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Dataset Distillation by Matching Training Trajectories

Dataset Distillation by Matching Training Trajectories

引用

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

作者： Cazenavette, George Wang, Tongzhou Torralba, Antonio Efros, Alexei A. Zhu, Jun-Yan Carnegie Mellon Univ Pittsburgh PA 15213 USA MIT Cambridge MA 02139 USA Univ Calif Berkeley Berkeley CA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Dataset distillation is the task of synthesizing a small dataset such that a model trained on the synthetic set will match the test accuracy of the model trained on the full dataset. In this paper, we propose a new formulation that optimizes our distilled data to guide networks to a similar state as those trained on real data across many training steps. Given a network, we train it for several iterations on our distilled data and optimize the distilled data with respect to the distance between the synthetically trained parameters and the parameters trained on real data. To efficiently obtain the initial and target network parameters for large-scale datasets, we pre-compute and store training trajectories of expert networks trained on the real dataset. Our method handily outperforms existing methods and also allows us to distill higher-resolution visual data.

关键词： Training Visualization computer vision conferences Trajectory Task analysis pattern matching

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DeiT-LT: Distillation Strikes Back for vision Transformer Training on Long-Tailed Datasets

DeiT-LT: Distillation Strikes Back for Vision Transformer Tr...

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

作者： Rangwani, Harsh Mondal, Pradipto Mishra, Mayank Asokan, Ashish Ramayee Babu, R. Venkatesh Indian Inst Sci Bangalore Karnataka India Indian Inst Technol Kharagpur W Bengal India

ISBN: (纸本)9798350353006

vision Transformer (ViT) has emerged as a prominent architecture for various computer vision tasks. In ViT, we divide the input image into patch tokens and process them through a stack of self-attention blocks. However, unlike Convolutional Neural Network (CNN), ViT's simple architecture has no informative inductive bias (e.g., locality, etc.). Due to this, ViT requires a large amount of data for pre-training. Various data-efficient approaches (DeiT) have been proposed to train ViT on balanced datasets effectively. However, limited literature discusses the use of ViT for datasets with long-tailed imbalances. In this work, we introduce DeiT-LT to tackle the problem of training ViTs from scratch on long-tailed datasets. In DeiT-LT, we introduce an efficient and effective way of distillation from CNN via distillation DIST token by using out-of-distribution images and re-weighting the distillation loss to enhance focus on tail classes. This leads to the learning of local CNN-like features in early ViT blocks, improving generalization for tail classes. Further, to mitigate overfitting, we propose distilling from a flat CNN teacher, which leads to learning low-rank generalizable features for DIST tokens across all ViT blocks. With the proposed DeiT-LT scheme, the distillation DIST token becomes an expert on the tail classes, and the classifier CLS token becomes an expert on the head classes. The experts help to effectively learn features corresponding to both the majority and minority classes using a distinct set of tokens within the same ViT architecture. We show the effectiveness of DeiT-LT for training ViT from scratch on datasets ranging from small-scale CIFAR-10 LT to large-scale iNaturalist-2018. Project Page: https://***/DeiT-LT.

关键词： distillation long-tail-learning vision transformers vit

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Learned Compression of High Dimensional Image Datasets

Learned Compression of High Dimensional Image Datasets

引用

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

作者： Cole, Elizabeth Meng, Qingxi Pauly, John Vasanawala, Shreyas Stanford Univ Dept Elect Engn Stanford CA 94305 USA Stanford Univ Dept Radiol Stanford CA 94305 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In many applications, such as burst photography and magnetic resonance imaging (MRI), multiple images are acquired to reduce the noise of the eventual reconstructed image. However, this leads to very high dimensional datasets which have redundant information across the various acquired images. In MRI, multiple images are acquired via multiple RF coil arrays in the scanner. Afterwards, coil compression is performed to convert the original set of coil images into a smaller set of virtual coil images to enable smaller datasets and faster computation time. However, traditional iterative coil compression methods are lossy and time-consuming. In this work, we propose a novel neural network-based coil compression method in pursuit of higher reconstruction accuracy and faster coil compression. Our learned compression method achieves up to 1.5x lower NRMSE and up to 10 times runtime speed compared to traditional methods on a benchmark test dataset.

关键词： Radio frequency Photography Image coding Runtime Magnetic resonance imaging conferences pattern recognition

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SOCAR: Socially-Obtained CAR Dataset for Image recognition in the Wild

SOCAR: Socially-Obtained CAR Dataset for Image Recognition i...

