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Class-Incremental Mixture of Gaussians for Deep Continual Learning

Class-Incremental Mixture of Gaussians for Deep Continual Le...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Lukasz Korycki Bartosz Krawczyk Virginia Commonwealth University Rochester Institute of Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Continual learning models for stationary data focus on learning and retaining concepts coming to them in a sequential manner. In the most generic class-incremental environment, we have to be ready to deal with classes coming one by one, without any higher-level grouping. This requirement invalidates many previously proposed methods and forces researchers to look for more flexible alternative approaches. In this work, we follow the idea of centroid-driven methods and propose end-to-end incorporation of the mixture of Gaussians model into the continual learning framework. By employing the gradient-based approach and designing losses capable of learning discriminative features while avoiding degenerate solutions, we successfully combine the mixture model with a deep feature extractor allowing for joint optimization and adjustments in the latent space. Additionally, we show that our model can effectively learn in memory-free scenarios with fixed extractors. In the conducted experiments, we empirically demonstrate the effectiveness of the proposed solutions and exhibit the competitiveness of our model when compared with state-of-the-art continual learning baselines evaluated in the context of image classification problems.

关键词： Continuing education computer vision conferences Mixture models Feature extraction Data models pattern recognition

来源：评论

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A Lightweight Spatiotemporal Network for Online Eye Tracking with Event Camera

A Lightweight Spatiotemporal Network for Online Eye Tracking...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Yan Ru Pei Sasskia Brüers Sébastien Crouzet Douglas McLelland Olivier Coenen Brainchip Inc. Laguna Hills CA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Event-based data are commonly encountered in edge computing environments where efficiency and low latency are critical. To interface with such data and leverage their rich temporal features, we propose a causal spatiotemporal convolutional network. This solution targets efficient implementation on edge-appropriate hardware with limited resources in three ways: 1) deliberately targets a simple architecture and set of operations (convolutions, ReLU activations) 2) can be configured to perform online inference efficiently via buffering of layer outputs 3) can achieve more than 90% activation sparsity through regularization during training, enabling very significant efficiency gains on event-based processors. In addition, we propose a general affine augmentation strategy acting directly on the events, which alleviates the problem of dataset scarcity for event-based systems. We apply our model on the AIS 2024 event-based eye tracking challenge, reaching a score of 0.9916 p10 accuracy on the Kaggle private testset.

关键词： Training Program processors Gaze tracking Streaming media Spatiotemporal phenomena pattern recognition Convolutional neural networks

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Delta Sampling R-BERT for limited data and low-light action recognition

Delta Sampling R-BERT for limited data and low-light action ...

引用

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

作者： Hira, Sanchit Das, Ritwik Modi, Abhinav Pakhomov, Daniil Johns Hopkins Univ Baltimore MD 21218 USA

ISBN: (纸本)9781665448994

We present an approach to perform supervised action recognition in the dark. In this work, we present our results on the ARID dataset[60]. Most previous works only evaluate performance on large, well illuminated datasets like Kinetics and HMDB51. We demonstrate that our work is able to achieve a very low error rate while being trained on a much smaller dataset of dark videos. We also explore a variety of training and inference strategies including domain transfer methodologies and also propose a simple but useful frame selection strategy. Our empirical results demonstrate that we beat previously published baseline models by 11%.

关键词： Training Deep learning Technological innovation Schedules computer vision conferences pattern recognition

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Separating lungs in CT scans for improved COVID19 detection

Separating lungs in CT scans for improved COVID19 detection

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Robert Turnbull Simon Mutch Melbourne Data Analytics Platform University of Melbourne Grattan St. Carlton VIC Australia

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper outlines our submission for the 4th COV19D competition as part of the ‘Domain adaptation, Explainability, Fairness in AI for Medical Image Analysis’ (DEFAI-MIA) workshop at the computer vision and pattern recognition conference (CVPR). The competition consists of two challenges. The first is to train a classifier to detect the presence of COVID-19 from over one thousand CT scans from the COV19-CT-DB database. The second challenge is to perform domain adaptation by taking the dataset from Challenge 1 and adding a small number of scans (some annotated and other not) for a different distribution. We preprocessed the CT scans to segment the lungs, and output volumes with the lungs individually and together. We then trained 3D ResNet and Swin Transformer models on these inputs. We annotated the unlabeled CT scans using an ensemble of these models and chose the high-confidence predictions as pseudo-labels for fine-tuning. This achieved the winning macro F1 score of 94.89% for Challenge 1 of the competition. It also achieved a second-best macro F1 score of 77.21% for Challenge 2.

关键词： COVID-19 Training computer vision Three-dimensional displays Computed tomography conferences Lung

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Towards Domain-Specific Explainable AI: Model Interpretation of a Skin Image Classifier using a Human Approach

Towards Domain-Specific Explainable AI: Model Interpretation...

引用

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

作者： Stieler, Fabian Rabe, Fabian Bauer, Bernhard Univ Augsburg Augsburg Germany

ISBN: (纸本)9781665448994

Machine Learning models have started to outperform medical experts in some classification tasks. Meanwhile, the question of how these classifiers produce certain results is attracting increasing research attention. Current interpretation methods provide a good starting point in investigating such questions, but they still massively lack the relation to the problem domain. In this work, we present how explanations of an AI system for skin image analysis can be made more domain-specific. We apply the synthesis of Local Interpretable Model-agnostic Explanations (LIME) with the ABCD-rule, a diagnostic approach of dermatologists, and present the results using a Deep Neural Network (DNN) based skin image classifier.

