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End-to-End High-Risk Tackle Detection System for Rugby

End-to-End High-Risk Tackle Detection System for Rugby

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

作者： Nonaka, Naoki Fujihira, Ryo Nishio, Monami Murakami, Hidetaka Tajima, Takuya Yamada, Mutsuo Maeda, Akira Seita, Jun RIKEN Informat R&D & Strategy Headquarters Adv Data Sci Project Tokyo Japan Murakami Surg Hosp Nagasaki Japan Univ Miyazaki Fac Med Miyazaki Japan Ryutsu Keizai Univ Fac Hlth & Sport Sci Ryugasaki Japan Hakata Knee & Sports Clin Fukuoka Japan Kurume Univ Fac Human Hlth Kurume Fukuoka Japan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Reducing risk of severe injury such as concussion is a high priority for any contact sports. In rugby, Head Injury Assessment (HIA) protocol has been introduced to identify and protect players showing symptoms of concussion and having potential risk of concussion. However, on-field decisions by officials are sometimes difficult and subjective, and HIA is affordable only for elite leagues since it requires medical specialists. To make rugby matches more safe, we aim to develop a system to detect high-risk tackles, potential triggers of concussion, based on deep learning models. Our system takes rugby match video, then first identifies frame with tackle, subsequently detects location of tackle and estimate pose of the ball carrier and the tackler, and finally evaluate the risk of tackle using posture pair of players. Among the model combinations we have examined, the best performance was achieved with the combination of ResNet (2+1)D as tackle frame selection model, RetinaNet as tackle detection model and CenterTrack as pose estimation model. Evaluation using test data, a set of short clips from broadcasted rugby match videos, showed our system was able to detect 50% of high-risk tackles without any human intervention. This result opens a path for automated systems to detect high-risk events, leading to less expensive and more objective monitoring not only for rugby but also for any contact sports.

关键词： Deep learning computer vision Protocols Head conferences Pose estimation pattern recognition

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Self-Supervised Voxel-Level Representation Rediscovers Subcellular Structures in Volume Electron Microscopy

Self-Supervised Voxel-Level Representation Rediscovers Subce...

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

作者： Han, Hongqing Dmitrieva, Mariia Sauer, Alexander Tam, Ka Ho Rittscher, Jens Univ Oxford Inst Biomed Engn Dept Engn Sci Oxford England

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Making sense of large volumes of biological imaging data without human annotation often relies on unsupervised representation learning. Although efforts have been made to representing cropped-out microscopy images of single cells and single molecules, a more robust and general model that effectively maps every voxel in a whole cell volume onto a latent space is still lacking. Here, we use variational auto-encoder and metric learning to obtain a voxel-level representation, and explore using it for unsupervised segmentation. To our knowledge we are the first to present self-supervised voxel-level representation and subsequent unsupervised segmentation results for a complete cell. We improve upon earlier work by proposing an innovative approach to separate latent space into a semantic subspace and a transformational subspace, and only use the semantic representation for segmentation. We show that in the learned semantic representation the major subcellular components are visually distinguishable and the semantic subspace is more transformation-invariant than another sample latent subspace of equal dimension. For unsupervised segmentation we found that our model manages to automatically rediscover and separate the major classes with errors demonstrating spatial patterns, and further dissect the class not specified by reference segmentation into areas with consistent textures. Our segmentation outperforms a baseline by a large margin.

关键词： Representation learning Measurement Solid modeling Image segmentation computer vision conferences Biological system modeling

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A Lightweight Network for High Dynamic Range Imaging

A Lightweight Network for High Dynamic Range Imaging

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

作者： Yan, Qingsen Zhang, Song Chen, Weiye Liu, Yuhang Zhang, Zhen Zhang, Yanning Shi, Javen Qinfeng Gong, Dong Univ Adelaide Australian Inst Machine Learning Adelaide SA Australia Xidian Univ Sch Artificial Intelligence Xian Peoples R China Xidian Univ Guangzhou Inst Technol Xian Peoples R China Northwestern Polytech Univ Sch Comp Sci Xian Peoples R China Univ New South Wales Sch Comp Sci & Engn Sydney NSW Australia

