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Where are my clothes? A multi-level approach for evaluating deep instance segmentation architectures on fashion images

Where are my clothes? A multi-level approach for evaluating ...

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

作者： Jouanneau, Warren Bugeau, Aurelie Palyart, Marc Papadakis, Nicolas Vezard, Laurent Univ Bordeaux LaBRI CNRS Bordeaux INP F-33400 Talence France Lectra F-33610 Cestas France Univ Bordeaux IMB CNRS Bordeaux INPUMR 5251 F-33400 Talence France

ISBN: (纸本)9781665448994

In this paper we present an extensive evaluation of instance segmentation in the context of images containing clothes. We propose a multi level evaluation that completes the classical overlapping criteria given by IoU. In particular, we quantify both the contour and color content accuracy of the the predicted segmentation masks. We demonstrate that the proposed evaluation framework is relevant to obtain meaningful insights on models performance through experiments conducted on five state of the art instance segmentation methods.

关键词： Image segmentation computer vision Image color analysis conferences Computational modeling computer architecture pattern recognition

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Contrastive Learning for Natural Language-Based Vehicle Retrieval

Contrastive Learning for Natural Language-Based Vehicle Retr...

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

作者： Tam Minh Nguyen Quang Huu Pham Linh Bao Doan Hoang Viet Trinh Viet-Anh Nguyen Viet-Hoang Phan Sun Asterisk Inc R&D Lab AI Res Team Tokyo Japan Hanoi Univ Sci & Technol Hanoi Vietnam

ISBN: (纸本)9781665448994

AI City Challenge 2021 Task 5: The Natural Language-Based Vehicle Tracking is a Natural Language-based Vehicle Retrieval task, which requires retrieving a single-camera track using a set of three natural language descriptions of the specific targets. In this paper, we present our methods to tackle the difficulties of the provided task. Experiments with our approaches on the competitive dataset from AICity Challenge 2021 show that our techniques achieve Mean Reciprocal Rank score of 0.1701 on the public test dataset and 0.1571 on the private test dataset.

关键词： computer vision Target tracking conferences Urban areas Natural languages computer architecture pattern recognition

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Attention! Stay Focus!

Attention! Stay Focus!

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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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Multi-Class Multi-Movement Vehicle Counting Based on CenterTrack

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

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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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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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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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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...

引用

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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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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FishEye8K: A Benchmark and Dataset for Fisheye Camera Object Detection

FishEye8K: A Benchmark and Dataset for Fisheye Camera Object...

引用

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

作者： Gochoo, Munkhjargal Otgonbold, Munkh-Erdene Ganbold, Erkhembayar Hsieh, Jun-Wei Chang, Ming-Ching Chen, Ping-Yang Dorj, Byambaa Al Jassmi, Hamad Batnasan, Ganzorig Alnajjar, Fady Abduljabbar, Mohammed Lin, Fang-Pang United Arab Emirates University Department of Computer Science and Software Engineering United Arab Emirates United Arab Emirates University Emirates Center for Mobility Research United Arab Emirates National Yang Ming Chiao Tung University College of AI and Green Energy Taiwan University at Albany - State University of New York NY United States National Yang Ming Chiao Tung University Department of Computer Science Taiwan Mongolian University of Science and Technology Mongolia National Center for High-Performance Computing Taiwan

ISBN: (纸本)9798350302493

With the advance of AI, road object detection has been a prominent topic in computer vision, mostly using perspective cameras. Fisheye lens provides omnidirectional wide coverage for using fewer cameras to monitor road intersections, however with view distortions. To our knowledge, there is no existing open dataset prepared for traffic surveillance on fisheye cameras. This paper introduces an open FishEye8K benchmark dataset for road object detection tasks, which comprises 157K bounding boxes across five classes (Pedestrian, Bike, Car, Bus, and Truck). In addition, we present benchmark results of State-of-The-Art (SoTA) models, including variations of YOLOv5, YOLOR, YOLO7, and YOLOv8. The dataset comprises 8,000 images recorded in 22 videos using 18 fisheye cameras for traffic monitoring in Hsinchu, Taiwan, at resolutions of 1080×1080 and 1280×1280. The data annotation and validation process were arduous and time-consuming, due to the ultra-wide panoramic and hemispherical fisheye camera images with large distortion and numerous road participants, particularly people riding scooters. To avoid bias, frames from a particular camera were assigned to either the training or test sets, maintaining a ratio of about 70:30 for both the number of images and bounding boxes in each class. Experimental results show that YOLOv8 and YOLOR outperform on input sizes 640×640 and 1280×1280, respectively. The dataset will be available on the GitHub link with PASCAL VOC, MS COCO, and YOLO annotation formats. The FishEye8K benchmark will provide significant contributions to the fisheye video analytics and smart city applications. © 2023 IEEE.

关键词： Object detection

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