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SoccerNet-Tracking: Multiple Object Tracking Dataset and Benchmark in Soccer Videos

SoccerNet-Tracking: Multiple Object Tracking Dataset and Ben...

引用

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

作者： Cioppa, Anthony Giancola, Silvio Deliege, Adrien Kang, Le Zhou, Xin Cheng, Zhiyu Ghanem, Bernard Van Droogenbroeck, Marc Univ Liege Liege Belgium KAUST Thuwal Saudi Arabia Baidu Res Beijing Peoples R China

ISBN: (纸本)9781665487399

Tracking objects in soccer videos is extremely important to gather both player and team statistics, whether it is to estimate the total distance run, the ball possession or the team formation. Video processing can help automating the extraction of those information, without the need of any invasive sensor, hence applicable to any team on any stadium. Yet, the availability of datasets to train learnable models and benchmarks to evaluate methods on a common testbed is very limited. In this work, we propose a novel dataset for multiple object tracking composed of 200 sequences of 30s each, representative of challenging soccer scenarios, and a complete 45-minutes half-time for long-term tracking. The dataset is fully annotated with bounding boxes and tracklet IDs, enabling the training of MOT baselines in the soccer domain and a full benchmarking of those methods on our segregated challenge sets. Our analysis shows that multiple player, referee and ball tracking in soccer videos is far from being solved, with several improvement required in case of fast motion or in scenarios of severe occlusion.

关键词： Training computer vision conferences Benchmark testing Object tracking Data mining Sports

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Unsupervised Continual Learning for Gradually Varying Domains

Unsupervised Continual Learning for Gradually Varying Domain...

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

作者： Taufique, Abu Md Niamul Jahan, Chowdhury Sadman Savakis, Andreas Rochester Inst Technol Rochester NY 14623 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In Unsupervised Domain Adaptation (UDA), a network is trained on a source domain and adapted on a target domain where no labeled data is available. Existing UDA techniques consider having the entire target domain available at once, which may not be feasible during deployment in realistic settings where batches of target data are acquired over time. Continual Learning (CL) has been dealing with data constrained paradigms in a supervised manner, where batches of labeled samples are sequentially presented to the network and the network continually learns from the new data without forgetting what was previously learned. Our method for unsupervised continual learning serves as a bridge between the UDA and CL paradigms. This research addresses a gradually evolving target domain fragmented into multiple sequential batches where the model continually adapts to the gradually varying stream of data in an unsupervised manner. To tackle this challenge, we propose a source free method based on episodic memory replay with buffer management. A contrastive loss is incorporated for better alignment of the buffer samples and the continual stream of batches. Our experiments on the rotating MNIST and CORe50 datasets confirm the benefits of our unsupervised continual learning method for gradually varying domains.

关键词： Learning systems Bridges computer vision Adaptation models Codes conferences Memory management

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Mitigating Paucity of Data in Sinusoid Characterization Using Generative Synthetic Noise

Mitigating Paucity of Data in Sinusoid Characterization Usin...

引用

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

作者： Sattarzadeh, Sam Shalmani, Shervin Manzuri Azad, Shervin Goldspot Discoveries Corp Montreal PQ Canada

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Although the remarkable breakthrough offered by Deep Learning (DL) models in numerous computer vision tasks, the need to acquire large amounts of high-quality natural data and fine-grained annotations is a shortcoming that fundamentally increases the cost and time devoted to training these models in real-world applications. Hence, synthetic datasets are considered reliable alternatives that can reduce the data acquisition by replacing or merging with natural data or effective pre-training of the models. To this end, in this work, we propose a novel approach to integrate structural data structures with the synthetic noise structures learned by unsupervised models that mimic the noise structures in natural data. Based on the proposed approach, we introduce the Sinusoid Feature recognition (SFR) dataset, which contains hard-to-detect fixed-period sinusoid waves. While the previous works in this regard use generative models to sample synthetic data to inflate the training set, we instead apply unsupervised learning models to generate deep synthetic noise which makes training models in the proposed dataset more challenging. We evaluate the segmentation, image reconstruction, and sinusoid characterization models pre-trained or fully trained on the synthetic SFR dataset on a private dataset of grayscale Acoustic Tele-Viewer (ATV) images. Experimental results show that supervision on our proposed synthetic dataset can improve the accuracy of the models by 3-4% via pre-training, and by 17-27% via ad-hoc training while dealing with challenging, realistic real-world images.

关键词： Training Deep learning Image segmentation computer vision Costs Computational modeling Data acquisition

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Goal-driven Self-Attentive Recurrent Networks for Trajectory Prediction

Goal-driven Self-Attentive Recurrent Networks for Trajectory...

