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AuxMix: Semi-Supervised Learning with Unconstrained Unlabeled Data

AuxMix: Semi-Supervised Learning with Unconstrained Unlabele...

引用

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

作者： Banitalebi-Dehkordi, Amin Gujjar, Pratik Zhang, Yong Huawei Technol Canada Co Ltd Vancouver BC Canada

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Semi-supervised learning (SSL) has seen great strides when labeled data is scarce but unlabeled data is abundant. Critically, most recent work assume that such unlabeled data is drawn from the same distribution as the labeled data. In this work, we show that state-of-the-art SSL algorithms suffer a degradation in performance in the presence of unlabeled auxiliary data that does not necessarily possess the same class distribution as the labeled set. We term this problem as Auxiliary-SSL and propose AuxMix, an algorithm that leverages self-supervised learning tasks to learn generic features in order to mask auxiliary data that are not semantically similar to the labeled set. We also propose to regularize learning by maximizing the predicted entropy for dissimilar auxiliary samples. We show an improvement of 5% over existing baselines on a ResNet-50 model when trained on CIFAR10 dataset with 4k labeled samples and all unlabeled data is drawn from the Tiny-Imagenet dataset. We report competitive results on several datasets and conduct ablation studies.

关键词： Degradation computer vision conferences Self-supervised learning Semisupervised learning Prediction algorithms Entropy

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Poisons that are learned faster are more effective

Poisons that are learned faster are more effective

引用

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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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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On-Sensor Binarized Fully Convolutional Neural Network for Localisation and Coarse Segmentation

On-Sensor Binarized Fully Convolutional Neural Network for L...

引用

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

作者： Liu, Yanan Lu, Yao Shanghai Univ Sch Microelect Shanghai Peoples R China Univ Bristol Visual Informat Lab Bristol Avon England

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Current neural networks are compatible with high-performance GPU/CPUs. However, implementing neural networks on emerging embedded sensor for inference is challenging due to sensor's unique hardware architecture and stringent computing resources. With this in mind, this work presents new methods to implement fully convolutional neural networks (FCNs) on Pixel Processor Array (PPA) sensors with many techniques to fully use the limited resources on sensor. Specifically, we, for the first time, design and train binarized FCN for both binary weights and activations using batchnorm, group convolution, and learnable threshold for binarization, producing networks small enough to be embedded on the focal plane of the PPA, with limited local memory resources, and using parallel elementary add/subtract, shifting, and bit operations only. We demonstrate the first implementation of an FCN on a PPA device, performing three convolution layers entirely in the pixel-level processors. We use this architecture to demonstrate inference generating heat maps for object segmentation and localisation at over 280 FPS using the SCAMP-5 PPA vision chip.

关键词： Performance evaluation Heating systems Convolution Neural networks computer architecture Object segmentation Parallel processing

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Asymmetric Information Distillation Network for Lightweight Super Resolution

Asymmetric Information Distillation Network for Lightweight ...

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

作者： Zong, Zhikai Zha, Lin Jiang, Jiande Liu, Xiaoxiao Qingdao Hiimage Technol Co Ltd Hisense Visual Technol Co Ltd Qingdao Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The purpose of this paper is to design a lightweight network to achieve image super resolution performance equivalent to SRResNet. We design an asymmetric information distillation block (AIDB) with distillation information multiplexing and asymmetric information extraction capabilities to better achieve this goal. Distillation information multiplexing refers to the repeated processing of distilled information to supplement the ability of key information extraction. Asymmetric information enhancement block (AIEB) refers to identify different features in the image by the horizontal and vertical feature extraction. AIEB greatly reduces the number of parameters, and distillation information multiplexing works as a supplement to the lost high dimensional information. A large number of experiments show that our asymmetric information distillation network (AIDN) achieves a better balance of performance and complexity than SOTA model. Moreover, Our proposed AIDN ranked second in the model complexity track of NTIRE2022 efficient super resolution challenge. Compared with the first place in this track, we achieves higher PSNR performance on testset with a slight disadvantage in the number of parameters. The code is available at https://***/zzksdu/AIDN.

关键词： Multiplexing Measurement computer vision Image resolution Computational modeling conferences Feature extraction

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Spacing Loss for Discovering Novel Categories

Spacing Loss for Discovering Novel Categories

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

作者： Joseph, K. J. Paul, Sujoy Aggarwal, Gaurav Biswas, Soma Rai, Piyush Han, Kai Balasubramanian, Vineeth N. Indian Inst Technol Hyderabad Hyderabad India Google Res Bengaluru India Indian Inst Sci Bengaluru India Lndian Inst Technol Kanpur Kanpur Uttar Pradesh India Univ Hong Kong Hong Kong Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Novel Class Discovery (NCD) is a learning paradigm, where a machine learning model is tasked to semantically group instances from unlabeled data, by utilizing labeled instances from a disjoint set of classes. In this work, we first characterize existing NCD approaches into single-stage and two-stage methods based on whether they require access to labeled and unlabeled data together while discovering new classes. Next, we devise a simple yet powerful loss function that enforces separability in the latent space using cues from multi-dimensional scaling, which we refer to as Spacing Loss. Our proposed formulation can either operate as a standalone method or can be plugged into existing methods to enhance them. We validate the efficacy of Spacing Loss with thorough experimental evaluation across multiple settings on CIFAR-10 and CIFAR-100 datasets.

