HR-NAS: Searching Efficient High-Resolution Neural Architectures with Lightweight Transformers

计算机科学 失败 卷积神经网络 变压器 编码(内存) 人工智能 计算 分割 编码 建筑 机器学习 计算机工程 模式识别(心理学) 并行计算 算法 物理 艺术 基因 视觉艺术 量子力学 电压 生物化学 化学
作者
Mingyu Ding,Xiaochen Lian,Linjie Yang,Peng Wang,Xian-Min Jin,Zhiwu Lu,Ping Luo
标识
DOI:10.1109/cvpr46437.2021.00300
摘要

High-resolution representations (HR) are essential for dense prediction tasks such as segmentation, detection, and pose estimation. Learning HR representations is typically ignored in previous Neural Architecture Search (NAS) methods that focus on image classification. This work proposes a novel NAS method, called HR-NAS, which is able to find efficient and accurate networks for different tasks, by effectively encoding multiscale contextual information while maintaining high-resolution representations. In HR-NAS, we renovate the NAS search space as well as its searching strategy. To better encode multiscale image contexts in the search space of HR-NAS, we first carefully design a lightweight transformer, whose computational complexity can be dynamically changed with respect to different objective functions and computation budgets. To maintain high-resolution representations of the learned networks, HR-NAS adopts a multi-branch architecture that provides convolutional encoding of multiple feature resolutions, inspired by HRNet [73]. Last, we proposed an efficient fine-grained search strategy to train HR-NAS, which effectively explores the search space, and finds optimal architectures given various tasks and computation resources. As shown in Fig. 1 (a), HR-NAS is capable of achieving state-of-the-art trade-offs between performance and FLOPs for three dense prediction tasks and an image classification task, given only small computational budgets. For example, HR-NAS surpasses SqueezeNAS [63] that is specially designed for semantic segmentation while improving efficiency by 45.9%. Code is available at https://github.com/dingmyu/HR-NAS.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
钱多多发布了新的文献求助10
刚刚
kyukyubiu完成签到,获得积分10
1秒前
曾经不言发布了新的文献求助10
2秒前
妮妮完成签到,获得积分10
2秒前
自由如风完成签到 ,获得积分10
2秒前
檀熹发布了新的文献求助10
2秒前
wlj发布了新的文献求助10
3秒前
的墨完成签到,获得积分10
3秒前
3秒前
小铭完成签到,获得积分10
3秒前
3秒前
Crush完成签到,获得积分10
4秒前
开心夏天完成签到,获得积分10
4秒前
浅见春子完成签到,获得积分10
4秒前
xuuuuu完成签到,获得积分10
4秒前
大模型应助LR采纳,获得10
4秒前
过于傻逼完成签到,获得积分10
5秒前
突突兔完成签到 ,获得积分10
5秒前
5秒前
6秒前
6秒前
猪猪hero应助山影流霞采纳,获得10
6秒前
wangmou完成签到,获得积分10
6秒前
:P完成签到,获得积分10
7秒前
7秒前
微微发布了新的文献求助10
7秒前
肖战战完成签到 ,获得积分10
7秒前
douzi完成签到,获得积分10
8秒前
8秒前
8秒前
Bryan应助林狗采纳,获得10
8秒前
汉堡包应助王博林采纳,获得10
8秒前
goldenfleece完成签到,获得积分10
8秒前
ye发布了新的文献求助10
9秒前
9秒前
9秒前
9秒前
健忘的幼晴完成签到,获得积分10
9秒前
檀熹完成签到,获得积分10
9秒前
9秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
Cognitive Neuroscience: The Biology of the Mind 1000
Technical Brochure TB 814: LPIT applications in HV gas insulated switchgear 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
A Preliminary Study on Correlation Between Independent Components of Facial Thermal Images and Subjective Assessment of Chronic Stress 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3968964
求助须知:如何正确求助?哪些是违规求助? 3513877
关于积分的说明 11170569
捐赠科研通 3249201
什么是DOI,文献DOI怎么找? 1794692
邀请新用户注册赠送积分活动 875297
科研通“疑难数据库(出版商)”最低求助积分说明 804755