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Making use of unlabeled data: Comparing strategies for marine animal detection in long-tailed datasets using self-supervised and semi-supervised pre-training

Making use of unlabeled data: Comparing strategies for marin...

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

作者： Sharma, Tarun Cline, Danelle E. Edgington, Duane CALTECH Pasadena CA 91125 USA MBARI Moss Landing CA USA

ISBN: (纸本)9798350365474

This paper discusses strategies for object detection in marine images from a practitioner's perspective working with real-world long-tail distributed datasets with a large amount of additional unlabeled data on hand. The paper discusses the benefits of separating the localization and classification stages, making the case for robustness in localization through the amalgamation of additional datasets inspired by a widely used approach by practitioners in the camera-trap literature. For the classification stage, the paper compares strategies to use additional unlabeled data, comparing supervised, supervised iteratively, self-supervised, and semi-supervised pre-training approaches. Our findings reveal that semi-supervised pre-training, followed by supervised fine-tuning, yields a significantly improved balanced performance across the long-tail distribution, albeit occasionally with a trade-off in overall accuracy. These insights are validated through experiments on two real-world long-tailed underwater datasets collected by the Monterey Bay Aquarium Research Institute (MBARI).

关键词： computer vision conservation technology long-tail long-tailed distribution marine imaging real-world datasets Self-supervised learning semi-supervised learning underwater animal detection unlabeled data

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Recognising Team Activities from Noisy Data

Recognising Team Activities from Noisy Data

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26th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Bialkowski, Alina Lucey, Patrick Carr, Peter Denman, Simon Matthews, Iain Sridharan, Sridha Disney Res Pittburgh PA 94565 USA Queensland Univ Technol Image & Video Lab Brisbane Qld 4072 Australia

ISBN: (纸本)9780769549903

Recently, vision-based systems have been deployed in professional sports to track the ball and players to enhance analysis of matches. Due to their unobtrusive nature, vision-based approaches are preferred to wearable sensors (e. g. GPS or RFID sensors) as it does not require players or balls to be instrumented prior to matches. Unfortunately, in continuous team sports where players need to be tracked continuously over long-periods of time (e. g. 35 minutes in field-hockey or 45 minutes in soccer), current vision-based tracking approaches are not reliable enough to provide fully automatic solutions. As such, human intervention is required to fix-up missed or false detections. However, in instances where a human can not intervene due to the sheer amount of data being generated - this data can not be used due to the missing/noisy data. In this paper, we investigate two representations based on raw player detections (and not tracking) which are immune to missed and false detections. Specifically, we show that both team occupancy maps and centroids can be used to detect team activities, while the occupancy maps can be used to retrieve specific team activities. An evaluation on over 8 hours of field hockey data captured at a recent international tournament demonstrates the validity of the proposed approach.

关键词： computer vision image sensors object recognition object tracking sport

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Material Swapping for 3D Scenes using a Learnt Material Similarity Measure

Material Swapping for 3D Scenes using a Learnt Material Simi...

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

作者： Perroni-Scharf, Maxine Sunkavalli, Kalyan Eisenmann, Jonathan Hold-Geoffroy, Yannick Princeton Univ Princeton NJ 08544 USA Adobe Res San Jose CA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We present a method for augmenting photo-realistic 3D scene assets by automatically recognizing, matching, and swapping their materials. Our method proposes a material matching pipeline for the efficient replacement of unknown materials with perceptually similar PBR materials from a database, enabling the quick creation of many variations of a given 3D synthetic scene. At the heart of this method is a novel material similarity feature that is learnt, in conjunction with optimal lighting conditions, by fine-tuning a deep neural network on a material classification task using our proposed dataset. Our evaluation demonstrates that lighting optimization improves CNN-based texture feature extraction methods and better estimates material properties. We conduct a series of experiments showing our method's ability to augment photo-realistic indoor scenes using both standard and procedurally generated PBR materials.

关键词： Training Three-dimensional displays Pipelines Neural networks Lighting Feature extraction Rendering (computer graphics)

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Poisons that are learned faster are more effective

Poisons that are learned faster are more effective

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

作者： Sandoval-Segura, Pedro Singla, Vasu Fowl, Liam Geiping, Jonas Goldblum, Micah Jacobs, David Goldstein, Tom Univ Maryland College Pk MD 20742 USA New York Univ New York NY USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Imperceptible poisoning attacks on entire datasets have recently been touted as methods for protecting data privacy. However, among a number of defenses preventing the practical use of these techniques, early-stopping stands out as a simple, yet effective defense. To gauge poisons' vulnerability to early-stopping, we benchmark error-minimizing, error-maximizing, and synthetic poisons in terms of peak test accuracy over 100 epochs and make a number of surprising observations. First, we find that poisons that reach a low training loss faster have lower peak test accuracy. Second, we find that a current state-of-the-art error-maximizing poison is 7x less effective when poison training is stopped at epoch 8. Third, we find that stronger, more transferable adversarial attacks do not make stronger poisons. We advocate for evaluating poisons in terms of peak test accuracy.