引用

23rd ieee/CVF Winter conference on Applications of computer vision (WACV)

作者： Seo, Taewon Park, Kyung Ho Chung, Hyunhee SOCAR AI Res Seoul South Korea

ISBN: (纸本)9798350320565

While cars have become a significant object in computer vision applications, there are fewer spotlights on publicly-available car-related datasets. Among previously-proposed car datasets, we discover several improvement avenues. As most previous car datasets consist of web-crawled or surveillance camera-taken images, they are insufficient to illustrate various attributes, such as points of view or parts. Moreover, prior datasets primarily deal with a car model recognition task;thus, the scope of applicative studies was limited. To improve these avenues, we propose a Socially-Obtained CAR (SOCAR) dataset, a real-world car image dataset consisting of car images with more prosperous attributes. The key contributions of our study are as follows. First, under coordination with a large-scale car-sharing platform, we retrieve user-taken car images on both external and internal attributes and establish a dataset consisting of 10K images on 14 classes. Second, we design each class to represent a particular car's state;therefore, the SOCAR dataset enables the practitioners to solve various image recognition tasks such as understanding defects, dirt, or car wash. Lastly, we suggest baseline experiment results on the proposed dataset and experimentally examine the trained model effectively capture discriminative regions similar to human vision. We highly expect practitioners to use our SOCAR dataset for academic research on understanding car attributes or computer vision applications.

关键词： computer vision Image recognition Computational modeling Surveillance conferences Reproducibility of results Automobiles

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Super-Resolution Reconstruction from Bayer-pattern Spike Streams

Super-Resolution Reconstruction from Bayer-Pattern Spike Str...

引用

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

作者： Dong, Yanchen Xiong, Ruiqin Zhang, Jian Yu, Zhaofei Fan, Xiaopeng Zhu, Shuyuan Huang, Tiejun Peking Univ Sch Comp Sci Beijing Peoples R China Peking Univ Sch Elect & Comp Engn Beijing Peoples R China Peking Univ Inst Artificial Intelligence Beijing Peoples R China Harbin Inst Technol Sch Comp Sci & Technol Harbin Peoples R China Univ Elect Sci & Technol China Chengdu Peoples R China

ISBN: (纸本)9798350353006

Spike camera is a neuromorphic vision sensor that can capture highly dynamic scenes by generating a continuous stream of binary spikes to represent the arrival of photons at very high temporal resolution. Equipped with Bayer color filter array (CFA), color spike camera (CSC) has been invented to capture color information. Although spike camera has already demonstrated great potential for high-speed imaging, its spatial resolution is limited compared with conventional digital cameras. This paper proposes a Color Spike Camera Super-Resolution (CSCSR) network to super-resolve higher-resolution color images from spike camera streams with Bayer CFA. To be specific, we first propose a representation for Bayer-pattern spike streams, exploring local temporal information with global perception to represent the binary data. Then we exploit the CFA layout and sub-pixel level motion to collect temporal pixels for the spatial super-resolution of each color channel. In particular, a residual-based module for feature refinement is developed to reduce the impact of motion estimation errors. Considering color correlation, we jointly utilize the multi-stage temporal-pixel features of color channels to reconstruct the high-resolution color image. Experimental results demonstrate that the proposed scheme can reconstruct satisfactory color images with both high temporal and spatial resolution from low-resolution Bayer-pattern spike streams. The source codes are available at https://***/csycdong/CSCSR.

关键词： Bayer pattern Color filter arrary Demosaicing Neuromorphic sensor Spike camera Super resolution

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Model Level Ensemble for Facial Action Unit recognition at the 3rd ABAW Challenge

Model Level Ensemble for Facial Action Unit Recognition at t...

引用

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

作者： Jiang, Wenqiang Wu, Yannan Qiao, Fengsheng Meng, Liyu Deng, Yuanyuan Liu, Chuanhe Beijing Seek Truth Data Technol Co Ltd Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In this paper, we present our latest work on Action Unit Detection, which is a part of the Affective Behavior Analysis in-the-wild (ABAW) 2022 Competition [15]. Our proposed network is based on the IResnet100 [6]. First of all, We utilize feature pyramid networks (FPN) [25] and single stage headless (SSH) [29] to enlarge the receptive field and extract more facial texture features. Then we employ the ML-ROS data balancing [4] and the BCE Loss plus Multi-label Loss to solve the multi-label imbalance problem. We also use three different models as the base model to fine-tune the Aff-Wild2 dataset. The pre-train backbones are the AU detection model, expression model and face recognition model. Finally, we adopt an ensemble methodology to get the final result. Our f1 score achieved 49.82 on the AU test set and ranked second in this challenge with a very small difference from the first team 49.89.

