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检索条件"任意字段=2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021"

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NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Dataset and Study

NTIRE 2021 Challenge on Quality Enhancement of Compressed Vi...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Yang, Ren Timofte, Radu Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland

ISBN: (纸本)9781665448994

This paper introduces a novel dataset for video enhancement and studies the state-of-the-art methods of the NTIRE 2021 challenge on quality enhancement of compressed video. The challenge is the first NTIRE challenge in this direction, with three competitions, hundreds of participants and tens of proposed solutions. Our newly collected Large-scale Diverse Video (LDV) dataset is employed in the challenge. In our study, we analyze the solutions of the challenges and several representative methods from previous literature on the proposed LDV dataset. We find that the NTIRE 2021 challenge advances the state-of-theart of quality enhancement on compressed video.

关键词： computer vision Databases conferences Benchmark testing Solids pattern recognition

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Multi-Class Multi-Movement Vehicle Counting Based on CenterTrack

Multi-Class Multi-Movement Vehicle Counting Based on CenterT...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Kocur, Viktor Ftacnik, Milan Comenius Univ Fac Math Phys & Informat Bratislava Slovakia

ISBN: (纸本)9781665448994

In this paper we present our approach to the Track 1 of the 2021 AI City Challenge. The goal of the challenge track is to to analyse footage captured with traffic cameras by counting the number of vehicles performing various pre-defined motions of interest. Our approach is based on the CenterTrack object detection and tracking neural network used in conjunction with a simple IoU-based tracking algorithm. In the public evaluation server our system achieved the S1 score of 0.8449 placing it at the 8th place on the public leaderboard.

关键词： computer vision Tracking conferences Urban areas Neural networks Object detection computer architecture

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Attention! Stay Focus!

Attention! Stay Focus!

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Vo, Tu BridgeAI Inc Seoul South Korea

ISBN: (纸本)9781665448994

We develop a deep convolutional neural networks (CNNs) to deal with the blurry artifacts caused by the defocus of the camera using dual-pixel images. Specifically, we develop a double attention network which consists of attentional encoders, triple locals and global local modules to effectively extract useful information from each image in the dual-pixels and select the useful information from each image and synthesize the final output image. We demonstrate the effectiveness of the proposed deblurring algorithm in terms of both qualitative and quantitative aspects by evaluating on the test set in the NTIRE 2021 Defocus Deblurring using Dual-pixel Images Challenge [1] [4].

关键词： Deep learning computer vision conferences computer architecture Feature extraction Cameras Decoding

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I Find Your Lack of Uncertainty in computer vision Disturbing

I Find Your Lack of Uncertainty in Computer Vision Disturbin...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Valdenegro-Toro, Matias German Res Ctr Artificial Intelligence Robert Hooke Str 1 D-28359 Bremen Germany

ISBN: (纸本)9781665448994

Neural networks are used for many real world applications, but often they have problems estimating their own confidence. This is particularly problematic for computer vision applications aimed at making high stakes decisions with humans and their lives. In this paper we make a meta-analysis of the literature, showing that most if not all computer vision applications do not use proper epistemic uncertainty quantification, which means that these models ignore their own limitations. We describe the consequences of using models without proper uncertainty quantification, and motivate the community to adopt versions of the models they use that have proper calibrated epistemic uncertainty, in order to enable out of distribution detection. We close the paper with a summary of challenges on estimating uncertainty for computer vision applications and recommendations.

关键词： computer vision Uncertainty Computational modeling conferences Neural networks pattern recognition

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Delta Sampling R-BERT for limited data and low-light action recognition

Delta Sampling R-BERT for limited data and low-light action ...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Hira, Sanchit Das, Ritwik Modi, Abhinav Pakhomov, Daniil Johns Hopkins Univ Baltimore MD 21218 USA

ISBN: (纸本)9781665448994

We present an approach to perform supervised action recognition in the dark. In this work, we present our results on the ARID dataset[60]. Most previous works only evaluate performance on large, well illuminated datasets like Kinetics and HMDB51. We demonstrate that our work is able to achieve a very low error rate while being trained on a much smaller dataset of dark videos. We also explore a variety of training and inference strategies including domain transfer methodologies and also propose a simple but useful frame selection strategy. Our empirical results demonstrate that we beat previously published baseline models by 11%.

