一些视频插帧老 paper 阅读 (AdaConv, SepConv, DVF, SuperSlomo, DAIN, QVI, CAIN, SoftSplat, AdaCoF, RIFE) 接上文: 一些基础模型老 paper 阅读 (ResNext, RegNet, ACNet, RepVGG, RepSR, DBB, OREPA, NAFNet),下文 CVPR24 一些视频插帧新 paper 阅读 SGM、IQ-VFI、SportsSloMo、PerVFI远古科技,...
CVPR2024接收paper分享,作者来自ETH Zurich等联合团队: 3D Few-shot分割结果示例 1. 技术背景 3D场景理解在自动驾驶、智能机器人等领域扮演着至关重要的角色,它使设备能够感知并理解周围的三维世界。尽管传统的全监督学习模型在特定类别的识别上表现出色,但这些模型通常只限于识别这些预定义的类别。这就意味着,每当需要...
本文由原paper一作胡良校博士全权翻译写作,胡良校博士就读于哈尔滨工业大学,师从张盛平教授,主要研究方向为人体数字化身建模与驱动,有多项工作发布于顶会顶刊上,本篇paper入选了CVPR 2024。 个人主页:https://huliangxiao.github.io/ https:...
论文/Paper: http://arxiv.org/pdf/2204.04668 代码/Code: None 深度估计/Depth Estimation - 1 篇 HiMODE: A Hybrid Monocular Omnidirectional Depth Estimation Model 标题:HIMODE:混合单眼全向深度估计模型 论文/Paper: http://arxiv.org/pdf/2204.05007 代码/Code: None 其他/Other - 12 篇 Single-Photon ...
ReadPaper论文阅读 用于3D目标检测的焦点稀疏卷积神经网络【CVPR2022】【3D检测】 ReadPaper论文阅读 【NeurIPS'23】spotlight:用于真实图像去模糊的层次结合扩散模型 ReadPaper论文阅读 【CVPR'24】特征适配:在计算病理学中达到病理大模型性能水平 ReadPaper论文阅读 ...
This paper delves into the nuanced challenge of tailoring the Segment Anything Models (SAMs) for integration with event data, with the overarching objective of attaining robust and universal object segmentation within the event-centric domain. Getting Started Installation Clone the repository locally: ...
这篇论文在处理视频帧插值时，特别针对大运动场景提出了一种创新方法。它建立在CVPR22年的论文Gmflow基础上，旨在解决全卷积插帧模型在遇到大运动时容易失败的问题。论文提出了一种将大运动区域的稀疏光流估计与原始插帧模型的光流估计相结合的策略。通过这种方式，能够利用Gmflow的强大全局匹配能力，改善大...
This repo contains the data release for our CVPR24 paper: Step Differences in Instructional Video Tushar Nagarajan, Lorenzo Torresani [arxiv] [openreview] Video: https://www.youtube.com/watch?v=52AOPbn1BAA Annotations The datasets for the three tasks can be found in the data/ folder. Each ...
model. however, in real-life applications, some clients may have severely limited resources and can only train a much smaller local model. this paper presents scalefl, a novel fl approach with two distinctive mechanisms to handle resource heter...
本文提出bottom-up的目标检测框架-ExtremeNet,用于检测目标物的四个极点。利用一个关键点检测网络,针对每个类别预测四个multi-peak heatmaps进而得到极点。对于每个类别又增加了一个heatmap用于预测目标的中心,其heatmap值为边框x,y方向的均值。然后基于纯几何的方法对extreme点进行组合。从每个map中得到一个点,得到四...