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Semantic segmentation aims to predict a label for each pixel in an image. It is one of the most fundamental prob-lems in computer vision, and is widely applied in a lot of areas such as automatic driving and human-machine inter-action. Recent semantic segmentation methods mainly focus on exploiting the contextual information in high-level features

Instance segmentation is to learn the pixel-level similarity and instance tracking is to learn the similarity between instances. Thus, instance track-ing is achieved seamlessly and naturally in the same framework of instance segmentation. • The key to the success of VisTR is a new strategy for instance sequence matching and segmentation, which

Modeling, Segmentation, and Reconstruction Feng Liu Luan Tran Xiaoming Liu Michigan State University, East Lansing MI 48824 fliufeng6, tranluan, liuxmg@msu.edu Abstract Inferring 3D structure of a generic object from a 2D im-age is a long-standing objective of computer vision. Con-ventional approaches either learn completely from CAD-

End-to-End Video Instance Segmentation with Transformers Yuqing Wang1, Zhaoliang Xu1, Xinlong Wang2, Chunhua Shen2, Baoshan Cheng1, Hao Shen1*, Huaxia Xia 1 1 Meituan 2 The University of Adelaide, Australia {wangyuqing06, shenhao04}@meituan.com CNN transformer encoder-decoder no object

Progressive Semantic Segmentation Chuong Huynh1 Anh Tuan Tran1,2 Khoa Luu1,3 Minh Hoai1,4 1VinAI Research, Hanoi, Vietnam, 2VinUniversity, Hanoi, Vietnam 3University of Arkansas, Fayetteville, AR 72701, USA 4Stony Brook University, Stony Brook, NY 11790, USA {v.chuonghm,v.anhtt152,v.khoal,v.hoainm}@vinai.io Abstract The objective of this work is to …

Contribute to amusi/CVPR2021-Papers-with-Code development by creating an account on GitHub. ... End-to-End Video Instance Segmentation with Transformers.

We present the WoodScape fisheye semantic segmentation challenge for autonomous driving which was held as part of the CVPR 2021 Workshop on ...

At CVPR 2021, CVFAD will continue to bring together artists, designers, and computer ... image segmentation, attribute discovery and trend forecast, etc.

CVPR 2021 Virtual ... June 19, 2021: Workshop @ CVPR 2021 Virtual ... images of skin lesions with diagnostic category information and segmentation masks.

In this paper, we propose 4D panoptic LiDAR segmentation to assign a semantic class and a temporally-consistent instance ID to a sequence of 3D points.

推荐阅读： 极市平台：iccv 2021 结果出炉！最新120篇iccv2021论文分方向汇总（更新中）作为计算机视觉领域三大顶会之一，cvpr2021目前已公布了所有接收论文id，一共有1663篇论文被接收，接收率为23.7%，虽然接受…

Semantic Segmentation. In recent years, benefited from the rapid development of deep learning, semantic segmen-tation has achieved great success. Most of the modern se-mantic segmentation models [23, 13, 25, 19, 5, 32, 8, 12] are based on fully convolutional networks (FCN) [23], which first replaces the fully connected layers in com-

In this work, we present our polygonal building extraction framework using frame field learning.Paper: https://arxiv.org/abs/2004.14875GitHub: https://github...

Most online multi-object trackers perform object detection stand-alone in a neural net without any input from tracking. In this paper, we present a new ...

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Jul 07, 2021 · CVPR2021_PFNet Camouflaged Object Segmentation With Distraction Mining. Haiyang Mei, Ge-Peng Ji, Ziqi Wei, Xin Yang, Xiaopeng Wei, Deng-Ping Fan [Project Page] Abstract. Camouflaged object segmentation (COS) aims to identify objects that are "perfectly" assimilate into their surroundings, which has a wide range of valuable applications.

... Augmentation Consistency for Adapting Semantic Segmentation (CVPR 2021) ... segmentation by enforcing consistency across photometric

[CVPR2021/PaperSummary]YOLOX: Exceeding YOLO Series in 2021 ... Simple copy-paste is a strong data augmentation method for instance segmentation.

These CVPR 2021 papers are the Open Access versions, provided by the ... GPU memory usage or losing the fine details in the output segmentation map.

Panoptic segmentation. Traditional approaches mainly conduct segmentation for things and stuff separately. The benchmark for panoptic segmentation [19] directly com-bines predictions of things and stuff from different models, causing heavy computational overhead. To solve this prob-lem, methods have been proposed by dealing with things