VALSE

VALSE 首页 活动通知 查看内容

VALSE 论文速览 第172期:Learning to Upsample by Learning to Sample

2024-5-20 13:32| 发布者: 程一-计算所| 查看: 274| 评论: 0

摘要: 为了使得视觉与学习领域相关从业者快速及时地了解领域的最新发展动态和前沿技术进展,VALSE最新推出了《论文速览》栏目,将在每周发布一至两篇顶会顶刊论文的录制视频,对单个前沿工作进行细致讲解。本期VALSE论文速 ...

为了使得视觉与学习领域相关从业者快速及时地了解领域的最新发展动态和前沿技术进展,VALSE最新推出了《论文速览》栏目,将在每周发布一至两篇顶会顶刊论文的录制视频,对单个前沿工作进行细致讲解。本期VALSE论文速览选取了来自华中科技大学的特征上采样 (Feature Upsampling)的工作。该工作由陆昊副教授指导,论文一作刘文泽同学录制。


论文题目:

Learning to Upsample by Learning to Sample

作者列表:

刘文泽 (华中科技大学),陆昊 (华中科技大学),付洪涛 (华中科技大学),曹治国 (华中科技大学)


B站观看网址:

https://www.bilibili.com/video/BV1Sj42197KT/


论文摘要:

We present DySample, an ultra-lightweight and effective dynamic upsampler. While impressive performance gains have been witnessed from recent kernel-based dynamic upsamplers such as CARAFE, FADE, and SAPA, they introduce much workload, mostly due to the time-consuming dynamic convolution and the additional sub-network used to generate dynamic kernels. Further, the need for high-res feature guidance of FADE and SAPA somehow limits their application scenarios. To address these concerns, we bypass dynamic convolution and formulate upsampling from the perspective of point sampling, which is more resource-efficient and can be easily implemented with the standard built-in function in PyTorch. We first showcase a naive design, and then demonstrate how to strengthen its upsampling behavior step by step towards our new upsampler, DySample. Compared with former kernel-based dynamic upsamplers, DySample requires no customized CUDA package and has much fewer parameters, FLOPs, GPU memory, and latency. Besides the light-weight characteristics, DySample outperforms other upsamplers across five dense prediction tasks, including semantic segmentation, object detection, instance segmentation, panoptic segmentation, and monocular depth estimation.


参考文献:

[1] Wenze Liu, Hao Lu, Hongtao Fu, Zhiguo Cao. Learning to Upsample by Learning to Sample. IEEE International Conference on Computer Vision (ICCV), 2023.


论文链接:

[https://arxiv.org/abs/2308.15085]

 

代码链接:

[https://github.com/tiny-smart/dysample]

 

视频讲者简介:

Wenze Liu is currently a master student at Huazhong University of Science and Technology. He received the bachelor degree in Automation from Huazhong University of Science and Technology in 2021. His research interests include computer vision and pattern recognition. He published several articles in ECCV, NeurIPS and ICCV. He currently studies dense prediction and multimodal AI.


个人主页:

https://github.com/teleppo



特别鸣谢本次论文速览主要组织者:

月度轮值AC:李爽 (北京理工大学)


活动参与方式

1、VALSE每周举行的Webinar活动依托B站直播平台进行,欢迎在B站搜索VALSE_Webinar关注我们!

直播地址:

https://live.bilibili.com/22300737;

历史视频观看地址:

https://space.bilibili.com/562085182/ 


2、VALSE Webinar活动通常每周三晚上20:00进行,但偶尔会因为讲者时区问题略有调整,为方便您参加活动,请关注VALSE微信公众号:valse_wechat 或加入VALSE QQ T群,群号:863867505);


*注:申请加入VALSE QQ群时需验证姓名、单位和身份缺一不可。入群后,请实名,姓名身份单位。身份:学校及科研单位人员T;企业研发I;博士D;硕士M。


3、VALSE微信公众号一般会在每周四发布下一周Webinar报告的通知。


4您也可以通过访问VALSE主页:http://valser.org/ 直接查看Webinar活动信息。Webinar报告的PPT(经讲者允许后),会在VALSE官网每期报告通知的最下方更新。

小黑屋|手机版|Archiver|Vision And Learning SEminar

GMT+8, 2024-6-22 08:15 , Processed in 0.014093 second(s), 14 queries .

Powered by Discuz! X3.4

Copyright © 2001-2020, Tencent Cloud.

返回顶部