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Detecting Low-Rank Regions in Omnidirectional Images

Detecting Low-Rank Regions in Omnidirectional Images

引用

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

作者： Kato, Zoltan Nagy, Gabor Humenberger, Martin Csurka, Gabriela Univ Szeged Inst Informat Szeged Hungary J Selye Univ Komarno Slovakia NAVER LABS Europe Meylan France

ISBN: (纸本)9781665448994

Planar low-rank regions commonly found in man-made environments, can be used to estimate a rectifying homography that provides valuable information about the camera and the 3D plane they observe. Methods to recover such a homography exist, but detection of low-rank regions is largely unsolved, especially for omnidirectional cameras where significant distortions make the problem even more challenging. In this paper we address this problem as follows. First we propose a method to generate a low-rank probability map on an omnidirectional image and use it to build a training set in a self-supervised manner to train deep models to predict low-rank likelihood maps for omnidirectional images. Second, we propose to adapt regular CNN operators to equirectangular images and to combine them seamlessly into a network where each layer preserves the properties of the equirectangular representation. Finally, on the new KITTI360 dataset, we show that the rectifying homography of detected low-rank regions in such predicted maps allows to factorize out the camera-plane pose up to certain ambiguities that can be easily overcome.

关键词： Training computer vision Adaptation models Three-dimensional displays conferences Predictive models Distortion

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Finding AI-Generated Faces in the Wild

Finding AI-Generated Faces in the Wild

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Gonzalo J. Aniano Porcile Jack Gindi Shivansh Mundra James R. Verbus Hany Farid LinkedIn Sunnyvale CA USA University of California Berkeley Berkeley CA USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

AI-based image generation has continued to rapidly improve, producing increasingly more realistic images with fewer obvious visual flaws. AI-generated images are being used to create fake online profiles which in turn are being used for spam, fraud, and disinformation campaigns. As the general problem of detecting any type of manipulated or synthesized content is receiving increasing attention, here we focus on a more narrow task of distinguishing a real face from an AI-generated face. This is particularly applicable when tackling inauthentic online accounts with a fake user profile photo. We show that by focusing on only faces, a more resilient and general-purpose artifact can be detected that allows for the detection of AI-generated faces from a variety of GAN- and diffusion-based synthesis engines, and across image resolutions (as low as 128 × 128 pixels) and qualities.

关键词： Visualization computer vision Image resolution Image synthesis Face recognition conferences Focusing

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Grounded, Controllable and Debiased Image Completion with Lexical Semantics

Grounded, Controllable and Debiased Image Completion with Le...

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

作者： Zhang, Shengyu Jiang, Tan Huang, Qinghao Tan, Ziqi Kuang, Kun Zhao, Zhou Tang, Siliang Yu, Jin Yang, Hongxia Yang, Yi Wu, Fei Zhejiang Univ Hangzhou Peoples R China Alibaba Grp Hangzhou Peoples R China

ISBN: (纸本)9781665448994

In this paper, we present an approach, namely Lexical Semantic Image Completion (LSIC)(1), that may have potential applications in art, design, and heritage conservation, among several others. Existing image completion procedure is highly subjective by considering only visual context, which may trigger unpredictable results which are plausible but not faithful to a grounded knowledge. To permit both grounded and controllable completion process, we advocate generating results faithful to both visual and lexical semantic context, i.e., the description of leaving holes or blank regions in the image (e.g., hole description). One major challenge for LSIC comes from modeling and aligning the structure of visual-semantic context and translating across different modalities. We devise multi-grained reasoning blocks to address this challenge. Another challenge relates to the unimodal biases, which occurs when the model generates plausible results without using the textual description. We devise an unsupervised unpaired-creation learning path that explicitly performs counterfactual thinking, i.e., what the complete image would be if given an unpaired text description to the incomplete image. A cycle consistency loss is devised to guarantee counterfactual faithfulness. We conduct extensive quantitative and qualitative experiments that reveal the strengths of LSIC in being grounded, controllable, and debiased.

关键词： Measurement Visualization computer vision conferences Semantics Process control Cognition

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Temporally-Aware Feature Pooling for Action Spotting in Soccer Broadcasts

Temporally-Aware Feature Pooling for Action Spotting in Socc...

