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Class-Incremental Learning with Generative Classifiers

Class-Incremental Learning with Generative Classifiers

引用

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

作者： van de Ven, Gido M. Li, Zhe Tolias, Andreas S. Baylor Coll Med Ctr Neurosci & Artificial Intelligence Houston TX 77030 USA Univ Cambridge Computat & Biol Learning Lab Cambridge England Rice Univ Dept Elect & Comp Engn POB 1892 Houston TX 77251 USA

ISBN: (纸本)9781665448994

Incrementally training deep neural networks to recognize new classes is a challenging problem. Most existing class-incremental learning methods store data or use generative replay, both of which have drawbacks, while 'rehearsal-free' alternatives such as parameter regularization or bias-correction methods do not consistently achieve high performance. Here, we put forward a new strategy for class-incremental learning: generative classification. Rather than directly learning the conditional distribution p(y\x), our proposal is to learn the joint distribution p(x, y), factorized as p(x\y)p(y), and to perform classification using Bayes' rule. As a proof-of-principle, here we implement this strategy by training a variational autoencoder for each class to be learned and by using importance sampling to estimate the likelihoods p(x\y). This simple approach performs very well on a diverse set of continual learning benchmarks, outperforming generative replay and other existing baselines that do not store data.

关键词： Training Learning systems Deep learning computer vision Monte Carlo methods conferences Benchmark testing

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Hierarchical Spatial Pyramid Network For Cervical Precancerous Segmentation By Reconstructing Deep Segmentation Networks

Hierarchical Spatial Pyramid Network For Cervical Precancero...

引用

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

作者： Meng, Zhu Zhao, Zhicheng Su, Fei Guo, Limei Beijing Univ Posts & Telecommun Beijing Peoples R China Beijing Key Lab Network Syst & Network Culture Beijing Peoples R China Peking Univ Hosp 3 Hlth Sci Ctr Beijing Peoples R China

ISBN: (纸本)9781665448994

Cervical cancer is one of the leading causes of cancer death in women aged 20 to 39 years, which emphasizes the importance of cervical precancerous diagnosis and treatment. Although there are many attempts on medical image processing, the research on the automatic diagnosis of cervical precancerous pathology is still scarce. In this paper, a challenging end-to-end automatic segmentation task for cervical precancerous diagnosis is focused. Specifically, considering that the diagnosis of cervical lesions relies heavily on spatial information, a hierarchical spatial pyramid network (HSP-Net) is proposed to enhance the representation ability of cervical structural features. First, a vertical hierarchical spatial pyramid (V-HSP) network is devised to aggregate the multiscale information during the feature extraction of the encoder. Second, a horizontal hierarchical spatial pyramid (H-HSP) network is designed to fuse information of multiscale receptive fields before and after cascading features from different branches. Experiments on the public dataset MTCHI demonstrate that HSP-Net achieves the state-of-the-art performance, reflecting the potential to assist doctors and patients clinically.

关键词： Pathology Image segmentation Fuses conferences Medical services Feature extraction pattern recognition

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A Robust MTMC Tracking System for AI-City Challenge 2021

A Robust MTMC Tracking System for AI-City Challenge 2021

引用

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

作者： Ye, Jin Yang, Xipeng Kang, Shuai He, Yue Zhang, Weiming Huang, Leping Jiang, Minyue Zhang, Wei Shi, Yifeng Xia, Meng Tan, Xiao Baidu Inc Dept Comp Vis Technol VIS Beijing Peoples R China

ISBN: (纸本)9781665448994

Multi-Target Multi-Camera tracking (MTMC) is an essential task in the intelligent city and traffic analysis. It is a great challenging task due to several problems such as heavy occlusions and appearance variance caused by various camera perspectives and congested vehicles. In this paper, we propose a practical framework for dealing with the MTMC problem. The proposed framework contains three stage. Firstly, in the vehicles detection and Re-ID stage, the proposed system leverages Cascade R-CNN to detect all vehicles and extract appearance features with a Re-ID module for all cameras. Secondly, in the Multi-Target Single-Camera tracking (MTSC) stage, on the basis of the detected boxes and appearance features, it tracks multiple vehicles to generate candidate trajectories within each single camera with Tracklet-Plane Matching (TPM) tracking algorithm. Finally, in the Inter-Camera Association (ICA) stage, it associates all candidate trajectories between two successive cameras using the established distance matrix, and combines all successively matching results for final submission. The established distance matrix is simply computed by the Re-ID features and refined by the constraints of traveling time, road structures, and traffic rules to accelerate matching time as well as reduce search space. Extensive experiments on the public track3 test set of NVIDIA AI CITY 2021 CHALLENGE demonstrate the effectiveness of our method, which achieves IDF1 of 77.87%.

关键词： Roads Urban areas Cameras Feature extraction Trajectory Space exploration pattern recognition

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Localized Latent Updates for Fine-Tuning vision-Language Models

Localized Latent Updates for Fine-Tuning Vision-Language Mod...

