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Neural Architecture Search of Deep Priors: Towards Continual Learning without Catastrophic Interference

Neural Architecture Search of Deep Priors: Towards Continual...

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

作者： Mundt, Martin Pliushch, Iuliia Ramesh, Visvanathan Goethe Univ Frankfurt Germany

ISBN: (纸本)9781665448994

In this paper we analyze the classification performance of neural network structures without parametric inference. Making use of neural architecture search, we empirically demonstrate that it is possible to find random weight architectures, a deep prior, that enables a linear classification to perform on par with fully trained deep counterparts. Through ablation experiments, we exclude the possibility of winning a weight initialization lottery and confirm that suitable deep priors do not require additional inference. In an extension to continual learning, we investigate the possibility of catastrophic interference free incremental learning. Under the assumption of classes originating from the same data distribution, a deep prior found on only a subset of classes is shown to allow discrimination of further classes through training of a simple linear classifier.

关键词： Training computer vision conferences Neural networks computer architecture Interference pattern recognition

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NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results

NTIRE 2022 Challenge on Super-Resolution and Quality Enhance...

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

作者： Yang, Ren Timofte, Radu Zheng, Meisong Xing, Qunliang Qiao, Minglang Xu, Mai Jiang, Lai Liu, Huaida Chen, Ying Ben, Youcheng Zhou, Xiao Fu, Chen Cheng, Pei Yu, Gang Li, Junyi Wu, Renlong Zhang, Zhilu Shang, Wei Lv, Zhengyao Chen, Yunjin Zhou, Mingcai Ren, Dongwei Zhang, Kai Zuo, Wangmeng Ostyakov, Pavel Dmitry, Vyal Soltanayev, Shakarim Sergey, Chervontsev Magauiya, Zhussip Zou, Xueyi Yan, Youliang Michelini, Pablo Navarrete Lu, Yunhua Zhang, Diankai Liu, Shaoli Gao, Si Wu, Biao Zheng, Chengjian Zhang, Xiaofeng Lu, Kaidi Wang, Ning Thuong Nguyen Canh Bach, Thong Wang, Qing Sun, Xiaopeng Ma, Haoyu Zhao, Shijie Li, Junlin Xie, Liangbin Shi, Shuwei Yang, Yujiu Wang, Xintao Gu, Jinjin Dong, Chao Shi, Xiaodi Nian, Chunmei Jiang, Dong Lin, Jucai Xie, Zhihuai Ye, Mao Luo, Dengyan Peng, Liuhan Chen, Shengjie Liu, Xin Wang, Qian Liang, Boyang Dong, Hang Huang, Yuhao Chen, Kai Guo, Xingbei Sun, Yujing Wu, Huilei Wei, Pengxu Huang, Yulin Chen, Junying Lee, Ik Hyun Khowaja, Sunder Ali Yoon, Jiseok Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Julius Maximilian Univ Wurzburg Wurzburg Germany Alibaba Grp Dept Tao Technol Beijing Peoples R China Beihang Univ Beijing Peoples R China Tencent Shenzhen Peoples R China Harbin Inst Technol Harbin Peoples R China Huawei Noahs Ark Lab Hong Kong Peoples R China BOE Technol Grp Co Ltd Beijing Peoples R China ZTE Audio & Video Technol Platform Dept Nanjing Peoples R China Osaka Univ Osaka Japan ByteDance Shenzhen Peoples R China Shanghai AI Lab Shanghai Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol SIAT Shenzhen Peoples R China Hangzhou Univ Elect Sci & Technol Hangzhou Peoples R China Univ Elect Sci & Technol China Chengdu Peoples R China Xinjiang Univ Urumqi Xinjiang Peoples R China China Mobile Res Inst Beijing Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China Peking Univ Beijing Peoples R China City Univ Hong Kong Hong Kong Peoples R China South China Univ Technol Guangzhou Peoples R China Tech Univ Korea Siheung Si South Korea Univ Sindh Sindh Pakistan IKLAB New York NY USA