引用

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

作者： Chiara, Luigi Filippo Coscia, Pasquale Das, Sourav Calderara, Simone Cucchiara, Rita Ballan, Lamberto Univ Padua Padua Italy Univ Modena & Reggio Emilia Modena Italy

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Human trajectory forecasting is a key component of autonomous vehicles, social-aware robots and advanced video-surveillance applications. This challenging task typically requires knowledge about past motion, the environment and likely destination areas. In this context, multimodality is a fundamental aspect and its effective modeling can be beneficial to any architecture. Inferring accurate trajectories is nevertheless challenging, due to the inherently uncertain nature of the future. To overcome these difficulties, recent models use different inputs and propose to model human intentions using complex fusion mechanisms. In this respect, we propose a lightweight attention-based recurrent backbone that acts solely on past observed positions. Although this backbone already provides promising results, we demonstrate that its prediction accuracy can be improved considerably when combined with a scene-aware goal-estimation module. To this end, we employ a common goal module, based on a U-Net architecture, which additionally extracts semantic information to predict scene-compliant destinations. We conduct extensive experiments on publicly-available datasets (i.e. SDD, inD, ETH/UCY) and show that our approach performs on par with state-of-the-art techniques while reducing model complexity.

关键词： Measurement Semantics Stochastic processes computer architecture Trajectory Data mining Task analysis

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Towards Real-world Shadow Removal with a Shadow Simulation Method and a Two-stage Framework

Towards Real-world Shadow Removal with a Shadow Simulation M...

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

作者： Gao, Jianhao Zheng, Quanlong Guo, Yandong Wuhan Univ Wuhan Peoples R China OPPO Res Inst Shenzhen Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Shadow removal is an important yet challenging restoration task. State-of-the-art shadow removal methods usually require paired datasets for training. Existing shadow removal datasets lack large-scale quantity and scene diversity. Hence, models trained on such datasets have poor generalization ability. This paper proposes a simple yet robust shadow simulation method to simulate shadow on the grayscale. The proposed shadow simulation method can be applied to arbitrary shadow-free images and masks to generate corresponding shadow images. With our shadow simulation method, we can generate a large-scale and diverse paired shadow removal dataset. Besides, we introduce a two-stage framework, Gray-to-Color Shadow Removal Network (G2C-DeshadowNet), for shadow removal. The first stage is a Grayscale Enhancement Network, which attempts to remove shadows on the grayscale. The second stage is a Colorization Network, which attempts to colorize the grayscale shadow-free image. Extensive experiments on ISTD+, SRD, and SBU datasets show that G2C-DeshadowNet outperforms state-of-the-art methods and has better generalization ability. We will release our code at https://***/jianhaogao/Shadow-Removal-with-Two-stage-Framework.

关键词： Training computer vision Image color analysis conferences Computational modeling Gray-scale Robustness

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DeepSegmenter: Temporal Action Localization for Detecting Anomalies in Untrimmed Naturalistic Driving Videos

DeepSegmenter: Temporal Action Localization for Detecting An...

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2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Aboah, Armstrong Bagci, Ulas Mussah, Abdul Rashid Owor, Neema Jakisa Adu-Gyamfi, Yaw Northwestern University Department of Radiology United States University of Missouri-Columbia Department of Civil Engineering United States

ISBN: (纸本)9798350302493

Identifying unusual driving behaviors exhibited by drivers during driving is essential for understanding driver behavior and the underlying causes of crashes. Previous studies have primarily approached this problem as a classification task, assuming that naturalistic driving videos come discretized. However, both activity segmentation and classification are required for this task due to the continuous nature of naturalistic driving videos. The current study therefore departs from conventional approaches and introduces a novel methodological framework, DeepSegmenter, that simultaneously performs activity segmentation and classification in a single framework. The proposed framework consists of four major modules namely Data Module, Activity Segmentation Module, Classification Module and Postprocessing Module. Our proposed method won 8th place in the 2023 AI City Challenge, Track 3, with an activity overlap score of 0.5426 on experimental validation data. The experimental results demonstrate the effectiveness, efficiency, and robustness of the proposed system. The code is available at https://***/aboah1994/***. © 2023 ieee.

关键词： computer vision

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2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Sreenivas, Manogna Biswas, Soma Indian Institute of Science Bangalore India

ISBN: (纸本)9798350302493

Cross-Domain Few-Shot Learning (CD-FSL) aims to recognize new classes from unseen domains, given limited training samples. Majority of the state-of-the-art approaches for this task introduce new task-specific additional parameters for adapting to the novel task, which involves changing the trained model architecture, in addition to increasing the number of model parameters. The first contribution of this work is to revisit the existing approaches like modifying the Batch Normalization affine parameters and the scale hyperparameter in cosine similarity based softmax loss for adapting the trained model to the new tasks, without changing the model architecture. Secondly, to aid the model learning with few examples per class, we propose to augment the data of each class with the styles of the semantically similar classes. Extensive evaluation on the challenging Meta-Dataset shows that this simple framework is very effective for the CD-FSL task. We also show that the Similar-class Style Augmentation module can be seamlessly integrated with existing approaches to further improve their performance, thus establishing the state-of-the-art in this challenging area. © 2023 ieee.

