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Content-Adaptive Downsampling in Convolutional Neural Networks

Content-Adaptive Downsampling in Convolutional Neural Networ...

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

作者： Hesse, Robin Schaub-Meyer, Simone Roth, Stefan TU Darmstadt Department of Computer Science Germany Hessian.AI

ISBN: (纸本)9798350302493

Many convolutional neural networks (CNNs) rely on progressive downsampling of their feature maps to increase the network's receptive field and decrease computational cost. However, this comes at the price of losing granularity in the feature maps, limiting the ability to correctly understand images or recover fine detail in dense prediction tasks. To address this, common practice is to replace the last few downsampling operations in a CNN with dilated convolutions, allowing to retain the feature map resolution without reducing the receptive field, albeit increasing the computational cost. This allows to trade off predictive performance against cost, depending on the output feature resolution. By either regularly downsampling or not downsampling the entire feature map, existing work implicitly treats all regions of the input image and subsequent feature maps as equally important, which generally does not hold. We propose an adaptive downsampling scheme that generalizes the above idea by allowing to process informative regions at a higher resolution than less informative ones. In a variety of experiments, we demonstrate the versatility of our adaptive downsampling strategy and empirically show that it improves the cost-accuracy trade-off of various established CNNs. © 2023 IEEE.

关键词： Convolution

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NTIRE 2022 Image Inpainting Challenge: Report

NTIRE 2022 Image Inpainting Challenge: Report

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

作者： Romero, Andres Castillo, Angela Abril-Nova, Jose Timofte, Radu Das, Ritwik Hira, Sanchit Pan, Zhihong Zhang, Min Li, Baopu He, Dongliang Lin, Tianwei Li, Fu Wu, Chengyue Liu, Xianming Wang, Xinying Yu, Yi Yang, Jie Li, Rengang Zhao, Yaqian Guo, Zhenhua Fan, Baoyu Li, Xiaochuan Zhang, Runze Lu, Zeyu Huang, Junqin Wu, Gang Jiang, Junjun Cai, Jiayin Li, Changlin Tao, Xin Tai, Yu-Wing Zhou, Xiaoqiang Huang, Huaibo Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Univ Los Andes Ctr Res & Format Artificial Intelligence Bogota Colombia Univ Wurzburg Wurzburg Germany Baidu Res Sunnyvale CA USA Univ Southern Calif Los Angeles CA 90089 USA Baidu Inc Dept Comp Vis Technol VIS Sunnyvale CA USA Harbin Inst Technol Harbin Peoples R China Beihang Univ Beijing Peoples R China MGTV Changchun Peoples R China Inspur Elect Informat Ind Co Ltd Jinan Peoples R China State Key Lab High End Server & Storage Technol Jinan Peoples R China Univ Sci & Technol China Hefei Peoples R China

ISBN: (纸本)9781665487399

Image Inpainting has recently become an important research problem due to the rise of generative image synthesis models. While many solutions have been proposed for this problem, it is challenging to establish a testbed due to the different possible types of inpainting masks e.g., completion mask, expand mask, thick brushes mask, etc. Most inpainting solutions shine on object removal or texture synthesis, while semantic generation is still difficult to achieve. To address these issues, we introduce the first general Image Inpainting Challenge. The target is to develop solutions that can achieve a robust performance across different and challenging masks while generating compelling semantic images. The proposed challenge consists of two tracks: unsupervised image inpainting and semantically-guided image inpainting. For Track 1, the participants were provided with four datasets: FFHQ, Places, ImageNet, and WikiArt, and trained their models to perform a mask-agnostic image inpainting solution. For Track 2, FFHQ and Places only. This report gathers the description and discussion of all solutions that participated in the final stage of the challenge.

关键词： computer vision Target tracking Brushes Image synthesis conferences Computational modeling Semantics

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Underwater Light Field Retention : Neural Rendering for Underwater Imaging

Underwater Light Field Retention : Neural Rendering for Unde...