ISBN: (纸本)9781665487399

This paper reviews the NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video. In this challenge, we proposed the LDV 2.0 dataset, which includes the LDV dataset (240 videos) and 95 additional videos. This challenge includes three tracks. Track 1 aims at enhancing the videos compressed by HEVC at a fixed QP. Track 2 and Track 3 target both the superresolution and quality enhancement of HEVC compressed video. They require x2 and x4 super-resolution, respectively. The three tracks totally attract more than 600 registrations. In the test phase, 8 teams, 8 teams and 12 teams submitted the final results to Tracks 1, 2 and 3, respectively. The proposed methods and solutions gauge the state-of-the-art of super-resolution and quality enhancement of compressed video. The proposed LDV 2.0 dataset is available at https:// ***/RenYanghome/LDV_dataset. The homepage of this challenge (including open-sourced codes) is at https://***/RenYang-home/NTIRE22_VEnh_SR.

关键词： computer vision Target tracking Codes conferences Superresolution pattern recognition

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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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Reconsidering CO2 emissions from computer vision

Reconsidering CO2 emissions from Computer Vision

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

作者： Fu, Andre Hosseini, Mahdi S. Plataniotis, Konstantinos N. Univ Toronto Toronto ON Canada Univ New Brunswick Fredericton NB Canada

ISBN: (纸本)9781665448994

Climate change is a pressing issue that is currently affecting and will affect every part of our lives. It's becoming incredibly vital we, as a society, address the climate crisis as a universal effort, including those in the computer vision (CV) community. In this work, we analyze the total cost of CO2 emissions by breaking it into (1) the architecture creation cost and (2) the life-time evaluation cost. We show that over time, these costs are non-negligible and are having a direct impact on our future. Importantly, we conduct an ethical analysis of how the CV-community is unintentionally overlooking its own ethical AI principles by emitting this level of CO2. To address these concerns, we propose adding "enforcement" as a pillar of ethical AI and provide some recommendations for how architecture designers and broader CV community can curb the climate crisis.

关键词： computer vision Climate change conferences computer architecture Pressing pattern recognition Artificial intelligence

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Towards Domain-Specific Explainable AI: Model Interpretation of a Skin Image Classifier using a Human Approach

Towards Domain-Specific Explainable AI: Model Interpretation...

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

作者： Stieler, Fabian Rabe, Fabian Bauer, Bernhard Univ Augsburg Augsburg Germany

ISBN: (纸本)9781665448994

Machine Learning models have started to outperform medical experts in some classification tasks. Meanwhile, the question of how these classifiers produce certain results is attracting increasing research attention. Current interpretation methods provide a good starting point in investigating such questions, but they still massively lack the relation to the problem domain. In this work, we present how explanations of an AI system for skin image analysis can be made more domain-specific. We apply the synthesis of Local Interpretable Model-agnostic Explanations (LIME) with the ABCD-rule, a diagnostic approach of dermatologists, and present the results using a Deep Neural Network (DNN) based skin image classifier.

关键词： Deep learning computer vision Image analysis Computational modeling conferences Skin pattern recognition

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Robust and Online Vehicle Counting at Crowded Intersections

Robust and Online Vehicle Counting at Crowded Intersections

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

作者： Lu, Jincheng Xia, Meng Gao, Xu Yang, Xipeng Tao, Tianran Meng, Hao Zhang, Wei Tan, Xiao Shi, Yifeng Li, Guanbin Ding, Errui

ISBN: (纸本)9781665448994

In this paper, we propose an online movement-specific vehicle counting system to realize robust traffic flow analysis at crowded intersections. Our proposed framework adopts PP-YOLO as the vehicle detector and adapts the Deep-Sort algorithm to perform multi-object tracking. In order to realize online and robust vehicle counting, we further adopt a shape-based movement assignment strategy to differentiate movements and carefully designed spatial constraints to effectively reduce false-positive counts. Our proposed framework achieves the overall S1-score of 0.9467, ranking the first in the AICITY2021-track1 challenge.

关键词： computer vision Tracking conferences Detectors pattern recognition

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Revisiting The Evaluation of Class Activation Mapping for Explainability: A Novel Metric and Experimental Analysis

Revisiting The Evaluation of Class Activation Mapping for Ex...

