We aim at providing the object detection community with an efficient and
performant object detector, termed YOLO-MS. The core design is based on a
series of investigations on how convolutions with different kernel sizes affect
the detection performance of objects at different scales. The outcome is a new
strategy that can strongly enhance multi-scale feature representations of
real-time object detectors. To verify the effectiveness of our strategy, we
build a network architecture, termed YOLO-MS. We train our YOLO-MS on the MS
COCO dataset from scratch without relying on any other large-scale datasets,
like ImageNet, or pre-trained weights. Without bells and whistles, our YOLO-MS
outperforms the recent state-of-the-art real-time object detectors, including
YOLO-v7 and RTMDet, when using a comparable number of parameters and FLOPs.
Taking the XS version of YOLO-MS as an example, with only 4.5M learnable
parameters and 8.7G FLOPs, it can achieve an AP score of 43%+ on MS COCO, which
is about 2%+ higher than RTMDet with the same model size. Moreover, our work
can also be used as a plug-and-play module for other YOLO models. Typically,
our method significantly improves the AP of YOLOv8 from 37%+ to 40%+ with even
fewer parameters and FLOPs. Code is available at
this https URL

提供高效且表现良好的目标检测器 YOLO-MS, 基于对不同核大小的卷积如何影响不同尺度物体的检测性能进行一系列研究来实现。新的策略能够大大增强实时目标检测器的多尺度特征表示。在 MS COCO 数据集上训练的 YOLO-MS, 不依赖于其他大规模数据集或预训练权重，在相同参数和 FLOPs 数量下，优于最近的实时目标检测器，包括 YOLO-v7 和 RTMDet。

YOLO-MS：实时物体检测的多尺度表示学习再思考

YOLO-MS: Rethinking Multi-Scale Representation Learning for Real-time  Object Detection

Convolutional neural networks (CNNs) have been the consensus for medical
image segmentation tasks. However, they suffer from the limitation in modeling
long-range dependencies and spatial correlations due to the nature of
convolution operation. Although transformers were first developed to address
this issue, they fail to capture low-level features. In contrast, it is
demonstrated that both local and global features are crucial for dense
prediction, such as segmenting in challenging contexts. In this paper, we
propose HiFormer, a novel method that efficiently bridges a CNN and a
transformer for medical image segmentation. Specifically, we design two
multi-scale feature representations using the seminal Swin Transformer module
and a CNN-based encoder. To secure a fine fusion of global and local features
obtained from the two aforementioned representations, we propose a Double-Level
Fusion (DLF) module in the skip connection of the encoder-decoder structure.
Extensive experiments on various medical image segmentation datasets
demonstrate the effectiveness of HiFormer over other CNN-based,
transformer-based, and hybrid methods in terms of computational complexity, and
quantitative and qualitative results. Our code is publicly available at:
this https URL

本文提出了一种新的方法 HiFormer，它通过使用 Swin Transformer 模块和基于 CNN 的编码器设计了两种多尺度特征表示来有效地跨越 CNN 和 Transformer 进行医学图像分割。在编码器解码器结构的跳跃连接中，我们提出了 Double-Level Fusion（DLF）模块，以确保从两个上述表示中获得的全局和局部特征的细粒度融合。在各种医学图像分割数据集上的广泛实验表明，HiFormer 在计算复杂度、定量和定性结果方面优于其他基于 CNN、Transformer 和混合方法。

HiFormer: 使用 Transformer 实现的分层多尺度表示，用于医学图像分割

HiFormer: Hierarchical Multi-scale Representations Using Transformers for Medical Image Segmentation

The recently developed vision transformer (ViT) has achieved promising
results on image classification compared to convolutional neural networks.
Inspired by this, in this paper, we study how to learn multi-scale feature
representations in transformer models for image classification. To this end, we
propose a dual-branch transformer to combine image patches (i.e., tokens in a
transformer) of different sizes to produce stronger image features. Our
approach processes small-patch and large-patch tokens with two separate
branches of different computational complexity and these tokens are then fused
purely by attention multiple times to complement each other. Furthermore, to
reduce computation, we develop a simple yet effective token fusion module based
on cross attention, which uses a single token for each branch as a query to
exchange information with other branches. Our proposed cross-attention only
requires linear time for both computational and memory complexity instead of
quadratic time otherwise. Extensive experiments demonstrate that our approach
performs better than or on par with several concurrent works on vision
transformer, in addition to efficient CNN models. For example, on the
ImageNet1K dataset, with some architectural changes, our approach outperforms
the recent DeiT by a large margin of 2\% with a small to moderate increase in
FLOPs and model parameters. Our source codes and models are available at
https://github.com/IBM/CrossViT.

本文提出了一种名为 Dual-Branch Transformer 的模型，通过使用不同尺寸的图像块来获得更强的图像特征，进而学习多尺度特征表示，并采用交叉关注的方法进行多尺度特征的融合，使得计算复杂度得到控制，并在 ImageNet1K 数据集上实现了表现优于或与几个同时期的视觉转换器相当的结果。