NTIRE 2021 Multi-modal Aerial View Object Classification Challenge

Liu, Jerrick; Inkawhich, Nathan; Nina, Oliver; Timofte, Radu; Duan, Yuru; Li, Gongzhe; Geng, Xueli; Cai, Huanqia

doi:10.1109/cvprw53098.2021.00071

Cited by 34 publications

(16 citation statements)

References 11 publications

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“…Attributes prediction shares some similarities with other popular topics in research such as object detection [88,36], image segmentation [32,42] and classification [50,81]. However, visual attributes recognition has its unique characteristics and challenges that distinguish it from other vision problems such as multi-class classification [72] or multi-label classification [17,13].…”

Section: Related Workmentioning

confidence: 99%

CAR -- Cityscapes Attributes Recognition A Multi-category Attributes Dataset for Autonomous Vehicles

Metwaly¹,

Kim²,

Branson³

et al. 2021

Preprint

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Self-driving vehicles are the future of transportation. With current advancements in this field, the world is getting closer to safe roads with almost zero probability of having accidents and eliminating human errors. However, there is still plenty of research and development necessary to reach a level of robustness. One important aspect is to understand a scene fully including all details. As some characteristics (attributes) of objects in a scene (drivers' behavior for instance) could be imperative for correct decision making. However, current algorithms suffer from low-quality datasets with such rich attributes. Therefore, in this paper, we present a new dataset for attributes recognition -Cityscapes Attributes Recognition (CAR). The new dataset extends the well-known dataset Cityscapes by adding an additional yet important annotation layer of attributes of objects in each image. Currently, we have annotated more than 32k instances of various categories (Vehicles, Pedestrians, etc.). The dataset has a structured and tailored taxonomy where each category has its own set of possible attributes. The tailored taxonomy focuses on attributes that is of most beneficent for developing better self-driving algorithms that depend on accurate computer vision and scene comprehension. We have also created an API for the dataset to ease the usage of CAR. The API can be accessed through https://github.com/kareem-metwaly/CAR-API * Kareem Metwaly was in an internship at Scale AI through this work.

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Section: Related Workmentioning

confidence: 99%

CAR -- Cityscapes Attributes Recognition A Multi-category Attributes Dataset for Autonomous Vehicles

Metwaly¹,

Kim²,

Branson³

et al. 2021

Preprint

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show abstract

“…This challenge is one of the NTIRE 2021 associated challenges: nonhomogeneous dehazing [4], defocus deblurring using dual-pixel [2], depth guided image relighting [16], image deblurring [39], multi-modal aerial view imagery classification [33], learning the super-resolution space [35], quality enhancement of compressed video (this report), video super-resolution [46], perceptual image quality assessment [18], burst super-resolution [5], and high dynamic range imaging [41].…”

Section: Ntire 2021 Challengementioning

confidence: 99%

NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Methods and Results

Yang¹,

Timofte²,

Liu³

et al. 2021

Preprint

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This paper reviews the first NTIRE challenge on quality enhancement of compressed video, with a focus on the proposed methods and results. In this challenge, the new Large-scale Diverse Video (LDV) dataset is employed. The challenge has three tracks. Tracks 1 and 2 aim at enhancing the videos compressed by HEVC at a fixed QP, while Track 3 is designed for enhancing the videos compressed by x265 at a fixed bit-rate. Besides, the quality enhancement of Tracks 1 and 3 targets at improving the fidelity (PSNR), and Track 2 targets at enhancing the perceptual quality. The three tracks totally attract 482 registrations. In the test phase, 12 teams, 8 teams and 11 teams submitted the final results of Tracks 1, 2 and 3, respectively. The proposed methods and solutions gauge the state-of-the-art of video quality enhancement. The homepage of the challenge: https: //github.com/RenYang-home/NTIRE21_VEnh

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“…The goal of the NTIRE 2021 Challenge on Burst Super-Resolution is to encourage further research in the burst SR task and provide a common benchmark for evaluating different methods. This challenge is one of the NTIRE 2021 associated challenges: nonhomogeneous dehazing [2], defocus deblurring using dual-pixel [1], depth guided image relighting [10], image deblurring [39], multi-modal aerial view imagery classification [30], learning the superresolution space [33], quality enhancement of heavily compressed videos [56], video super-resolution [47], perceptual image quality assessment [12], burst super-resolution, and high dynamic range [42]. The burst super-resolution challenge contained two tracks.…”

Section: Ntire 2021 Challengementioning

confidence: 99%

NTIRE 2021 Challenge on Burst Super-Resolution: Methods and Results

Bhat¹,

Danelljan²,

Radu³

et al. 2021

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This paper reviews the NTIRE2021 challenge on burst super-resolution. Given a RAW noisy burst as input, the task in the challenge was to generate a clean RGB image with 4 times higher resolution. The challenge contained two tracks; Track 1 evaluating on synthetically generated data, and Track 2 using real-world bursts from mobile camera. In the final testing phase, 6 teams submitted results using a diverse set of solutions. The top-performing methods set a new state-of-the-art for the burst super-resolution task. * Goutam Bhat, Martin Danelljan, and Radu Timofte at ETH Zurich are the NTIRE 2021 challenge organizers. The other authors participated in the challenge and are listed alphabetically. Appendix A contains the participants' team names and affiliations. NTIRE 2021 webpage: https://data.vision.ee.ethz.ch/cvl/ntire21/

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NTIRE 2021 Multi-modal Aerial View Object Classification Challenge

Cited by 34 publications

References 11 publications

CAR -- Cityscapes Attributes Recognition A Multi-category Attributes Dataset for Autonomous Vehicles

CAR -- Cityscapes Attributes Recognition A Multi-category Attributes Dataset for Autonomous Vehicles

NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Methods and Results

NTIRE 2021 Challenge on Burst Super-Resolution: Methods and Results

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