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

作者： Giancola, Silvio Ghanem, Bernard King Abdullah Univ Sci & Technol Thuwal Saudi Arabia

ISBN: (纸本)9781665448994

Toward the goal of automatic production for sports broadcasts, a paramount task consists in understanding the high-level semantic information of the game in play. For instance, recognizing and localizing the main actions of the game would allow producers to adapt and automatize the broadcast production, focusing on the important details of the game and maximizing the spectator engagement. In this paper, we focus our analysis on action spotting in soccer broadcast, which consists in temporally localizing the main actions in a soccer game. To that end, we propose a novel feature pooling method based on NetVLAD, dubbed NetVLAD++, that embeds temporally-aware knowledge. Different from previous pooling methods that consider the temporal context as a single set to pool from, we split the context before and after an action occurs. We argue that considering the contextual information around the action spot as a single entity leads to a sub-optimal learning for the pooling module. With NetVLAD++, we disentangle the context from the past and future frames and learn specific vocabularies of semantics for each subsets, avoiding to blend and blur such vocabulary in time. Injecting such prior knowledge creates more informative pooling modules and more discriminative pooled features, leading into a better understanding of the actions. We train and evaluate our methodology on the recent large-scale dataset SoccerNet-v2, reaching 53:4% Average-mAP for action spotting, a +12:7% improvement w.r.t the current state-of-the-art.

关键词： Vocabulary Roads Semantics Focusing Games Production pattern recognition

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Effectively Leveraging Attributes for Visual Similarity

Effectively Leveraging Attributes for Visual Similarity

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

作者： Mishra, Samarth Zhang, Zhongping Shen, Yuan Kumar, Ranjitha Saligrama, Venkatesh Plummer, Bryan Boston Univ Boston MA 02215 USA Univ Illinois Champaign IL USA

ISBN: (纸本)9781665448994

Measuring similarity between two images often requires performing complex reasoning along different axes (e.g., color, texture, or shape). Insights into what might be important for measuring similarity can be provided by annotated attributes. Prior work tends to view these annotations as complete, resulting in them using a simplistic approach of predicting attributes on single images, which are, in turn, used to measure similarity. However, it is impractical for a dataset to fully annotate every attribute that may be important. Thus, only representing images based on these incomplete annotations may miss out on key information. To address this issue, we propose the Pairwise Attribute-informed similarity Network (PAN), which breaks similarity learning into capturing similarity conditions and relevance scores from a joint representation of two images. This enables our model to identify that two images contain the same attribute, but can have it deemed irrelevant (e.g., due to fine-grained differences between them) and ignored for measuring similarity between the two images. Notably, while prior methods of using attribute annotations are often unable to outperform prior art, PAN obtains a 4-9% improvement on compatibility prediction between clothing items on Polyvore Outfits and a 5% gain on few shot classification of images using Caltech-UCSD Birds (CUB), and over 1% boost to Recall@1 on In-Shop Clothes Retrieval.

关键词： Visualization computer vision Annotations Shape Image color analysis Shape measurement conferences

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***: Dataset for Automatic Mapping of Buildings, Woodlands, Water and Roads from Aerial Imagery

***: Dataset for Automatic Mapping of Buildings, Woodlands, ...

引用

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

作者： Boguszewski, Adrian Batorski, Dominik Ziemba-Jankowska, Natalia Dziedzic, Tomasz Zambrzycka, Anna Linux Polska Warsaw Poland Univ Warsaw Interdisciplinary Ctr Math & Computat Modelling Warsaw Poland Agcy Restructuring & Modernisat Agr Warsaw Poland

ISBN: (纸本)9781665448994

Monitoring of land cover and land use is crucial in natural resources management. Automatic visual mapping can carry enormous economic value for agriculture, forestry, or public administration. Satellite or aerial images combined with computer vision and deep learning enable precise assessment and can significantly speed up change detection. Aerial imagery usually provides images with much higher pixel resolution than satellite data allowing more detailed mapping. However, there is still a lack of aerial datasets made for the segmentation, covering rural areas with a resolution of tens centimeters per pixel, manual fine labels, and highly publicly important environmental instances like buildings, woods, water, or roads. Here we introduce *** (Land Cover from Aerial Imagery) dataset for semantic segmentation. We collected images of 216.27 km(2) rural areas across Poland, a country in Central Europe, 39.51 km(2) with resolution 50 cm per pixel and 176.76 km(2) with resolution 25 cm per pixel and manually fine annotated four following classes of objects: buildings, woodlands, water, and roads. Additionally, we report simple benchmark results, achieving 85.56% of mean intersection over union on the test set. It proves that the automatic mapping of land cover is possible with a relatively small, cost-efficient, RGB-only dataset. The dataset is publicly available at https://***/

关键词： Deep learning Image segmentation computer vision Image resolution Satellites Roads Buildings

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Boosting the Performance of Video Compression Artifact Reduction with Reference Frame Proposals and Frequency Domain Information

Boosting the Performance of Video Compression Artifact Reduc...