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

作者： Ye, Tian Chen, Sixiang Liu, Yun Ye, Yi Chen, Erkang Li, Yuche Jimei Univ Sch Ocean Informat Engn Xiamen Peoples R China Southwest Univ Coll Artificial Intelligence Chongqing Peoples R China China Univ Petr Coll Geosci Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Underwater Image Rendering aims to generate a true-to-life underwater image from a given clean one, which could be applied to various practical applications such as underwater image enhancement, camera filter, and virtual gaming. We explore two less-touched but challenging problems in underwater image rendering, namely, i) how to render diverse underwater scenes by a single neural network? ii) how to adaptively learn the underwater light fields from natural exemplars, i,e., realistic underwater images? To this end, we propose a neural rendering method for underwater imaging, dubbed UWNR (Underwater Neural Rendering). Specifically, UWNR is a data-driven neural network that implicitly learns the natural degenerated model from authentic underwater images, avoiding introducing erroneous biases by hand-craft imaging models. Compared with existing underwater image generation methods, UWNR utilizes the natural light field to simulate the main characteristics of the underwater scene. Thus, it is able to synthesize a wide variety of underwater images from one clean image with various realistic underwater images. Extensive experiments demonstrate that our approach achieves better visual effects and quantitative metrics over previous methods. Moreover, we adopt UWNR to build an open Large Neural Rendering Underwater Dataset containing various types of water quality, dubbed LNRUD. The source code and LNRUD are available at https://***/Ephemeral182/UWNR. [GRAPHICS] .

关键词： Measurement Image synthesis conferences Neural networks Water quality Rendering (computer graphics) Visual effects

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NTIRE 2022 Spectral Demosaicing Challenge and Data Set

NTIRE 2022 Spectral Demosaicing Challenge and Data Set

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

作者： Arad, Boaz Timofte, Radu Yahel, Rony Morag, Nimrod Bernat, Amir Wu, Yaqi Wu, Xun Fan, Zhihao Xia, Chenjie Zhang, Feng Liu, Shuai Li, Yongqiang Feng, Chaoyu Lei, Lei Zhang, Mingwei Feng, Kai Zhang, Xun Yao, Jiaxin Zhao, Yongqiang Ma, Suina He, Fan Dong, Yangyang Yu, Shufang Qiu, Difa Liu, Jinhui Bi, Mengzhao Song, Beibei Sun, WenFang Zheng, Jiesi Zhao, Bowen Cao, Yanpeng Yang, Jiangxin Cao, Yanlong Kong, Xiangyu Yu, Jingbo Xue, Yuanyang Xie, Zheng Oddity Tech Ltd New York NY USA Voyage81 Ltd New York NY USA Univ Wurzburg Ctr Artificial Intelligence & Data Sci Wurzburg Germany Swiss Fed Inst Technol Computat Vision Lab Zurich Switzerland Acad Coll Tel Aviv Yaffo Tel Aviv Israel Tel Aviv Univ Tel Aviv Israel Harbin Inst Technol Harbin 150001 Peoples R China Tsinghua Univ Beijing 100084 Peoples R China Univ Shanghai Sci & Technol Shanghai 200093 Peoples R China Zhejiang Univ Hangzhou 310027 Peoples R China Xiaomi Inc Beijing Peoples R China Northwestern Polytech Univ Xian Peoples R China Changan Univ Xian Peoples R China Xidian Univ Xian Peoples R China Zhejiang Univ Sch Mech Engn State Key Lab Fluid Power & Mech Syst Hangzhou 310027 Peoples R China Zhejiang Univ Sch Mech Engn Key Lab Adv Mfg Technol Zhejiang Prov Hangzhou 310027 Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

This paper presents the first challenge on demosaicing of natural spectral images for snapshot hyperspectral imaging systems (HIS) which utilize a multi-spectral filer array (MSFA), i.e., the recovery of whole-scene hyperspectral information from spatially sub-sampled hyperspectral information. This challenge expands the "ARAD_1K" data set to a first-of-its-kind large-scale data set for multi-spectral filter array demosaicing of natural scenes containing 1,000 images. Challenge participants were required to recover hyperspectral information from synthetically generated MSFA images simulating capture by a known calibrated snapshot mosaic hyperspectral camera. The challenge was attended by 157 teams, with 29 teams competing in the final testing phase, 7 of which provided detailed descriptions of their methodology which are included in this report. The performance of these submissions is reviewed and provided here as a gauge for the current state-of-the-art in multi-spectral filter array demosaicing of natural images.