关键词： Deep learning computer vision Image analysis Computational modeling conferences Skin pattern recognition

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Enhancing 2D Representation Learning with a 3D Prior

Enhancing 2D Representation Learning with a 3D Prior

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Mehmet Aygün Prithviraj Dhar Zhicheng Yan Oisin Mac Aodha Rakesh Ranjan University of Edinburgh Meta Inc.

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Learning robust and effective representations of visual data is a fundamental task in computer vision. Traditionally, this is achieved by training models with labeled data which can be expensive to obtain. Self-supervised learning attempts to circumvent the requirement for labeled data by learning representations from raw unlabeled visual data alone. However, unlike humans who obtain rich 3D information from their binocular vision and through motion, the majority of current self-supervised methods are tasked with learning from monocular 2D image collections. This is noteworthy as it has been demonstrated that shape-centric visual processing is more robust compared to texture-biased automated methods. Inspired by this, we propose a new approach for strengthening existing self-supervised methods by explicitly enforcing a strong 3D structural prior directly into the model during training. Through experiments, across a range of datasets, we demonstrate that our 3D aware representations are more robust compared to conventional self-supervised baselines.

关键词： Training Representation learning Visualization computer vision Solid modeling Three-dimensional displays Shape

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Estimating (and fixing) the Effect of Face Obfuscation in Video recognition

Estimating (and <i>fixing</i>) the Effect of Face Obfuscatio...

引用

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

作者： Tomei, Matteo Baraldi, Lorenzo Bronzin, Simone Cucchiara, Rita Univ Modena & Reggio Emilia Modena Italy Metaliquid Srl Milan Italy

ISBN: (纸本)9781665448994

Recent research has shown that faces can be obfuscated in large-scale datasets with a minimal performance impact on image classification and downstream tasks like object recognition. In this paper, we investigate the role of face obfuscation in video classification datasets and quantify a more significant reduction in performance caused by face blurring. To reduce such performance effects, we propose a generalized distillation approach in which a privacy-preserving action recognition network is trained with privileged information given by face identities. We show, through experiments performed on Kinetics-400, that the proposed approach can fully close the performance gap caused by face anonymization.

关键词： Visualization computer vision Face recognition conferences Computational modeling Pipelines Object recognition

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The Myth of the Pyramid

The Myth of the Pyramid

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Ramon Izquierdo-Cordova Walterio Mayol-Cuevas Department of Computer Science University of Bristol United Kingdom

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

A deep-rooted strategy for building convolutional neural networks in computer vision is to increase the number of filters every time the feature map resolution is decreased. The notion ruling this pyramidal design is that the expressivity of the network increases with a higher number of filters to compensate for losses caused for lower resolutions. This paper challenges the practice by testing a set of variate distribution of filters, named filter templates, on popular CNN architectures (VGG, ResNet, MobileNet and MnasNet). The experimental results show that the superiority of the pyramidal design holds on the ImageNet dataset but fails for other datasets such as MNIST, CIFAR and TinyImageNet, and for other tasks such as audio classification. CNN models with different filter distributions deliver higher accuracy with reduced resource consumption suggesting the pyramidal design has been optimised to Imagenet and that each model-dataset pair benefits from tuning the number and distribution of filters. To further illustrate the benefits of exploring other distributions, this paper shows that the best performing model from the NASBench101 dataset can increase its accuracy over the original pyramidal design with reductions of parameters up to 68 per cent by using templates. Overall, our experiments point to new opportunities for model designers to find more efficient models.

关键词： computer vision Filters Accuracy Image resolution Computational modeling conferences pattern recognition

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Learning Optimized Low-Light Image Enhancement for Edge vision Tasks

Learning Optimized Low-Light Image Enhancement for Edge Visi...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： S M A Sharif Azamat Myrzabekov Nodirkhuja Khujaev Roman Tsoy Seongwan Kim Jaeho Lee Opt-AI Inc. LG Sciencepark Seoul South Korea

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Low-light image enhancement (LLIE) has a significant role in edge vision applications (EVA). Despite its widespread practicability, the existing LLIE methods are impractical due to their high computational costs. This study proposed a framework to learn optimized low-light image enhancement to tackle the limitations of existing enhancement methods for accelerating EVA. The proposed framework incorporates a lightweight and mobile-friendly deep network. We optimized our proposed model with INT8 precision with a post-training quantization strategy and deployed it on an edge device. The LLIE model has achieved over 199 frames per second (FPS) on a low-power edge board. Additionally, we evaluated the practicability of an optimized model for accelerating the vision application of an edge environment. The experimental results illustrate that our optimized method can significantly accelerate the performance of SOTA vision algorithms in challenging low-light conditions for numerous everyday vision tasks, including object detection and image registration.

关键词： Performance evaluation YOLO Training Image registration Quantization (signal) Image edge detection Hardware

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MDMMT: Multidomain Multimodal Transformer for Video Retrieval

MDMMT: Multidomain Multimodal Transformer for Video Retrieva...

引用

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

作者： Dzabraev, Maksim Kalashnikov, Maksim Komkov, Stepan Petiushko, Aleksandr Lomonosov Moscow State Univ Moscow Russia Huawei Moscow Res Ctr Moscow Russia

ISBN: (纸本)9781665448994

We present a new state-of-the-art on the text-to-video retrieval task on MSRVTT and LSMDC benchmarks where our model outperforms all previous solutions by a large margin. Moreover, state-of-the-art results are achieved using a single model and without finetuning. This multidomain generalisation is achieved by a proper combination of different video caption datasets. We show that our practical approach for training on different datasets can improve test results of each other. Additionally, we check intersection between many popular datasets and show that MSRVTT as well as ActivityNet contains a significant overlap between the test and the training parts. More details are available at https://***/papermsucode/mdmmt.

关键词： Training computer vision conferences Benchmark testing pattern recognition Task analysis

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