关键词： Gold computer vision Face recognition conferences Feature extraction Behavioral sciences Task analysis

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Gated Recurrent Unit-Based RNN for Remote Photoplethysmography Signal Segmentation

Gated Recurrent Unit-Based RNN for Remote Photoplethysmograp...

引用

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

作者： Sabour, Rita Meziati Benezeth, Yannick Univ Bourgogne Franche Comte Dijon France Univ Bourgogne Franche Comte ImViA Lab Dijon France

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Remote Photoplethysmography (rPPG) enables quantifying blood volume variations in the skin tissues from an input video recording, using a regular RGB camera. Obtained pulse signals often contain noisy portions due to motion, leading researchers to put aside a great number of rPPG signals in their studies. In this paper, an approach using a Gated Recurrent Unit-based neural network model in order to identify reliable portions in rPPG signals is proposed. This is done by classifying rPPG signal samples into reliable and unreliable samples. For this purpose, rPPG and electrocardiography signals (ECG) were collected from 11 participants, rPPG signal samples were labeled (ECG was used as ground truth), and data were augmented to reach a total number of 11000 1-minute-long rPPG signals. We developed a model composed of a unidimensional CNN and a Bidirectional GRU (1D-CNN+B-GRU) for this study, and obtained an accuracy rate of 85.88%.

关键词： computer network reliability Neural networks Electrocardiography Logic gates Photoplethysmography Skin pattern recognition

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Super-Resolution based Video Coding Scheme

Super-Resolution based Video Coding Scheme

引用

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

作者： Cho, Hyun Min Choi, Kiho Gacheon Univ Sch Comp 1342 Seongnamdaero Seongnam Si Gyeonggi Do South Korea

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In this paper, we present a super-resolution-based video coding scheme that compresses video data by combining traditional hybrid video coding and Convolutional neural network-based video coding. During video encoding, downsampling reduces the resolution of an original video in both horizontal and vertical directions to reduce original video data, and Convolutional neural networkbased super-resolution is employed after the decoding process to recover the resolution of the reconstructed video during upsampling. For core encoding and decoding processes, the latest video coding standard (i.e., VVC/H.266) is conducted. The experimental results show that the proposed method can provide efficient coding performance while maintaining good visual quality.

关键词： Video coding Visualization Image coding Superresolution Encoding Decoding pattern recognition

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Federated Learning-based Driver Activity recognition for Edge Devices

Federated Learning-based Driver Activity Recognition for Edg...

引用

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

作者： Doshi, Keval Yilmaz, Yasin Univ S Florida 4202 E Fowler Ave Tampa FL 33620 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Video action recognition has been an active area of research for the past several years. However, the majority of research is concentrated on recognizing a diverse range of activities in distinct environments. On the other hand, Driver Activity recognition (DAR) is significantly more difficult since there is a much finer distinction between various actions. Moreover, training robust DAR models requires diverse training data from multiple sources, which might not be feasible for a centralized setup due to privacy and security concerns. Furthermore, it is critical to develop efficient models due to limited computational resources available on vehicular edge devices. Federated Learning (FL), which allows data parties to collaborate on machine learning models while preserving data privacy and reducing communication requirements, can be used to overcome these challenges. Despite significant progress on various computer vision tasks, FL for DAR has been largely unexplored. In this work, we propose an FL-based DAR model and extensively benchmark the model performance on two datasets under various practical setups. Our results indicate that the proposed approach performs competitively under the centralized (non-FL) and decentralized (FL) settings.

关键词： Performance evaluation Data privacy computer vision Computational modeling Activity recognition Collaborative work Data models

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CorrGAN: Input Transformation Technique Against Natural Corruptions

CorrGAN: Input Transformation Technique Against Natural Corr...

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

作者： Haque, Mirazul Budnik, Christof J. Yang, Wei UT Dallas Richardson TX 75080 USA Siemens Corp Technol Princeton NJ USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Because of the increasing accuracy of Deep Neural Networks (DNNs) on different tasks, a lot of real times systems are utilizing DNNs. These DNNs are vulnerable to adversarial perturbations and corruptions. Specifically, natural corruptions like fog, blur, contrast etc can affect the prediction of DNN in an autonomous vehicle. In real time, these corruptions are needed to be detected and also the corrupted inputs are needed to be de-noised to be predicted correctly. In this work, we propose CorrGAN approach, which can generate benign input when a corrupted input is provided. In this framework, we train Generative Adversarial Network (GAN) with novel intermediate output-based loss function. The GAN can denoise the corrupted input and generate benign input. Through experimentation, we show that up to 75.2% of the corrupted misclassified inputs can be classified correctly by DNN using CorrGAN.

关键词： Deep learning computer vision Perturbation methods conferences Neural networks computer architecture Generative adversarial networks

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