关键词： Performance evaluation Training Runtime Neural networks Cameras Real-time systems pattern recognition

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A Data-Centric Solution to NonHomogeneous Dehazing via vision Transformer

A Data-Centric Solution to NonHomogeneous Dehazing via Visio...

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

作者： Liu, Yangyi Liu, Huan Li, Liangyan Wu, Zijun Chen, Jun McMaster University Hamilton Canada China Telecom Research Institute Shanghai China

ISBN: (纸本)9798350302493

Recent years have witnessed an increased interest in image dehazing. Many deep learning methods have been proposed to tackle this challenge, and have made significant accomplishments dealing with homogeneous haze. However, these solutions cannot maintain comparable performance when they are applied to images with non-homogeneous haze, e.g., NH-HAZE23 dataset introduced by NTIRE challenge. One of the reasons for such failures is that non-homogeneous haze does not obey one of the assumptions that is required for modeling homogeneous haze. In addition, a large number of pairs of non-homogeneous hazy image and the clean counterpart is required using traditional end-to-end training approaches, while NH-HAZE23 dataset is of limited quantities. Although it is possible to augment the NH-HAZE23 dataset by leveraging other non-homogeneous dehazing datasets, we observe that it is necessary to design a proper data-preprocessing technique that reduces the distribution gaps between the target dataset and the augmented one. This finding indeed aligns with the essence of data-centric AI. With a novel network architecture and a principled data-preprocessing approach that systematically enhances data quality, we present an innovative dehazing method. Specifically, we apply RGB-channel-wise transformations on the augmented datasets, and incorporate the state-of-the-art transformers as the backbone in the two-branch framework. We conduct extensive experiments and ablation studies to demonstrate the effectiveness of our proposed method. The source code is available at https://***/yangyiliu21/ntire2023-ITBdehaze. © 2023 IEEE.

关键词： Network architecture

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BeCAPTCHA-Type: Biometric Keystroke Data Generation for Improved Bot Detection

BeCAPTCHA-Type: Biometric Keystroke Data Generation for Impr...

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

作者： Dealcala, Daniel Morales, Aythami Tolosana, Ruben Acien, Alejandro Fierrez, Julian Hernandez, Santiago Ferrer, Miguel A. Diaz, Moises Universidad Autonoma de Madrid Biometrics and Data Pattern Analytics Lab Spain University Las Palmas Gran Canaria Spain

ISBN: (纸本)9798350302493

This work proposes a data driven learning model for the synthesis of keystroke biometric data. The proposed method is compared with two statistical approaches based on Universal and User-dependent models. These approaches are validated on a bot detection task, using the keystroke synthetic data to improve the training process of keystroke-based bot detection systems. Our experimental framework considers a dataset with 136 million keystroke events from 168 thousand subjects. We have analyzed the performance of the three synthesis approaches through qualitative and quantitative experiments. Different bot detectors are considered based on several supervised classifiers (Support Vector Machine, Random Forest, Gaussian Naive Bayes and a Long Short-Term Memory network) and a learning framework including human and synthetic samples. The experiments demonstrate the realism of the synthetic samples. The classification results suggest that in scenarios with large labeled data, these synthetic samples can be detected with high accuracy. However, if the proposed synthetic data is nor properly modelled using massive data by bot detectors, then that data will be very difficult to detect even for the most sophisticate bot detectors. Furthermore, these results show the great potential of the presented models for improving the training of bot detection technology. © 2023 IEEE.