关键词： Training computer vision conferences Computational modeling Machine learning Benchmark testing Data models

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Semi-Supervised Training to Improve Player and Ball Detection in Soccer

Semi-Supervised Training to Improve Player and Ball Detectio...

引用

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

作者： Vandeghen, Renaud Cioppa, Anthony Van Droogenbroeck, Marc Univ Liege Liege Belgium

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Accurate player and ball detection has become increasingly important in recent years for sport analytics. As most state-of-the-art methods rely on training deep learning networks in a supervised fashion, they require huge amounts of annotated data, which are rarely available. In this paper, we present a novel generic semi-supervised method to train a network based on a labeled image dataset by leveraging a large unlabeled dataset of soccer broadcast videos. More precisely, we design a teacher-student approach in which the teacher produces surrogate annotations on the unlabeled data to be used later for training a student which has the same architecture as the teacher. Furthermore, we introduce three training loss parametrizations that allow the student to doubt the predictions of the teacher during training depending on the proposal confidence score. We show that including unlabeled data in the training process allows to substantially improve the performances of the detection network trained only on the labeled data. Finally, we provide a thorough performance study including different proportions of labeled and unlabeled data, and establish the first benchmark on the new SoccerNet-v3 detection task, with an mAP of 52.3%.

关键词： Training Adaptation models Annotations Benchmark testing Data models pattern recognition Proposals

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Multiple Object Detection and Tracking in the Thermal Spectrum

Multiple Object Detection and Tracking in the Thermal Spectr...

引用

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

作者： El Ahmar, Wassim A. Kolhatkar, Dhanvin Nowruzi, Farzan Erlik AlGhamdi, Hamzah Hou, Jonathan Laganiere, Robert Univ Ottawa Ottawa ON Canada Sensor Cortek Inc Ottawa ON Canada Pleora Technol Kanata ON Canada

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Multiple Object Tracking (MOT) is an integral part of machine vision research. Most tracking-by-detection based MOT solutions utilize video streams from RGB cameras for their operation. However, for real-world applications, it is necessary to utilize sensors that operate in different spectrums to accommodate for varying lighting conditions. Since object detection is the first step of the tracking pipeline in tracking-by-detection approaches, we compare the performance of state-of-the-art object detectors when trained on color images to their performance when trained on thermal images. We introduce a new dataset for multiple object tracking with thermal images and corresponding RGB images and show that state-of-the-art trackers perform better on thermal images, especially in poor lighting conditions. Finally, we propose the use of a dynamic cut-off threshold for tracking-by-detection approaches that factors the size of a predicted box to enhance the tracker association. Our dataset and source code are publicly available at https://***/wassimea/thermalMOT

关键词： Machine vision conferences Pipelines Lighting Object detection Detectors Streaming media

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GraphWalks: Efficient Shape Agnostic Geodesic Shortest Path Estimation

GraphWalks: Efficient Shape Agnostic Geodesic Shortest Path ...

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

作者： Potamias, Rolandos Alexandros Neofytou, Alexandros Bintsi, Kyriaki Margarita Zafeiriou, Stefanos Imperial Coll London London England OCM Digital Media Athens Greece

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Geodesic paths and distances are among the most popular intrinsic properties of 3D surfaces. Traditionally, geodesic paths on discrete polygon surfaces were computed using shortest path algorithms, such as Dijkstra. However, such algorithms have two major limitations. They are non-differentiable which limits their direct usage in learnable pipelines and they are considerably time demanding. To address such limitations and alleviate the computational burden, we propose a learnable network to approximate geodesic paths. The proposed method is comprised by three major components: a graph neural network that encodes node positions in a high dimensional space, a path embedding that describes previously visited nodes and a point classifier that selects the next point in the path. The proposed method provides efficient approximations of the shortest paths and geodesic distances estimations. Given that all of the components of our method are fully differentiable, it can be directly plugged into any learnable pipeline as well as customized under any differentiable constraint. We extensively evaluate the proposed method with several qualitative and quantitative experiments.

关键词： computer vision Three-dimensional displays Shape conferences Pipelines Estimation Approximation algorithms

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Linear Combination Approximation of Feature for Channel Pruning

Linear Combination Approximation of Feature for Channel Prun...

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

作者： Joo, Donggyu Kim, Doyeon Yi, Eojindl Kim, Junmo Korea Adv Inst Sci & Technol KAIST Daejeon South Korea

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Network pruning is an effective method that reduces the computation of neural networks while maintaining high performance. This enables the operation of deep neural networks in resource-limited environments. In a general large network, the roles of each channel often inevitably overlap with those of others. Therefore, for more effective pruning, it is important to observe the correlation between features in the network. In this paper, we propose a novel channel pruning method, namely, the linear combination approximation of features (LCAF). We approximate each feature map by a linear combination of other feature maps in the same layer, and then remove the most approximated one. Additionally, by exploiting the linearity of the convolution operation, we propose a supporting method called weight modification, to further reduce the loss change that occurs during pruning. Extensive experiments show that LCAF achieves state-of-the-art performance in several benchmarks. Furthermore, ablations on the LCAF demonstrate the effectiveness of our approach in a variety of ways.

关键词： Deep learning computer vision Correlation Convolution conferences Neural networks Linearity

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