关键词： computer vision

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Multi stain graph fusion for multimodal integration in pathology

Multi stain graph fusion for multimodal integration in patho...

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

作者： Dwivedi, Chaitanya Nofallah, Shima Pouryahya, Maryam Iyer, Janani Leidal, Kenneth Chung, Chuhan Watkins, Timothy Billin, Andrew Myers, Robert Abel, John Behrooz, Ali PathAI Inc Boston MA 02215 USA Gilead Sci Inc Foster City CA 94404 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In pathology, tissue samples are assessed using multiple staining techniques to enhance contrast in unique histologic features. In this paper, we introduce a multimodal CNN-GNN based graph fusion approach that leverages complementary information from multiple non-registered histopathology images to predict pathologic scores. We demonstrate this approach in nonalcoholic steatohepatitis (NASH) by predicting CRN fibrosis stage and NAFLD Activity Score (NAS). Primary assessment of NASH typically requires liver biopsy evaluation on two histological stains: Trichrome (TC) and hematoxylin and eosin (H&E). Our multimodal approach learns to extract complementary information from TC and H&E graphs corresponding to each stain while simultaneously learning an optimal policy to combine this information. We report up to 20% improvement in predicting fibrosis stage and NAS component grades over single-stain modeling approaches, measured by computing linearly weighted Cohen's kappa between machinederived vs. pathologist consensus scores. Broadly, this paper demonstrates the value of leveraging diverse pathology images for improved ML-powered histologic assessment.

关键词： Weight measurement computer vision Histopathology Computational modeling conferences Liver Predictive models

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BCI: Breast Cancer Immunohistochemical Image Generation through Pyramid Pix2pix

BCI: Breast Cancer Immunohistochemical Image Generation thro...

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

作者： Liu, Shengjie Zhu, Chuang Xu, Feng Jia, Xinyu Shi, Zhongyue Jin, Mulan Beijing Univ Posts & Telecommun Beijing Peoples R China Capital Med Univ Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The evaluation of human epidermal growth factor receptor 2 (HER2) expression is essential to formulate a precise treatment for breast cancer. The routine evaluation of HER2 is conducted with immunohistochemical techniques (IHC), which is very expensive. Therefore, for the first time, we propose a breast cancer immunohistochemical (BCI) benchmark attempting to synthesize IHC data directly with the paired hematoxylin and eosin (HE) stained images. The dataset contains 4870 registered image pairs, covering a variety of HER2 expression levels. Based on BCI, as a minor contribution, we further build a pyramid pix2pix image generation method, which achieves better HE to IHC translation results than the other current popular algorithms. Extensive experiments demonstrate that BCI poses new challenges to the existing image translation research. Besides, BCI also opens the door for future pathology studies in HER2 expression evaluation based on the synthesized IHC images. BCI dataset can be downloaded from https://***/BCI.

关键词： Pathology computer vision Image synthesis conferences Breast tissue Epidermis Breast cancer

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Pyramid Ensemble Structure for High Resolution Image Shadow Removal

Pyramid Ensemble Structure for High Resolution Image Shadow ...

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2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Cui, Shuhao Huang, Junshi Tian, Shuman Fan, Mingyuan Zhang, Jiaqi Zhu, Li Wei, Xiaoming Wei, Xiaolin Meituan Group China

ISBN: (纸本)9798350302493

Existing methods for shadow removal in high-resolution images may not be effective due to challenges such as the time-consuming nature of training and the loss of visual data during image cropping or resizing, highlighting the necessity for the development of more efficient methods. In this paper, we propose a novel Pyramid Ensemble Structure (PES) for High Resolution Image Shadow Removal. Our approach takes advantage of multiple scales by constructing pyramid inputs that allow for the capturing of a wide range of shadow sizes and shapes. We then train the network in pyramid stages to enhance global information processing. Furthermore, an ensemble of different shadow removal models is employed, and the maximum value is chosen to indicate the least amount of remaining shadow in the output. Experiments on both validation and testing data sets confirm the effectiveness of our method. In the Image Shadow Removal Challenge competition, our method obtained 22.36 PSNR score (1st place) and 0.70 SSIM score (2nd place) on the test sets. © 2023 ieee.

关键词： computer vision

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