关键词： Biometrics

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CrisisHateMM: Multimodal Analysis of Directed and Undirected Hate Speech in Text-Embedded Images from Russia-Ukraine Conflict

CrisisHateMM: Multimodal Analysis of Directed and Undirected...

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

作者： Bhandari, Aashish Shah, Siddhant B. Thapa, Surendrabikram Naseem, Usman Nasim, Mehwish Delhi Technological University Department of CSE India Virginia Tech Department of Computer Science United States School of Computer Science The University of Sydney Australia School of Physics Mathematics and Computing The University of Western Australia Australia College of Science and Engineering Flinders University Australia

ISBN: (纸本)9798350302493

Text-embedded images are frequently used on social media to convey opinions and emotions, but they can also be a medium for disseminating hate speech, propaganda, and extremist ideologies. During the Russia-Ukraine war, both sides used text-embedded images extensively to spread propaganda and hate speech. To aid in moderating such content, this paper introduces CrisisHateMM, a novel multimodal dataset of over 4,700 text-embedded images from the Russia-Ukraine conflict, annotated for hate and non-hate speech. The hate speech is annotated for directed and undirected hate speech, with directed hate speech further annotated for individual, community, and organizational targets. We benchmark the dataset using unimodal and multimodal algorithms, providing insights into the effectiveness of different approaches for detecting hate speech in text-embedded images. Our results show that multimodal approaches outperform unimodal approaches in detecting hate speech, highlighting the importance of combining visual and textual features. This work provides a valuable resource for researchers and practitioners in automated content moderation and social media analysis. The CrisisHateMM dataset and codes are made publicly available at https://***/aabhandari/CrisisHateMM. © 2023 IEEE.

关键词： Modal analysis

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Exposing and Mitigating Spurious Correlations for Cross-Modal Retrieval

Exposing and Mitigating Spurious Correlations for Cross-Moda...

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

作者： Kim, Jae Myung Sophia Koepke, A. Schmid, Cordelia Akata, Zeynep University of Tübingen Germany PSL Research University Inria Ecole Normale Supérieure CNRS France MPI for Intelligent Systems Germany

ISBN: (纸本)9798350302493

Cross-modal retrieval methods are the preferred tool to search databases for the text that best matches a query image and vice versa. However, image-text retrieval models commonly learn to memorize spurious correlations in the training data, such as frequent object co-occurrence, instead of looking at the actual underlying reasons for the prediction in the image. For image-text retrieval, this manifests in retrieved sentences that mention objects that are not present in the query image. In this work, we introduce ODmAP@k, an object decorrelation metric that measures a model's robustness to spurious correlations in the training data. We use automatic image and text manipulations to control the presence of such object correlations in designated test data. Additionally, our data synthesis technique is used to tackle model biases due to spurious correlations of semantically unrelated objects in the training data. We apply our proposed pipeline, which involves the finetuning of image-text retrieval frameworks on carefully designed synthetic data, to three state-of-the-art models for image-text retrieval. This results in significant improvements for all three models, both in terms of the standard retrieval performance and in terms of our object decorrelation metric. The code is available at https://***/ExplainableML/Spurious-CM-Retrieval. © 2023 IEEE.

关键词： computer vision

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NTIRE 2022 Challenge on Night Photography Rendering