引用

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

作者： Xu, Yi Zhao, Minyi Liu, Jing Zhang, Xinjian Gao, Longwen Zhou, Shuigeng Sun, Huyang Fudan Univ Shanghai Key Lab Intelligent Informat Proc Shanghai Peoples R China Fudan Univ Sch Comp Sci Shanghai Peoples R China Bilibili Shanghai Peoples R China

ISBN: (纸本)9781665448994

Many deep learning based video compression artifact removal algorithms have been proposed to recover high-quality videos from low-quality compressed videos. Recently, methods were proposed to mine spatiotemporal information via utilizing multiple neighboring frames as reference frames. However, these post-processing methods take advantage of adjacent frames directly, but neglect the information of the video itself which can be exploited. In this paper, we propose an effective reference frame proposal strategy to boost the performance of the existing multi-frame approaches. Besides, we introduce a loss based on fast Fourier transformation (FFT) to further improve the effectiveness of restoration. Experimental results show that our method achieves better fidelity and perceptual performance on MFQE 2.0 dataset than the state-of-the-art methods. And our method won Track I and Track 2, and was ranked the 2nd in Track 3 of NTIRE 2021 Quality enhancement of heavily compressed videos Challenge.

关键词： Deep learning computer vision Frequency-domain analysis conferences Video compression Boosting Spatiotemporal phenomena

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Continual learning in cross-modal retrieval

Continual learning in cross-modal retrieval

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

作者： Wang, Kai Herranz, Luis van de Weijer, Joost Univ Autonoma Barcelona Comp Vis Ctr Barcelona Spain

ISBN: (纸本)9781665448994

Multimodal representations and continual learning are two areas closely related to human intelligence. The former considers the learning of shared representation spaces where information from different modalities can be compared and integrated (we focus on cross-modal retrieval between language and visual representations). The latter studies how to prevent forgetting a previously learned task when learning a new one. While humans excel in these two aspects, deep neural networks are still quite limited. In this paper, we propose a combination of both problems into a continual cross-modal retrieval setting, where we study how the catastrophic interference caused by new tasks impacts the embedding spaces and their cross-modal alignment required for effective retrieval. We propose a general framework that decouples the training, indexing and querying stages. We also identify and study different factors that may lead to forgetting, and propose tools to alleviate it. We found that the indexing stage pays an important role and that simply avoiding reindexing the database with updated embedding networks can lead to significant gains. We evaluated our methods in two image-text retrieval datasets, obtaining significant gains with respect to the fine tuning baseline.

关键词： Training Visualization Human intelligence Focusing Interference Tools pattern recognition

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NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results

NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods ...

