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Overlap Suppression Clustering for Offline Multi-Camera People Tracking

Overlap Suppression Clustering for Offline Multi-Camera Peop...

引用

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

作者： Ryuto Yoshida Junichi Okubo Junichiro Fujii Masazumi Amakata Takayoshi Yamashita Yachiyo Engineering Co. Ltd. Chubu University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Multi-Camera People Tracking is a multifaceted issue that requires the integration of several computer vision tasks, such as Object Detection, Multiple Object Tracking, and Person Re-identification. This study presents a multi-camera people tracking method that comprises four main processes: (1) single camera people tracking based on overlap suppression clustering, (2) representative image extraction using pose estimation for re-identification, (3) re-identification using hierarchical clustering with average linkage, and (4) low-identifiability tracklets *** RIIPS team achieved the highest Higher Order Tracking Accuracy (HOTA) of 71.9446% in the 2024 AI City Challenge Track 1.

关键词： Couplings computer vision Correlation conferences Pose estimation Urban areas Object detection

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DANICE: Domain adaptation without forgetting in neural image compression

DANICE: Domain adaptation without forgetting in neural image...

引用

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

作者： Katakol, Sudeep Herranz, Luis Yang, Fei Mrak, Marta Univ Michigan Ann Arbor MI 48109 USA UAB Comp Vis Ctr Barcelona Spain BBC R&D London England

ISBN: (纸本)9781665448994

Neural image compression (NIC) is a new coding paradigm where coding capabilities are captured by deep models learned from data. This data-driven nature enables new potential functionalities. In this paper, we study the adaptability of codecs to custom domains of interest. We show that NIC codecs are transferable and that they can be adapted with relatively few target domain images. However, naive adaptation interferes with the solution optimized for the original source domain, resulting in forgetting the original coding capabilities in that domain, and may even break the compatibility with previously encoded bitstreams. Addressing these problems, we propose Codec Adaptation without Forgetting (CAwF), a framework that can avoid these problems by adding a small amount of custom parameters, where the source codec remains embedded and unchanged during the adaptation process. Experiments demonstrate its effectiveness and provide useful insights on the characteristics of catastrophic interference in NIC.

关键词： Video coding computer vision Image coding Codecs conferences Transfer learning Interference

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Collaborative Image and Object Level Features for Image Colourisation

Collaborative Image and Object Level Features for Image Colo...

引用

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

作者： Pucci, Rita Micheloni, Christian Martinel, Niki Univ Udine Udine Italy

ISBN: (纸本)9781665448994

Image colourisation is an ill-posed problem, with multiple correct solutions which depend on the context and object instances present in the input datum. Previous approaches attacked the problem either by requiring intense user-interactions or by exploiting the ability of convolutional neural networks (CNNs) in learning image-level (context) features. However, obtaining human hints is not always feasible and CNNs alone are not able to learn entity-level semantics, unless multiple models pre-trained with supervision are considered. In this work, we propose a single network, named UCapsNet, that takes into consideration the image-level features obtained through convolutions and entity-level features captured by means of capsules. Then, by skip connections over different layers, we enforce collaboration between such the convolutional and entity factors to produce a high-quality and plausible image colourisation. We pose the problem as a classification task that can be addressed by a fully unsupervised approach, thus requires no human effort. Experimental results on three benchmark datasets show that our approach outperforms existing methods on standard quality metrics and achieves state-of-the-art performances on image colourisation. A large scale user study shows that our method is preferred over existing solutions. Code available at https://***/Riretta/Image_Colourisation_WiCV_2021.

关键词： Convolutional codes computer vision Semantics Collaboration Training data Feature extraction pattern recognition

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Dual-Teacher Class-Incremental Learning With Data-Free Generative Replay

Dual-Teacher Class-Incremental Learning With Data-Free Gener...

引用

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

作者： Choi, Yoojin El-Khamy, Mostafa Lee, Jungwon Samsung Semicond Inc SoC R&D San Diego CA 92121 USA Samsung Elect Syst LSI Suwon South Korea

ISBN: (纸本)9781665448994

This paper proposes two novel knowledge transfer techniques for class-incremental learning (CIL). First, we propose data-free generative replay (DF-GR) to mitigate catastrophic forgetting in CIL by using synthetic samples from a generative model. In the conventional generative replay, the generative model is pre-trained for old data and shared in extra memory for later incremental learning. In our proposed DF-GR, we train a generative model from scratch without using any training data, based on the pre-trained classification model from the past, so we curtail the cost of sharing pre-trained generative models. Second, we introduce dual-teacher information distillation (DT-ID) for knowledge distillation from two teachers to one student. In CIL, we use DT-ID to learn new classes incrementally based on the pre-trained model for old classes and another model (pre-)trained on the new data for new classes. We implemented the proposed schemes on top of one of the state-of-the-art CIL methods and showed the performance improvement on CIFAR-100 and ImageNet datasets.