关键词： Training Deep learning Technological innovation Schedules computer vision conferences pattern recognition

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Efficient CNN Architecture for Multi-modal Aerial View Object Classification

Efficient CNN Architecture for Multi-modal Aerial View Objec...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Miron, Casian Pasarica, Alexandru Timofte, Radu Gheorghe Asachi Tech Univ MCC Resources SRL Iasi Romania

ISBN: (纸本)9781665448994

The NTIRE 2021 workshop features a Multi-modal Aerial View Object Classification Challenge. Its focus is on multi-sensor imagery classification in order to improve the performance of automatic target recognition (ATR) systems. In this paper we describe our entry in this challenge, a method focused on efficiency and low computational time, while maintaining a high level of accuracy. The method is a convolutional neural network with 11 convolutions, 1 max pooling layers and 3 residual blocks which has a total of 373.130 parameters. The method ranks 3rd in the Track 2 (SAR+EO) of the challenge.

关键词： Training computer vision Target recognition conferences Systems architecture Graphics processing units computer architecture

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Estimating (and fixing) the Effect of Face Obfuscation in Video recognition

Estimating (and <i>fixing</i>) the Effect of Face Obfuscatio...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Tomei, Matteo Baraldi, Lorenzo Bronzin, Simone Cucchiara, Rita Univ Modena & Reggio Emilia Modena Italy Metaliquid Srl Milan Italy

ISBN: (纸本)9781665448994

Recent research has shown that faces can be obfuscated in large-scale datasets with a minimal performance impact on image classification and downstream tasks like object recognition. In this paper, we investigate the role of face obfuscation in video classification datasets and quantify a more significant reduction in performance caused by face blurring. To reduce such performance effects, we propose a generalized distillation approach in which a privacy-preserving action recognition network is trained with privileged information given by face identities. We show, through experiments performed on Kinetics-400, that the proposed approach can fully close the performance gap caused by face anonymization.

关键词： Visualization computer vision Face recognition conferences Computational modeling Pipelines Object recognition

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Live Demonstration: Incremental Motion Estimation for Event-based Cameras by Dispersion Minimisation

Live Demonstration: Incremental Motion Estimation for Event-...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Nunes, Urbano Miguel Demiris, Yiannis Imperial Coll London Personal Robot Lab London England

ISBN: (纸本)9781665448994

Live demonstration setup. (Left) The setup consists of a DAVIS346B event camera connected to a standard consumer laptop and undergoes some motion. (Right) The motion estimates are plotted in red and, for rotation-like motions, the angular velocities provided by the camera IMU are also plotted in blue. This plot exemplifies an event camera undergoing large rotational motions (up to ~ 1000 deg/s) around the (a) x-axis, (b) y-axis and (c) z-axis. Overall, the incremental motion estimation method follows the IMU measurements. Optionally, the resultant global optical flow can also be shown, as well as the corresponding generated events by accumulating them onto the image plane (bottom left corner).

关键词： Portable computers Motion estimation conferences Cameras Minimization pattern recognition Motion measurement

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MDMMT: Multidomain Multimodal Transformer for Video Retrieval

MDMMT: Multidomain Multimodal Transformer for Video Retrieva...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Dzabraev, Maksim Kalashnikov, Maksim Komkov, Stepan Petiushko, Aleksandr Lomonosov Moscow State Univ Moscow Russia Huawei Moscow Res Ctr Moscow Russia

ISBN: (纸本)9781665448994

We present a new state-of-the-art on the text-to-video retrieval task on MSRVTT and LSMDC benchmarks where our model outperforms all previous solutions by a large margin. Moreover, state-of-the-art results are achieved using a single model and without finetuning. This multidomain generalisation is achieved by a proper combination of different video caption datasets. We show that our practical approach for training on different datasets can improve test results of each other. Additionally, we check intersection between many popular datasets and show that MSRVTT as well as ActivityNet contains a significant overlap between the test and the training parts. More details are available at https://***/papermsucode/mdmmt.

关键词： Training computer vision conferences Benchmark testing pattern recognition Task analysis

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Event-based spacecraft landing using time-to-contact

Event-based spacecraft landing using time-to-contact

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Sikorski, Olaf Izzo, Dario Meoni, Gabriele European Space Technol Ctr Adv Concepts Team Keplerlaan 1 NL-2201 AZ Noordwijk Netherlands

ISBN: (纸本)9781665448994

We study event-based sensors in the context of spacecraft guidance and control during a descent on Moon-like terrains. For this purpose, we develop a simulator reproducing the event-based camera outputs when exposed to synthetic images of a space environment. We find that it is possible to reconstruct, in this context, the divergence of optical flow vectors (and therefore the time to contact) and use it in a simple control feedback scheme during simulated descents. The results obtained are very encouraging, albeit insufficient to meet the stringent safety constraints and modelling accuracy imposed upon space missions. We thus conclude by discussing future work aimed at addressing these limitations.

关键词： Space vehicles computer vision Space missions conferences Computational modeling Sensors Safety

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