, and a shallow DETR decoder. Our
approach leverages recent advanced techniques, such as training-effective
techniques, e.g., improved loss and pretraining, and interleaved window and
global attentions for reducing the ViT encoder complexity. We improve the ViT
encoder by aggregating multi-level feature maps, and the intermediate and final
feature maps in the ViT encoder, forming richer feature maps, and introduce
window-major feature map organization for improving the efficiency of
interleaved attention computation. Experimental results demonstrate that the
proposed approach is superior over existing real-time detectors, e.g., YOLO and
its variants, on COCO and other benchmark datasets. Code and models are
available at (this https URL).

本文介绍了一种轻量级检测变换器 (LW-DETR)，它在实时目标检测方面优于 YOLO，架构简单，由 ViT 编码器、投影器和浅层 DETR 解码器组成，结合训练有效技术和插入式窗口和全局注意力等最新技术，改进了 ViT 编码器的复杂性，实验证明所提出的方法在 COCO 和其他基准数据集上优于现有的实时检测器。

LW-DETR：实时检测的一种用于替代 YOLO 的 Transformer

LW-DETR: A Transformer Replacement to YOLO for Real-Time Detection

Given a group of images, co-salient object detection (CoSOD) aims to
highlight the common salient object in each image. There are two factors
closely related to the success of this task, namely consensus extraction, and
the dispersion of consensus to each image. Most previous works represent the
group consensus using local features, while we instead utilize a hierarchical
Transformer module for extracting semantic-level consensus. Therefore, it can
obtain a more comprehensive representation of the common object category, and
exclude interference from other objects that share local similarities with the
target object. In addition, we propose a Transformer-based dispersion module
that takes into account the variation of the co-salient object in different
scenes. It distributes the consensus to the image feature maps in an
image-specific way while making full use of interactions within the group.
These two modules are integrated with a ViT encoder and an FPN-like decoder to
form an end-to-end trainable network, without additional branch and auxiliary
loss. The proposed method is evaluated on three commonly used CoSOD datasets
and achieves state-of-the-art performance.

给定一组图像，合作显著目标检测（CoSOD）的目标是突出显示每个图像中的共同显著对象。本文提出了一种基于分层 Transformer 模块的方法，用于提取语义级共识，以获得对公共对象类别更全面的表示，并排除与目标对象具有局部相似性的其他对象的干扰。此外，还提出了一种基于 Transformer 的分散模块，它考虑了不同场景中协同显著对象的变化。它以图像特征图的图像特定方式对共识进行分发，同时充分利用组内的交互。这两个模块与 ViT 编码器和类似 FPN 的解码器一起集成成一个可端到端训练的网络，不需要额外的分支和辅助损失。该方法在三个常用的 CoSOD 数据集上进行评估，并取得了最先进的性能。

带有语义级别共识提取和分散的共显目标检测

Co-Salient Object Detection with Semantic-Level Consensus Extraction and  Dispersion

Structural magnetic resonance imaging (sMRI) has shown great clinical value
and has been widely used in deep learning (DL) based computer-aided brain
disease diagnosis. Previous approaches focused on local shapes and textures in
sMRI that may be significant only within a particular domain. The learned
representations are likely to contain spurious information and have a poor
generalization ability in other diseases and datasets. To facilitate capturing
meaningful and robust features, it is necessary to first comprehensively
understand the intrinsic pattern of the brain that is not restricted within a
single data/task domain. Considering that the brain is a complex connectome of
interlinked neurons, the connectional properties in the brain have strong
biological significance, which is shared across multiple domains and covers
most pathological information. In this work, we propose a connectional style
contextual representation learning model (CS-CRL) to capture the intrinsic
pattern of the brain, used for multiple brain disease diagnosis. Specifically,
it has a vision transformer (ViT) encoder and leverages mask reconstruction as
the proxy task and Gram matrices to guide the representation of connectional
information. It facilitates the capture of global context and the aggregation
of features with biological plausibility. The results indicate that CS-CRL
achieves superior accuracy in multiple brain disease diagnosis tasks across six
datasets and three diseases and outperforms state-of-the-art models.
Furthermore, we demonstrate that CS-CRL captures more brain-network-like
properties, better aggregates features, is easier to optimize and is more
robust to noise, which explains its superiority in theory. Our source code will
be released soon.

提出一种基于连接方式的上下文表示学习模型 (CS-CRL)，用于多种脑病的诊断，特别是具有视觉变换 (ViT) 编码器和掩码重建作为代理任务和格拉姆矩阵来指导连通信息的表示，能够更好地聚合特征和提取全局上下文。实验结果表明，其在六个数据集和三种脑病的诊断任务中取得了卓越的准确性，并且胜过了现有的最先进模型。