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Multi-frame high dynamic range (HDR) reconstruction methods try to expand the range of illuminance with differently exposed images. They suffer from ghost artifacts when camera jittering or object moving. Several methods can generate high-quality HDR images with high computational complexity, but the inference process is too slow. However, the network with small parameters will produce unsatisfactory results. To balance the quality and computational complexity, we propose a lightweight network for HDR imaging that has small parameters and fast speed. Specifically, following AHDRNet, we employ a spatial attention module to detect the misaligned regions to avoid ghost artifacts. Considering the missing details in over/under- exposure regions, we propose a dual attention module for selectively retaining information to force the fusion network to learn more details for degenerated regions. Furthermore, we employ an encoder-decoder structure with a lightweight block to achieve the fusion process. As a result, the high-quality content and features can be reconstructed after the attention module. Finally, we fuse high-resolution features and the encoder-decoder features into the HDR imaging results. Experimental results demonstrate that the proposed method performs favorably against the state-of-the-art methods, achieving a PSNR of 39.05 and a PSNR-mu of 37.27 with 156.12 GMAcs in NTIRE 2022 HDR Challenge (Track 2 Fidelity).

关键词： computer vision Fuses conferences Force Dynamic range Reconstruction algorithms Cameras

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Learning Co-segmentation by Segment Swapping for Retrieval and Discovery

Learning Co-segmentation by Segment Swapping for Retrieval a...

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

作者： Shen, Xi Efros, Alexei A. Joulin, Armand Aubry, Mathieu Ecole Ponts ParisTech LIGM UMR 8049 Champs Sur Marne France Univ Calif Berkeley Berkeley CA 94720 USA Facebook AI Res Menlo Pk CA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The goal of this work is to efficiently identify visually similar patterns in images, e.g. identifying an artwork detail copied between an engraving and an oil painting, or recognizing parts a night-time photograph visible in its daytime counterpart. Lack of training data is a key challenge for this co-segmentation task. We present a simple yet surprisingly effective approach to overcome this difficulty: we generate synthetic training pairs by selecting segments in an image and copy-pasting them into another image. We then learn to predict the repeated region masks. We find that it is crucial to predict the correspondences as an auxiliary task and to use Poisson blending and style transfer on the training pairs to generalize on real data. We analyse results with two deep architectures relevant to our joint image analysis task: a transformer-based architecture and Sparse Nc-Net, a recent network designed to predict coarse correspondences using 4D convolutions. We show our approach provides clear improvements for artwork details retrieval on the Brueghel dataset and achieves competitive performance on two place recognition benchmarks, Tokyo247 and Pitts30K. We also demonstrate the potential of our approach for unsupervised image collection analysis by introducing a spectral graph clustering approach to object discovery and demonstrating it on the object discovery dataset of [49] and the Brueghel dataset. Our code and data are available at http://***/similar to shenx/SegSwap/.

关键词： Training Image segmentation Oils Training data computer architecture Oil insulation Transformers

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MANIQA: Multi-dimension Attention Network for No-Reference Image Quality Assessment

MANIQA: Multi-dimension Attention Network for No-Reference I...

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

作者： Yang, Sidi Wu, Tianhe Shi, Shuwei Lao, Shanshan Gong, Yuan Cao, Mingdeng Wang, Jiahao Yang, Yujiu Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Shenzhen Peoples R China Tsinghua Univ Shenzhen Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

No-Reference Image Quality Assessment (NR-IQA) aims to assess the perceptual quality of images in accordance with human subjective perception. Unfortunately, existing NR-IQA methods are far from meeting the needs of predicting accurate quality scores on GAN-based distortion images. To this end, we propose Multi-dimension Attention Network for no-reference Image Quality Assessment (MANIQA) to improve the performance on GAN-based distortion. We firstly extract features via ViT, then to strengthen global and local interactions, we propose the Transposed Attention Block (TAB) and the Scale Swin Transformer Block (SSTB). These two modules apply attention mechanisms across the channel and spatial dimension, respectively. In this multi-dimensional manner, the modules cooperatively increase the interaction among different regions of images globally and locally. Finally, a dual branch structure for patch-weighted quality prediction is applied to predict the final score depending on the weight of each patch's score. Experimental results demonstrate that MANIQA outperforms state-of-the-art methods on four standard datasets (LIVE, TID2013, CSIQ, and KADID-10K) by a large margin. Besides, our method ranked first place in the final testing phase of the NTIRE 2022 Perceptual Image Quality Assessment Challenge Track 2: No-Reference. Codes and models are available at https://***/IIGROUP/MANIQA.