关键词： Data privacy computer vision conferences Training data Data models pattern recognition Knowledge transfer

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Expression Transfer Using Flow-based Generative Models

Expression Transfer Using Flow-based Generative Models

引用

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

作者： Valenzuela, Andrea Segura, Carlos Diego, Ferran Gomez, Vicenc Univ Pompeu Fabra Barcelona Catalonia Spain Tel Res Barcelona Catalonia Spain

ISBN: (纸本)9781665448994

Among the different deepfake generation techniques, flow-based methods appear as natural candidates. Due to the property of invertibility, flow-based methods eliminate the necessity of person-specific training and are able to reconstruct any input image almost perfectly to human perception. We present a method for deepfake generation based on facial expression transfer using flow-based generative models. Our approach relies on simple latent vector operations akin to the ones used for attribute manipulation, but for transferring expressions between identity source-target pairs. We show the feasibility of this approach using a pre-trained Glow model and small sets of source and target images, not necessarily considered during prior training. We also provide an evaluation pipeline of the generated images in terms of similarities between identities and Action Units encoding the expression to be transferred. Our results show that an efficient expression transfer is feasible by using the proposed approach setting up a first precedent in deepfake content creation, and its evaluation, independently of the training identities.

关键词： Training computer vision Image coding conferences Computational modeling Pipelines pattern recognition

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Editing like Humans: A Contextual, Multimodal Framework for Automated Video Editing

Editing like Humans: A Contextual, Multimodal Framework for ...

引用

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

作者： Koorathota, Sharath Adelman, Patrick Cotton, Kelly Sajda, Paul Columbia Univ Dept Biomed Engn New York NY 10027 USA Fovea Inc New York NY 10001 USA CUNY Grad Ctr Dept Psychol New York NY USA

ISBN: (纸本)9781665448994

We propose an automated video editing model, which we term contextual and multimodal video editing (CMVE). The model leverages visual and textual metadata describing videos, integrating essential information from both modalities, and uses a learned editing style from a single example video to coherently combine clips. The editing model is useful for tasks such as generating news clip montages and highlight reels given a text query that describes the video storyline. The model exploits the perceptual similarity between video frames, objects in videos and text descriptions to emulate coherent video editing. Amazon Mechanical Turk participants made judgements comparing CMVE to expert human editing. Experimental results showed no significant difference in the CMVE vs human edited video in terms of matching the text query and the level of interest each generates, suggesting CMVE is able to effectively integrate semantic information across visual and textual modalities and create perceptually coherent quality videos typical of human video editors. We publicly release an online demonstration of our method.

关键词： Training Visualization computer vision conferences Semantics Metadata pattern recognition

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Initialization and Transfer Learning of Stochastic Binary Networks from Real-Valued Ones

Initialization and Transfer Learning of Stochastic Binary Ne...

引用

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

作者： Livochka, Anastasiia Shekhovtsov, Alexander Ukrainian Catholic Univ Lvov Ukraine Czech Tech Univ Prague Czech Republic

ISBN: (纸本)9781665448994

We consider the training of binary neural networks (BNNs) using the stochastic relaxation approach, which leads to stochastic binary networks (SBNs). We identify that a severe obstacle to training deep SBNs without skip connections is already the initialization phase. While smaller models can be trained from a random (possibly data-driven) initialization, for deeper models and large datasets, it becomes increasingly difficult to obtain non-vanishing and low variance gradients when initializing randomly. In this work, we initialize SBNs from real-valued networks with ReLU activations. Real valued networks are well established, easier to train and benefit from many techniques to improve their generalization properties. We propose that closely approximating their internal features can provide a good initialization for SBN. We transfer features incrementally, layer-by-layer, accounting for noises in the SBN, exploiting equivalent reparametrizations of ReLU networks and using a novel transfer loss formulation. We demonstrate experimentally that with the proposed initialization, binary networks can be trained faster and achieve a higher accuracy than when initialized randomly.

