ECCV的全称是European Conference on Computer Vision(欧洲计算机视觉国际会议) ,两年一次,是计算机视觉三大会议(另外两个是ICCV和CVPR)之一。每次会议在全球范围录用论文300篇左右,主要的录用论文都来自美国、欧洲等顶尖实验室及研究所,中国大陆的论文数量一般在10-20篇之间。

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针对自监督学习的深度聚类是无监督视觉表示学习中一个非常重要和有前途的方向,因为设计前置任务需要较少的领域知识。而关键组件嵌入聚类由于需要保存整个数据集的全局潜在嵌入,限制了其扩展到超大规模的数据集。在这项工作中,我们的目标是使这个框架在不降低性能的情况下更加简单和优雅。提出了一种不使用嵌入聚类的无监督图像分类框架,与标准的监督训练方法非常相似。为了进一步解释,我们进一步分析了其与深度聚类和对比学习的关系。在ImageNet数据集上进行了大量的实验,验证了该方法的有效性。此外,在迁移学习基准上的实验验证了它对其他下游任务的推广,包括多标签图像分类、目标检测、语义分割和小样本图像分类。

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https://arxiv.org/abs/2006.11480

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Drones, or general UAVs, equipped with cameras have been fast deployed with a wide range of applications, including agriculture, aerial photography, and surveillance. Consequently, automatic understanding of visual data collected from drones becomes highly demanding, bringing computer vision and drones more and more closely. To promote and track the developments of object detection and tracking algorithms, we have organized three challenge workshops in conjunction with ECCV 2018, ICCV 2019 and ECCV 2020, attracting more than 100 teams around the world. We provide a large-scale drone captured dataset, VisDrone, which includes four tracks, i.e., (1) image object detection, (2) video object detection, (3) single object tracking, and (4) multi-object tracking. In this paper, we first present a thorough review of object detection and tracking datasets and benchmarks, and discuss the challenges of collecting large-scale drone-based object detection and tracking datasets with fully manual annotations. After that, we describe our VisDrone dataset, which is captured over various urban/suburban areas of 14 different cities across China from North to South. Being the largest such dataset ever published, VisDrone enables extensive evaluation and investigation of visual analysis algorithms for the drone platform. We provide a detailed analysis of the current state of the field of large-scale object detection and tracking on drones, and conclude the challenge as well as propose future directions. We expect the benchmark largely boost the research and development in video analysis on drone platforms. All the datasets and experimental results can be downloaded from https://github.com/VisDrone/VisDrone-Dataset.

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