关键词： Image quality Databases Distortion Feature extraction Transformers Quality assessment pattern recognition

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Holistic Approach to Measure Sample-level Adversarial Vulnerability and its Utility in Building Trustworthy Systems

Holistic Approach to Measure Sample-level Adversarial Vulner...

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

作者： Nayak, Gaurav Kumar Rawal, Ruchit Lal, Rohit Patil, Himanshu Chakraborty, Anirban Indian Inst Sci Bangalore Karnataka India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Adversarial attack perturbs an image with an imperceptible noise, leading to incorrect model prediction. Recently, a few works showed inherent bias associated with such attack (robustness bias), where certain subgroups in a dataset (e.g. based on class, gender, etc.) are less robust than others. This bias not only persists even after adversarial training, but often results in severe performance discrepancies across these subgroups. Existing works characterize the subgroup's robustness bias by only checking individual sample's proximity to the decision boundary. In this work, we argue that this measure alone is not sufficient and validate our argument via extensive experimental analysis. It has been observed that adversarial attacks often corrupt the high-frequency components of the input image. We, therefore, propose a holistic approach for quantifying adversarial vulnerability of a sample by combining these different perspectives, i.e., degree of model's reliance on high-frequency features and the (conventional) sample-distance to the decision boundary. We demonstrate that by reliably estimating adversarial vulnerability at the sample level using the proposed holistic metric, it is possible to develop a trustworthy system where humans can be alerted about the incoming samples that are highly likely to be misclassified at test time. This is achieved with better precision when our holistic metric is used over individual measures. To further corroborate the utility of the proposed holistic approach, we perform knowledge distillation in a limited-sample setting. We observe that the student network trained with the subset of samples selected using our combined metric performs better than both the competing baselines, viz., where samples are selected randomly or based on their distances to the decision boundary.

关键词： Measurement Training Knowledge engineering computer vision conferences Predictive models Reliability engineering

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Deep Scale-space Mining Network for Single Image Deraining

Deep Scale-space Mining Network for Single Image Deraining

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

作者： Li, Pengpeng Jin, Jiyu Jin, Guiyue Fan, Lei Gao, Xiao Song, Tianyu Chen, Xiang Dalian Polytech Univ Sch Informat Sci & Engn Dalian Peoples R China Shenyang Aerosp Univ Coll Elect & Informat Engn Shenyang Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Images captured by outdoor vision systems can often be affected by rain weather, resulting in severe degradation of the visual quality of the captured images. Therefore, image deraining has attracted attention as urgent and challenging research. Many current data-driven approaches achieve better performance but are limited in recovering image details. This is because these methods do not fully mine the correlation of scale-space, which are beneficial for rain removal. In this paper, we design an end-to-end Deep Scalespace Mining Network (DSM-Net) for single image deraining to solve these problems. The proposed network with multi-scale extraction, concurrent attention distillation, and hierarchical information fusion accurately captures scale-space features and learns richer information for better deraining. For better feature extraction, a Multi-scale Attention Block (MAB) is introduced to obtain multi-scale rain streak features by different dilated convolutions. Besides,a Concurrent Attention Distillation Block (CADB) is developed which combined channel attention and subspace attention to calibrate the image features obtained from multiscale acquisition and hierarchical learning, then eliminate redundant features. Importantly, the overall architecture of DSM-Net is inspired by the HourglassNet and DenseNet, which progressively explores and fuses local and global features at different scales in a hierarchical manner instead of direct concatenation. Extensive experiments on synthetic and real datasets show that the proposed DSM-Net outperforms recent state-of-the-art deraining algorithms in terms of both performance and preservation of image details.

关键词： Degradation Deep learning Visualization Rain Correlation Fuses Machine vision

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OMG: Observe Multiple Granularities for Natural Language-Based Vehicle Retrieval

OMG: Observe Multiple Granularities for Natural Language-Bas...