关键词： Training Data privacy Privacy computer vision Toxicology Correlation Perturbation methods

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Bio-inspired Dynamic 3D Discriminative Skeletal Features for Human Action recognition

Bio-inspired Dynamic 3D Discriminative Skeletal Features for...

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26th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Chaudhry, Rizwan Ofli, Ferda Kurillo, Gregorij Bajcsy, Ruzena Vidal, Rene Johns Hopkins Univ Ctr Imaging Sci Baltimore MD 21218 USA Univ Calif Berkeley Tele Immers Lah Berkeley CA 94720 USA

ISBN: (纸本)9780769549903

Over the last few years, with the immense popularity of the Kinect, there has been renewed interest in developing methods for human gesture and action recognition from 3D data. A number of approaches have been proposed that extract representative features from 3D depth data, a reconstructed 3D surface mesh or more commonly from the recovered estimate of the human skeleton. Recent advances in neuroscience have discovered a neural encoding of static 3D shapes in primate infero-temporal cortex that can be represented as a hierarchy of medial axis and surface features. We hypothesize a similar neural encoding might also exist for 3D shapes in motion and propose a hierarchy of dynamic medial axis structures at several spatio-temporal scales that can be modeled using a set of Linear Dynamical Systems (LDSs). We then propose novel discriminative metrics for comparing these sets of LDSs for the task of human activity recognition. Combined with simple classification frameworks, our proposed features and corresponding hierarchical dynamical models provide the highest human activity recognition rates as compared to state-of-the-art methods on several skeletal datasets.

关键词： gesture recognition mesh generation neural nets pattern classification shape recognition solid modelling surface fitting

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MTUNet: Few-shot Image Classification with Visual Explanations

MTUNet: Few-shot Image Classification with Visual Explanatio...

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

作者： Wang, Bowen Li, Liangzhi Verma, Manisha Nakashima, Yuta Kawasaki, Ryo Nagahara, Hajime Osaka Univ Inst Databil Sci IDS Suita Osaka Japan Osaka Univ Grad Sch Med Suita Osaka Japan

ISBN: (纸本)9781665448994

Few-shot learning (FSL) approaches, mostly neural network-based, are assuming that the pre-trained knowledge can be obtained from base (seen) categories and transferred to novel (unseen) categories. However, the black-box nature of neural networks makes it difficult to understand what is actually transferred, which may hamper its application in some risk-sensitive areas. In this paper, we reveal a new way to perform explainable FSL for image classification, using discriminative patterns and pairwise matching. Experimental results prove that the proposed method can achieve satisfactory explainability on two mainstream datasets. Code is available*.

关键词： Knowledge engineering Visualization computer vision conferences Computational modeling Neural networks Benchmark testing

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Class-Incremental Experience Replay for Continual Learning under Concept Drift

Class-Incremental Experience Replay for Continual Learning u...

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

作者： Korycki, Lukasz Krawczyk, Bartosz Virginia Commonwealth Univ Dept Comp Sci Richmond VA 23284 USA

ISBN: (纸本)9781665448994

Modern machine learning systems need to be able to cope with constantly arriving and changing data. Two main areas of research dealing with such scenarios are continual learning and data stream mining. Continual learning focuses on accumulating knowledge and avoiding forgetting, assuming information once learned should be stored. Data stream mining focuses on adaptation to concept drift and discarding outdated information, assuming that only the most recent data is relevant. While these two areas are mainly being developed in separation, they offer complementary views on the problem of learning from dynamic data. There is a need for unifying them, by offering architectures capable of both learning and storing new information, as well as revisiting and adapting to changes in previously seen concepts. We propose a novel continual learning approach that can handle both tasks. Our experience replay method is fueled by a centroid-driven memory storing diverse instances of incrementally arriving classes. This is enhanced with a reactive subspace buffer that tracks concept drift occurrences in previously seen classes and adapts clusters accordingly. The proposed architecture is thus capable of both remembering valid and forgetting outdated information, offering a holistic framework for continual learning under concept drift.