关键词： Training computer vision conferences Transfer learning Neural networks Stochastic processes pattern recognition

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Group Leakage Overestimates Performance: A Case Study in Keystroke Dynamics

Group Leakage Overestimates Performance: A Case Study in Key...

引用

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

作者： Ayotte, Blaine Banavar, Mahesh K. Hou, Daqing Schuckers, Stephanie Clarkson Univ Dept Elect & Comp Engn 8 Clarkson Ave Potsdam NY 13699 USA

ISBN: (纸本)9781665448994

Keystroke dynamics is a powerful behavioral biometric capable of user authentication based on typing patterns. As larger keystroke datasets become available, machine learning and deep learning algorithms are becoming popular. Knowledge of every possible impostor is not known during training which means that keystroke dynamics is an open set recognition problem. Treating open set recognition problems as closed set (assuming samples from all impostors are present) can cause models to incur data leakage, which can provide unrealistic overestimates of performance. It is a common problem in machine learning and can cause models to report higher accuracies than would be expected in the real world. In this paper, we outline open set recognition and discuss how, if not handled properly, it can lead to data leakage. The performance of common machine learning methods, such as SVM and MLP are investigated with and without leakage to clearly demonstrate the differences in performance. A synthetic dataset and a publicly available keystroke dynamics fixed-text dataset are used for research transparency and reproducibility.

关键词： Training Support vector machines Deep learning computer vision Machine learning algorithms Heuristic algorithms conferences

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Multi-Target Multi-Camera Vehicle Tracking for City-Scale Traffic Management

Multi-Target Multi-Camera Vehicle Tracking for City-Scale Tr...

引用

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

作者： Shim, Kyujin Yoon, Sungjoon Ko, Kangwook Kim, Changick Korea Adv Inst Sci & Technol Sch Elect Engn Daejeon South Korea

ISBN: (纸本)9781665448994

Multi-target multi-camera (MTMC) tracking is one of the important fields in computer vision, where multiple objects are tracked across multiple cameras. MTMC tracking can be applied to various tasks such as crowd analysis, city-scale traffic management, and transportation systems analysis for intelligent city planning. However, it is challenging due to the large variety of conditions of each camera, such as perspective and illumination. Furthermore, MTMC tracking for vehicles is more problematic because of the relatively large inter-class similarity and intra-class variability. In this paper, we tackle the MTMC tracking problem for vehicles by dividing it into three main steps: (i) vehicle detection and feature extraction, (ii) multi-target single-camera tracking using the appearance feature of each vehicle, and (iii) multi-camera association of local trajectories from each camera. Our method shows comparable results with other highly-ranked methods in AI City Challenge 2021 and outperforms a recent MTMC tracking method that ranked first place in AI City Challenge 2020.

关键词： computer vision Urban planning Pipelines Transportation Cameras Feature extraction Prediction algorithms

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Unsupervised Detection of Cancerous Regions in Histology Imagery using Image-to-Image Translation

Unsupervised Detection of Cancerous Regions in Histology Ima...

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

作者： Stepec, Dejan Skocaj, Danijel Univ Ljubljana Fac Comp & Informat Sci Vecna Pot 113 Ljubljana 1000 Slovenia XLAB Doo Pot Za Brdom 100 Ljubljana 1000 Slovenia

ISBN: (纸本)9781665448994

Detection of visual anomalies refers to the problem of finding patterns in different imaging data that do not conform to the expected visual appearance and is a widely studied problem in different domains. Due to the nature of anomaly occurrences and underlying generating processes, it is hard to characterize them and obtain labeled data. Obtaining labeled data is especially difficult in biomedical applications, where only trained domain experts can provide labels, which often come in large diversity and complexity. Recently presented approaches for unsupervised detection of visual anomalies approaches omit the need for labeled data and demonstrate promising results in domains, where anomalous samples significantly deviate from the normal appearance. Despite promising results, the performance of such approaches still lags behind supervised approaches and does not provide a one-fits-all solution. In this work, we present an image-to-image translation-based framework that significantly surpasses the performance of existing unsupervised methods and approaches the performance of supervised methods in a challenging domain of cancerous region detection in histology imagery.

关键词： Visualization computer vision Image analysis Histopathology conferences Biomedical measurement pattern recognition

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