NTIRE 2022 Challenge on Night Photography Rendering

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

作者： Ershov, Egor Savchik, Alex Shepelev, Denis Banic, Nikola Brown, Michael S. Timofte, Radu Koscevic, Karlo Freeman, Michael Tesalin, Vasily Bocharov, Dmitry Semenkov, Illya Subasic, Marko Loncaric, Sven Terekhin, Arseniy Liu, Shuai Feng, Chaoyu Wang, Hao Zhu, Ran Li, Yongqiang Lei, Lei Li, Zhihao Yi, Si Han, Ling-Hao Wu, Ruiqi Jin, Xin Guo, Chunle Kinli, Furkan Mentes, Sami Ozcan, Baris Kirac, Furkan Zini, Simone Rota, Claudio Buzzelli, Marco Bianco, Simone Schettini, Raimondo Li, Wei Ma, Yipeng Wang, Tao Xu, Ruikang Song, Fenglong Chen, Wei-Ting Yang, Hao-Hsiang Huang, Zhi-Kai Chang, Hua-En Kuo, Sy-Yen Liang, Zhexin Zhou, Shangchen Feng, Ruicheng Li, Chongyi Chen, Xiangyu Song, Binbin Zhang, Shile Liu, Lin Wang, Zhendong Ryu, Dohoon Bae, Hyokyoung Kwon, Taesung Desai, Chaitra Akalwadi, Nikhil Joshi, Amogh Mandi, Chinmayee Malagi, Sampada Uppin, Akash Reddy, Sai Sudheer Tabib, Ramesh Ashok Patil, Ujwala Mudenagudi, Uma Kharkevich Inst Inst Informat Transmiss Problems Moscow Russia Gideon Bros Osijek Croatia York Univ Toronto ON Canada Swiss Fed Inst Technol Zurich Zurich Switzerland Univ Wurzburg Wurzburg Germany Univ Zagreb Fac Elect Engn & Comp Zagreb Croatia Michael Freeman Photog London England Xiaomi Inc Beijing Peoples R China Nanjing Univ Nanjing Peoples R China Nankai Univ Tianjin Peoples R China Wuhan Univ Technol Wuhan Peoples R China Ozyegin Univ Istanbul Turkey Univ Milano Bicocca Milan Italy Huawei Noahs Ark Lab Montreal PQ Canada Natl Taiwan Univ Grad Inst Elect Engn New Taipei Taiwan Natl Taiwan Univ Dept Elect Engn New Taipei Taiwan Nanyang Technol Univ Singapore Singapore Univ Macau Taipa Macao Peoples R China Univ Sci & Technol China Hefei Peoples R China Korea Adv Inst Sci & Technol Daejeon South Korea KLE Technol Univ Ctr Excellence Visual Intelligence CEVI Hubballi Karnataka India

ISBN: (纸本)9781665487399

This paper reviews the NTIRE 2022 challenge on night photography rendering. The challenge solicited solutions that processed RAW camera images captured in night scenes to produce a photo-finished output image encoded in the standard RGB (sRGB) space. Given the subjective nature of this task, the proposed solutions were evaluated based on the mean opinions of viewers asked to judge the visual appearance of the results. Michael Freeman, a world-renowned photographer, further ranked the solutions with the highest mean opinion scores. A total of 13 teams competed in the final phase of the challenge. The proposed methods provided by the participating teams represent state-of-the-art performance in nighttime photography. Results from the various teams can be found here: https://***/

关键词： Photography Visualization computer vision conferences Rendering (computer graphics) Cameras pattern recognition

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An Interpretable Deep Learning Approach for Morphological Script Type Analysis

An Interpretable Deep Learning Approach for Morphological Sc...

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18th International conference on Document Analysis and recognition (ICDAR)

作者： Vlachou-Efstathiou, Malamatenia Siglidis, Ioannis Stutzmann, Dominique Aubry, Mathieu Inst Rech & Hist Textes Paris Ile De France France Univ Gustave Eiffel CNRS Ecole Ponts LIGM Marne La Vallee France

ISBN: (纸本)9783031706417;9783031706424

Defining script types and establishing classification criteria for medieval handwriting is a central aspect of palaeographical analysis. However, existing typologies often encounter methodological challenges, such as descriptive limitations and subjective criteria. We propose an interpretable deep learning-based approach to morphological script type analysis, which enables systematic and objective analysis and contributes to bridging the gap between qualitative observations and quantitative measurements. More precisely, we adapt a deep instance segmentation method to learn comparable character prototypes, representative of letter morphology, and provide qualitative and quantitative tools for their comparison and analysis. We demonstrate our approach by applying it to the Textualis Formata script type and its two subtypes formalized by A. Derolez: Northern and Southern Textualis.

关键词： Latin Palaeography computer vision Palaeographical Analysis Character Prototypes Textualis Formata

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