关键词： computer vision conferences computer architecture Machine learning Data mining Task analysis

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Sea Situational Awareness (SeaSAw) Dataset

Sea Situational Awareness (SeaSAw) Dataset

引用

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

作者： Kaur, Parneet Aziz, Arslan Jain, Darshan Patel, Harshil Hirokawa, Jonathan Townsend, Lachlan Reimers, Christoph Hua, Fiona

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Vessels move 90% of international cargo by volume, with the marine economy contributing to 5.1% of global GDP. As one of the oldest industries, the marine industry has yet to embrace innovations in modern technology to safeguard the blue economy. Situational awareness from intelligent vessel systems can enable enhanced safety and decision-making for mariners. As the foundation for these intelligent systems, advanced perception technology requires sufficient real-world operational data to leverage recent AI technologies. In this work, we introduce the Sea Situational Awareness (SeaSAw) dataset - a novel dataset that is comprised of 1.9 million images with 14.6 million objects associated with 20.4 million attributes from 12 object classes, making it the largest maritime dataset for object detection, fine-grained classification and tracking. Furthermore, this dataset consists of 9 sources in combination with various RGB cameras, mounted on different moving vessels, operating in different geographic locations globally, having variations in scenario, weather and illumination conditions. This data collection took place across 4 years with rigorous efforts on data selection, annotation, management and analysis to enhance the marine perception technology.

关键词： Industries computer vision Technological innovation Image resolution Lighting Object detection Safety

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A Sequential Classifier for Hand Detection in the Framework of Egocentric vision

A Sequential Classifier for Hand Detection in the Framework ...

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27th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Betancourt, Alejandro Lopez, Miriam M. Regazzoni, Carlo S. Rauterberg, Matthias Univ Genoa Dept Naval Elect Elect & Telecommun Engn I-16126 Genoa Italy Eindhoven Univ Technol Dept Ind Design Designed Intelligence Grp Eindhoven Netherlands

ISBN: (纸本)9781479943098

Hand detection is one of the most explored areas in Egocentric vision Video Analysis for wearable devices. Current methods are focused on pixel-by-pixel hand segmentation, with the implicit assumption of hand presence in almost all activities. However, this assumption is false in many applications for wearable cameras. Ignoring this fact could affect the whole performance of the device since hand measurements are usually the starting point for higher level inference, or could lead to inefficient use of computational resources and battery power. In this paper we propose a two-level sequential classifier, in which the first level, a hand-detector, deals with the possible presence of hands from a global perspective, and the second level, a hand-segmentator, delineates the hand regions at pixel level in the cases indicated by the first block. The performance of the sequential classifier is stated in probabilistic notation as a combination of both, classifiers allowing to test new hand-detectors independently of the type of segmentation and the dataset used in the training stage. Experimental results show a considerable improvement in the detection of true negatives, without compromising the performance of the true positives.

关键词： Hand Detection First Person vision Egocentric vision Video Processing

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A Spacecraft Dataset for Detection, Segmentation and Parts recognition

A Spacecraft Dataset for Detection, Segmentation and Parts R...

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

作者： Hoang Anh Dung Chen, Bo Chin, Tat-Jun Univ Adelaide Adelaide SA Australia

ISBN: (纸本)9781665448994

Virtually all aspects of modern life depend on space technology. Thanks to the great advancement of computer vision in general and deep learning-based techniques in particular, over the decades, the world witnessed the growing use of deep learning in solving problems for space applications, such as self-driving robot, tracers, insect-like robot on cosmos and health monitoring of spacecraft. These are just some prominent examples that has advanced space industry with the help of deep learning. However, the success of deep learning models requires a lot of training data in order to have decent performance, while on the other hand, there are very limited amount of publicly available space datasets for the training of deep learning models. Currently, there is no public datasets for space-based object detection or instance segmentation, partly because manually annotating object segmentation masks is very time consuming as they require pixel-level labelling, not to mention the challenge of obtaining images from space. In this paper, we aim to fill this gap by releasing a dataset for spacecraft detection, instance segmentation and part recognition. The main contribution of this work is the development of the dataset using images of space stations and satellites, with rich annotations including bounding boxes of spacecrafts and masks to the level of object parts, which are obtained with a mixture of automatic processes and manual efforts. We also provide evaluations with state-of-the-art methods in object detection and instance segmentation as a benchmark for the dataset. The link for downloading the proposed dataset can be found on https://***/Yurushia1998/SatelliteDataset.

关键词： Space vehicles Deep learning Image segmentation computer vision Satellites Service robots Object detection

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