引用

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

作者： Moritz Ibing Isaak Lim Leif Kobbelt Visual Computing Institute RWTH Aachen University

Although massive pre-trained vision-language models like CLIP show impressive generalization capabilities for many tasks, still it often remains necessary to fine-tune them for improved performance on specific datasets. When doing so, it is desirable that updating the model is fast and that the model does not lose its capabilities on data outside of the dataset, as is often the case with classical fine-tuning approaches. In this work we suggest a lightweight adapter that only updates the models predictions close to seen datapoints. We demonstrate the effectiveness and speed of this relatively simple approach in the context of few-shot learning, where our results both on classes seen and unseen during training are comparable with or improve on the state of the art.

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Augmented Self-Mask Attention Transformer for Naturalistic Driving Action recognition

Augmented Self-Mask Attention Transformer for Naturalistic D...

引用

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

作者： Tiantian Zhang Qingtian Wang Xiaodong Dong Wenqing Yu Hao Sun Xuyang Zhou Aigong Zhen Shun Cui Dong Wu Zhongjiang He China Telecom Artificial Intelligence Technology (Beijing) Co. Ltd.

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Nowadays, naturalistic driving action recognition and computer vision techniques provide crucial solutions to identify and eliminate distracting driving behavior. Existing methods often extract features through fixed-size sliding windows and predict an action’s start and end time. However, the information about a fixed-size window may be incomplete or redundant and the connections between different windows are insufficient. To alleviate this problem, we propose a novel Augmented Self-Mask Attention (AMA) architecture that enables learning bidirectional contexts by maximizing the expected likelihood over all permutations of the factorization order. We employ an ensemble technique and use a weighted boundaries fusion to combine and refine predictions with high confidence scores action boundaries. On the test dataset of AI City Challenge 2024 Track3, we achieved significant results compared with other teams, the proposed model ranks first on the public leaderboard of the challenge. Codes are available at https://***/wolfworld6/AIcity2024-track3.

关键词： computer vision conferences Computational modeling Urban areas computer architecture Feature extraction Transformers

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Assessment of deep learning based blood pressure prediction from PPG and rPPG signals

Assessment of deep learning based blood pressure prediction ...

引用

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

作者： Schrumpf, Fabian Frenzel, Patrick Aust, Christoph Osterhoff, Georg Fuchs, Mirco Leipzig Univ Appl Sci Leipzig Germany Univ Hosp Leipzig Leipzig Germany

ISBN: (纸本)9781665448994

Exploiting photoplethysmography signals (PPG) for non-invasive blood pressure (BP) measurement is interesting for various reasons. First, PPG can easily be measured using fingerclip sensors. Second, camera-based approaches allow to derive remote PPG (rPPG) signals similar to PPG and therefore provide the opportunity for non-invasive measurements of BP. Various methods relying on machine learning techniques have recently been published. Performances are often reported as the mean average error (MAE) on the data which is problematic. This work aims to analyze the PPG- and rPPG-based BP prediction error with respect to the underlying data distribution. First, we train established neural network (NN) architectures and derive an appropriate parameterization of input segments drawn from continuous PPG signals. Second, we apply this parameterization to a larger PPG dataset and train NNs to predict BP. The resulting prediction errors increase towards less frequent BP values. Third, we use transfer learning to train the NNs for rPPG based BP prediction. The resulting performances are similar to the PPG-only case. Finally, we apply a personalization technique and retrain our NNs with subject-specific data. This slightly reduces the prediction errors.

关键词： Deep learning computer vision conferences Transfer learning Artificial neural networks Photoplethysmography Blood pressure

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FReTAL: Generalizing Deepfake Detection using Knowledge Distillation and Representation Learning

FReTAL: Generalizing Deepfake Detection using Knowledge Dist...

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

作者： Kim, Minha Tariq, Shahroz Woo, Simon S. Sungkyunkwan Univ Coll Comp & Informat Seoul South Korea Sungkyunkwan Univ Dept Appl Data Sci Seoul South Korea

ISBN: (纸本)9781665448994

As GAN-based video and image manipulation technologies become more sophisticated and easily accessible, there is an urgent need for effective deepfake detection technologies. Moreover, various deepfake generation techniques have emerged over the past few years. While many deepfake detection methods have been proposed, their performance suffers from new types of deepfake methods on which they are not sufficiently trained. To detect new types of deepfakes, the model should learn from additional data without losing its prior knowledge about deepfakes (catastrophic forgetting), especially when new deepfakes are significantly different. In this work, we employ the Representation Learning (ReL) and Knowledge Distillation (KD) paradigms to introduce a transfer learning-based Feature Representation Transfer Adaptation Learning (FReTAL) method. We use FReTAL to perform domain adaptation tasks on new deepfake datasets, while minimizing the catastrophic forgetting. Our student model can quickly adapt to new types of deepfake by distilling knowledge from a pre-trained teacher model and applying transfer learning without using source domain data during domain adaptation. Through experiments on FaceForensics++ datasets, we demonstrate that FReTAL outperforms all baselines on the domain adaptation task with up to 86.97% accuracy on low-quality deepfakes.