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

作者： Perez-Pellitero, Eduardo Catley-Chandar, Sibi Shaw, Richard Leonardis, Ales Timofte, Radu Zhang, Zexin Liu, Cen Peng, Yunbo Lin, Yue Yu, Gaocheng Zhang, Jin Ma, Zhe Wang, Hongbin Chen, Xiangyu Wang, Xintao Wu, Haiwei Liu, Lin Dong, Chao Zhou, Jiantao Yan, Qingsen Zhang, Song Chen, Weiye Liu, Yuhang Zhang, Zhen Zhang, Yanning Shi, Javen Qinfeng Gong, Dong Zhu, Dan Sun, Mengdi Chen, Guannan Hu, Yang Li, Haowei Zou, Baozhu Liu, Zhen Lin, Wenjie Jiang, Ting Jiang, Chengzhi Li, Xinpeng Han, Mingyan Fan, Haoqiang Sun, Jian Liu, Shuaicheng Marin-Vega, Juan Sloth, Michael Schneider-Kamp, Peter Rottger, Richard Li, Chunyang Bao, Long He, Gang Xu, Ziyao Xu, Li Zhan, Gen Sun, Ming Wen, Xing Li, Junlin Li, Jinjing Li, Chenghua Gang, Ruipeng Li, Fangya Liu, Chenming Feng, Shuang Lei, Fei Liu, Rui Ruan, Junxiang Dai, Tianhong Li, Wei Lu, Zhan Liu, Hengyan Huang, Peian Ren, Guangyu Luo, Yonglin Liu, Chang Tu, Qiang Ma, Sai Cao, Yizhen Tel, Steven Heyrman, Barthelemy Ginhac, Dominique Lee, Chul Kim, Gahyeon Park, Seonghyun An Gia Vien Truong Thanh Nhat Mai Yoon, Howoon Tu Vo Holston, Alexander Zaheer, Sheir Park, Chan Y. Huawei Noahs Ark Lab Hong Kong Peoples R China Univ Wurzburg Wurzburg Germany Swiss Fed Inst Technol Zurich Switzerland Netease Games AI Lab Shanghai Peoples R China AntGroup Hangzhou Peoples R China Univ Macau Macau Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China Tencent PCG Shenzhen Peoples R China Univ Sci & Technol China Hefei Peoples R China Univ Adelaide Adelaide SA Australia Xidian Univ Xian Peoples R China Northwestern Polytech Univ Xian Peoples R China Univ New South Wales Sydney NSW Australia BOE Technol Grp Co Ltd Beijing Peoples R China CZUR Technol Grp Co Ltd Liaoning Peoples R China Megvii Technol Beijing Peoples R China Univ Southern Denmark Dept Math & Comp Sci IMADA Aarhus Denmark Esoft Syst Odense Denmark Xiaomi Beijing Peoples R China ByteDance Beijing Peoples R China Kuaishou Technol Beijing Peoples R China Commun Univ China Beijing Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China NRTA Acad Broadcasting Sci Beijing Peoples R China Tetras AI Technol Hangzhou Peoples R China Imperial Coll London London England Tsinghua Univ Beijing Peoples R China Univ Edinburgh Edinburgh Midlothian Scotland Sun Yat Sen Univ Guangzhou Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China Beijing Inst Technol Beijing Peoples R China NRTA Acad Broadcasting Sciencience Beijing Peoples R China Univ Burgundy ImViA Lab Dijon France Dongguk Univ Dept Multimedia Engn Seoul South Korea Gachon Univ Seongnam South Korea KC Machine Learning Lab Seoul South Korea

ISBN: (纸本)9781665487399

This paper reviews the challenge on constrained high dynamic range (HDR) imaging that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2022. This manuscript focuses on the competition set-up, datasets, the proposed methods and their results. The challenge aims at estimating an HDR image from multiple respective low dynamic range (LDR) observations, which might suffer from under- or over-exposed regions and different sources of noise. The challenge is composed of two tracks with an emphasis on fidelity and complexity constraints: In Track 1, participants are asked to optimize objective fidelity scores while imposing a low-complexity constraint (i.e. solutions can not exceed a given number of operations). In Track 2, participants are asked to minimize the complexity of their solutions while imposing a constraint on fidelity scores (i.e. solutions are required to obtain a higher fidelity score than the prescribed baseline). Both tracks use the same data and metrics: Fidelity is measured by means of PSNR with respect to a ground-truth HDR image (computed both directly and with a canonical tonemapping operation), while complexity metrics include the number of Multiply-Accumulate (MAC) operations and runtime (in seconds).

关键词： computer vision Runtime conferences Imaging Dynamic range Particle measurements Market research

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Automated Tackle Injury Risk Assessment in Contact-Based Sports - A Rugby Union Example

Automated Tackle Injury Risk Assessment in Contact-Based Spo...

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

作者： Martin, Zubair Hendricks, Sharief Patel, Amir Univ Cape Town African Robot Unit Cape Town South Africa Univ Cape Town Div Exercise Sci Sports Med Cape Town South Africa

ISBN: (纸本)9781665448994

Video analysis in tackle-collision based sports is highly subjective and exposed to bias, which is inherent in human observation, especially under time constraints. This limitation of match analysis in tackle-collision based sports can be seen as an opportunity for computer vision applications. Objectively tracking, detecting and recognising an athlete's movements and actions during match play from a distance using video, along with our improved understanding of injury aetiology and skill execution will enhance our understanding how injury occurs, assist match day injury management, reduce referee subjectivity. In this paper, we present a system of objectively evaluating in-game tackle risk in rugby union matches. First, a ball detection model is trained using the You Only Look Once (YOLO) framework, these detections are then tracked by a Kalman Filter (KF). Following this, a separate YOLO model is used to detect persons/players within a tackle segment and then the ball-carrier and tackler are identified. Subsequently, we utilize OpenPose to determine the pose of ball-carrier and tackle, the relative pose of these is then used to evaluate the risk of the tackle. We tested the system on a diverse collection of rugby tackles and achieved an evaluation accuracy of 62.50%. These results will enable referees in tackle-contact based sports to make more subjective decisions, ultimately making these sports safer.

关键词： computer vision Protocols Tracking System dynamics Stability analysis Time factors Risk management

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