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

作者： Poppi, Samuele Cornia, Marcella Baraldi, Lorenzo Cucchiara, Rita Univ Modena & Reggio Emilia Modena Italy

ISBN: (纸本)9781665448994

As the request for deep learning solutions increases, the need for explainability is even more fundamental. In this setting, particular attention has been given to visualization techniques, that try to attribute the right relevance to each input pixel with respect to the output of the network. In this paper, we focus on Class Activation Mapping (CAM) approaches, which provide an effective visualization by taking weighted averages of the activation maps. To enhance the evaluation and the reproducibility of such approaches, we propose a novel set of metrics to quantify explanation maps, which show better effectiveness and simplify comparisons between approaches. To evaluate the appropriateness of the proposal, we compare different CAM-based visualization methods on the entire ImageNet validation set, fostering proper comparisons and reproducibility.

关键词： Deep learning Visualization computer vision Protocols conferences Reproducibility of results pattern recognition

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Scalable and Explainable Outfit Generation

Scalable and Explainable Outfit Generation

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

作者： Lorbert, Alexander Neiman, David Poznanski, Arik Oks, Eduard Davis, Larry Amazon Seattle WA 98109 USA

ISBN: (纸本)9781665448994

We present an end-to-end system for learning outfit recommendations. The core problem we address is how a customer can receive clothing/accessory recommendations based on a current outfit and what type of item the customer wishes to add to the outfit. Using a repository of coherent and stylish outfits, we leverage self-attention to learn a mapping from the current outfit and the customer-requested category to a visual descriptor output. This output is then fed into nearest-neighbor-based visual search, which, during training, is learned via triplet loss and mini-batch retrievals. At inference time, we use a beam search with a desired outfit composition to generate outfits at scale. Moreover, the attention networks provide a diagnostic look into the recommendation process, serving as a fashion-based sanity check.

关键词： Training Visualization computer vision conferences pattern recognition

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Bush Detection for vision-based UGV Guidance in Blueberry Orchards: Data Set and Methods

Bush Detection for Vision-based UGV Guidance in Blueberry Or...

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2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Filipović, Vladan Stefanović, Dimitrije Pajević, Nina Grbović, Željana Djuric, Nemanja Panić, Marko BioSense Institute Novi Sad Serbia

ISBN: (纸本)9798350302493

Object detection has reached strong performance in the last decade, having seen its usage spreading to various application areas, such as medicine, transportation, sports, and others. However, one of the more underutilized areas where advanced detection methods have yet to fully fulfill their promise is in the area of agriculture, where a strong potential exists for applying learned models to achieve practical, real-world impact affecting a large number of people. In this work, we focus on this application area and consider the problem of orchard guidance for ground robots, focusing on obstacle and plant detection from RGB camera images. First, we present an overview of public data sets used to train models to detect relevant objects from camera images and other sensor inputs. Then we introduce a novel data set collected in blueberry orchards that contains camera images in various conditions and provides blueberry bushes as targets for detection. The introduced data set provides the research community with a novel task of blueberry bush detection, which was not commonly considered thus far due to the lack of relevant data sets. We describe a detailed analysis of the data set, and finally provide an experimental study with several state-of-the-art deep object detection models, that set a baseline for the performance on this novel data set. The data set is made available online, enriching the variability of the existing tasks in the field and supporting further development of smart agriculture applications. © 2023 ieee.

关键词： Object detection

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Training Rare Object Detection in Satellite Imagery with Synthetic GAN Images

Training Rare Object Detection in Satellite Imagery with Syn...

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

作者： Martinson, Eric Furlong, Bridget Gillies, Andy Soar Technol 3600 Green StSte 600 Ann Arbor MI 48105 USA

ISBN: (纸本)9781665448994

When creating a new labeled dataset, human analysts or data reductionists must review and annotate large numbers of images. This process is time consuming and a barrier to the deployment of new computer vision solutions, particularly for rarely occurring objects. To reduce the number of images requiring human attention, we evaluate the utility of images created from 3D models refined with a generative adversarial network to select confidence thresholds that significantly reduce false alarms rates. The resulting approach has been demonstrated to cut the number of images needing to be reviewed by 50% while preserving a 95% recall rate, with only 6 labeled examples of the target.

关键词： Training Solid modeling computer vision Analytical models Three-dimensional displays Satellites Generative adversarial networks

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