关键词： Adaptation models computer vision conferences Computational modeling Transfer learning Data models pattern recognition

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Generalized Unsupervised Clustering of Hyperspectral Images of Geological Targets in the Near Infrared

Generalized Unsupervised Clustering of Hyperspectral Images ...

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

作者： Gao, Angela F. Rasmussen, Brandon Kulits, Peter Scheller, Eva L. Greenberger, Rebecca Ehlmann, Bethany L. CALTECH Pasadena CA 91125 USA

ISBN: (纸本)9781665448994

The application of infrared hyperspectral imagery to geological problems is becoming more popular as data become more accessible and cost-effective. Clustering and classifying spectrally similar materials is often a first step in applications ranging from economic mineral exploration on Earth to planetary exploration on Mars. Semi-manual classification guided by expertly developed spectral parameters can be time consuming and biased, while supervised methods require abundant labeled data and can be difficult to generalize. Here we develop a fully unsupervised workflow for feature extraction and clustering informed by both expert spectral geologist input and quantitative metrics. Our pipeline uses a lightweight autoencoder followed by Gaussian mixture modeling to map the spectral diversity within any image. We validate the performance of our pipeline at submillimeter-scale with expert-labelled data from the Oman ophiolite drill core and evaluate performance at meters-scale with partially classified orbital data of Jezero Crater on Mars (the landing site for the Perseverance rover). We additionally examine the effects of various preprocessing techniques used in traditional analysis of hyperspectral imagery. This pipeline provides a fast and accurate clustering map of similar geological materials and consistently identifies and separates major mineral classes in both laboratory imagery and remote sensing imagery. We refer to our pipeline as "Generalized Pipeline for Spectroscopic Unsupervised clustering of Minerals (GyPSUM)."

关键词： Mars Pipelines Memory management Semisupervised learning Feature extraction Minerals pattern recognition

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Im2Vec: Synthesizing Vector Graphics without Vector Supervision

Im2Vec: Synthesizing Vector Graphics without Vector Supervis...

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

作者： Reddy, Pradyumna Gharbi, Michael Lukac, Michal Mitra, Niloy J. UCL London England Adobe Res San Jose CA USA

ISBN: (纸本)9781665448994

Vector graphics are widely used to represent fonts, logos, digital artworks, and graphic designs. But, while a vast body of work has focused on generative algorithms for raster images, only a handful of options exists for vector graphics. One can always rasterize the input graphic and resort to image-based generative approaches, but this negates the advantages of the vector representation. The current alternative is to use specialized models that require explicit supervision on the vector graphics representation at training time. This is not ideal because large-scale high-quality vector-graphics datasets are difficult to obtain. Furthermore, the vector representation for a given design is not unique, so models that supervise on the vector representation are unnecessarily constrained. Instead, we propose a new neural network that can generate complex vector graphics with varying topologies, and only requires indirect supervision from readily-available raster training images (i.e., with no vector counterparts). To enable this, we use a differentiable rasterization pipeline that renders the generated vector shapes and composites them together onto a raster canvas. We demonstrate our method on a range of datasets, and provide comparison with state-of-the-art SVG-VAE and DeepSVG, both of which require explicit vector graphics supervision. Finally, we also demonstrate our approach on the MNIST dataset, for which no groundtruth vector representation is available. Source code, datasets and more results are available at http://***/projects/2021/Im2Vec/.

关键词： Training Interpolation computer vision Shape Network topology Pipelines Neural networks

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Toward Interactive Modulation for Photo-Realistic Image Restoration

Toward Interactive Modulation for Photo-Realistic Image Rest...

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

作者： Cai, Haoming He, Jingwen Qiao, Yu Dong, Chao Chinese Acad Sci Shenzhen Inst Adv Technol Key Lab Human Machine Intelligence Synergy Syst Shenzhen Peoples R China Shanghai AI Lab Shanghai Peoples R China Shenzhen Inst Artificial Intelligence & Robot Soc SIAT Branch Shenzhen Peoples R China

ISBN: (纸本)9781665448994

Modulating image restoration level aims to generate a restored image by altering a factor that represents the restoration strength. Previous works mainly focused on optimizing the mean squared reconstruction error, which brings high reconstruction accuracy but lacks finer texture details. This paper presents a Controllable Unet Generative Adversarial Network (CUGAN) to generate high frequency textures in the modulation tasks. CUGAN consists of two modules - base networks and condition networks. The base networks comprise a generator and a discriminator. In the generator, we realize the interactive control of restoration levels by tuning the weights of different features from different scales in the Unet architecture. Moreover, we adaptively modulate the intermediate features in the discriminator according to the severity of degradations. The condition networks accept the condition vector (encoded degradation information) as input, then generate modulation parameters for both the generator and the discriminator. During testing, users can control the output effects by tweaking the condition vector. We also provide a smooth transition between GAN and MSE effects by a simple transition method. Extensive experiments demonstrate that the proposed CUGAN achieves excellent performance on image restoration modulation tasks.

关键词： Degradation Modulation Generative adversarial networks Generators Image restoration pattern recognition Task analysis

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