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

作者： Du, Yunhao Zhang, Binyu Ruan, Xiangning Su, Fei Zhao, Zhicheng Chen, Hong Beijing Univ Posts & Telecommun Beijing Peoples R China Beijing Key Lab Network Syst & Network Culture Beijing Peoples R China China Mobile Res Inst Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Retrieving tracked-vehicles by natural language descriptions plays a critical role in smart city construction. It aims to find the best match for the given texts from a set of tracked vehicles in surveillance videos. Existing works generally solve it by a dual-stream framework, which consists of a text encoder, a visual encoder and a cross-modal loss function. Although some progress has been made, they failed to fully exploit the information at various levels of granularity. To tackle this issue, we propose a novel framework for the natural language-based vehicle retrieval task, OMG, which Observes Multiple Granularities with respect to visual representation, textual representation and objective functions. For the visual representation, target features, context features and motion features are encoded separately. For the textual representation, one global embedding, three local embeddings and a color-type prompt embedding are extracted to represent various granularities of semantic features. Finally, the overall framework is optimized by a cross-modal multi-granularity contrastive loss function. Experiments demonstrate the effectiveness of our method. Our OMG significantly outperforms all previous methods and ranks the 9th on the 6th AI City Challenge Track2.

关键词： Representation learning Visualization Smart cities Surveillance Semantics Feature extraction pattern recognition

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Nighttime Image Dehazing Based on Variational Decomposition Model

Nighttime Image Dehazing Based on Variational Decomposition ...

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

作者： Liu, Yun Yan, Zhongsheng Wu, Aimin Ye, Tian Li, Yuche Southwest Univ Coll Artificial Intelligence Chongqing 400715 Peoples R China Chongqing Coll Int Business & Econ Coll Big Data & Intelligent Engn Chongqing 401520 Peoples R China Jimei Univ Sch Ocean Informat Engn Xiamen 361021 Peoples R China China Univ Petr Coll Geosci Beijing 102249 Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Most of existing dehazing algorithms are unable to deal with nighttime hazy scenarios well due to complex degraded factors such as non-uniform illumination, low light and glows. To obtain high-quality image under nighttime haze imaging conditions, we present an effective single nighttime image dehazing framework based on a variational decomposition model to simultaneously address these undesirable issues. First, a variational decomposition model consisting of three regularization terms is proposed to simultaneously decompose a nighttime hazy image into a structure layer, a detail layer and a noise layer. Concretely, we employ l(1) norm to constrain the structure component, adopt l(0) sparsity term to enforce the piece-wise continuous of the detail layer, and use l(2) norm to separate the noise layer. Next, the structure layer is recovered by means of inversing the physical model and the detail layers are revealed in a multi-scale gradient enhancement manner. Finally, the dehazed structure layer and the enhanced detail layers are integrated into a haze-free image. Experimental results show that the proposed framework achieves superior performance on nighttime haze removal and noise suppression compared with several state-of-the-art dehazing techniques.

关键词： Degradation computer vision conferences Computational modeling Noise reduction Lighting Focusing

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CIPPSRNet: A Camera Internal Parameters Perception Network Based Contrastive Learning for Thermal Image Super-Resolution

CIPPSRNet: A Camera Internal Parameters Perception Network B...

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

作者： Wang, Kai Sun, Qigong Wang, Yicheng Wei, Huiyuan Lv, Chonghua Tian, Xiaolin Liu, Xu SenseTime Res Hong Kong Peoples R China Xidian Univ Xian Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Thermal Image Super-Resolution (TISR) is a technique for converting Low-Resolution (LR) thermal images to High-Resolution (HR) thermal images. This technique has recently become a research hotspot due to its ability to reduce sensor costs and improve visual perception. However, current research does not provide an effective solution for multi-sensor data training, possibly driven by pixel mismatch and simple degradation setting issues. In this paper, we proposed a Camera Internal Parameters Perception Network (CIPPSRNet) for LR thermal image enhancement. The camera internal parameters (CIP) were explicitly modeled as a feature representation, the LR features were transformed into the intermediate domain containing the internal parameters information by perceiving CIP representation. The mapping between the intermediate domain and the spatial domain of the HR features was learned by CIPPSRNet. In addition, we introduced contrastive learning to optimize the pretrained Camera Internal Parameters Representation Network and the feature encoders. Our proposed network is capable of achieving a more efficient transformation from the LR to the HR domains. Additionally, the use of contrastive learning can improve the network's adaptability to misalignment data with insufficient pixel matching and its robustness. Experiments on PBVS2022 TISR Dataset show that our network has achieved state-of-the-art performance for the Thermal SR task.

关键词： Training Degradation Visualization Superresolution Cameras Robustness pattern recognition

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