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检索条件"任意字段=IEEE Conference on Computer Vision and Pattern Recognition"

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Learning Deep Latent Variable Models by Short-Run MCMC Inference with Optimal Transport Correction

Learning Deep Latent Variable Models by Short-Run MCMC Infer...

引用

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

作者： An, Dongsheng Xie, Jianwen Li, Ping Baidu Res Cognit Comp Lab 10900 NE 8th St Bellevue WA 98004 USA

ISBN: (纸本)9781665445092

Learning latent variable models with deep top-down architectures typically requires inferring the latent variables for each training example based on the posterior distribution of these latent variables. The inference step typically relies on either time-consuming long-run Markov chain Monte Carlo (MCMC) sampling or a separate inference model for variational learning. In this paper, we propose to use a short-run MCMC, such as a short-run Langevin dynamics, as an approximate flow-based inference engine. The bias existing in the output distribution of the non-convergent short-run Langevin dynamics is corrected by the optimal transport (OT), which aims at transforming the biased distribution produced by the finite-step MCMC to the prior distribution with a minimum transport cost. Our experiments not only verify the effectiveness of the OT correction for the short-run MCMC, but also demonstrate that the latent variable model trained by the proposed strategy performs better than the variational auto-encoder (VAE) in terms of image reconstruction/generation and anomaly detection.

关键词： Training Monte Carlo methods Costs Image synthesis Markov processes pattern recognition Task analysis

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From Evaluation to Verification: Towards Task-oriented Relevance Metrics for Pedestrian Detection in Safety-critical Domains

From Evaluation to Verification: Towards Task-oriented Relev...

引用

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

作者： Lyssenko, Maria Gladisch, Christoph Heinzemann, Christian Woehrle, Matthias Triebel, Rudolph Robert Bosch GmbH Corp Res Robert Bosch Campus 1 D-71272 Renningen Germany Tech Univ Munich Boltzmannstr 3 D-85748 Garching Germany German Aerosp Ctr DLR Munchener Str 20 D-82234 Wessling Germany

ISBN: (纸本)9781665448994

Whenever a visual perception system is employed in safety-critical applications such as automated driving, a thorough, task-oriented experimental evaluation is necessary to guarantee safe system behavior. While most standard evaluation methods in computer vision provide a good comparability on benchmarks, they tend to fall short on assessing the system performance that is actually relevant for the given task. In our work, we consider pedestrian detection as a highly relevant perception task, and we argue that standard measures such as Intersection over Union (IoU) give insufficient results, mainly because they are insensitive to important physical cues including distance, speed, and direction of motion. Therefore, we investigate so-called relevance metrics, where specific domain knowledge is exploited to obtain a task-oriented performance measure focusing on distance in this initial work. Our experimental setup is based on the CARLA simulator and allows a controlled evaluation of the impact of that domain knowledge. Our first results indicate a linear decrease of the IoU related to the pedestrians' distance, leading to the proposal of a first relevance metric that is also conditioned on the distance.

关键词： Measurement Meters computer vision System performance pattern recognition Proposals Motion measurement

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DAP: Detection-Aware Pre-training with Weak Supervision

DAP: Detection-Aware Pre-training with Weak Supervision

引用

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

作者： Zhong, Yuanyi Wang, Jianfeng Wang, Lijuan Peng, Jian Wang, Yu-Xiong Zhang, Lei Univ Illinois Urbana IL 61801 USA Microsoft Redmond WA USA

ISBN: (纸本)9781665445092

This paper presents a detection-aware pre-training (DAP) approach, which leverages only weakly-labeled classification-style datasets (e.g., ImageNet) for pre-training, but is specifically tailored to benefit object de- tection tasks. In contrast to the widely used image classification-based pre-training (e.g., on ImageNet), which does not include any location-related training tasks, we transform a classification dataset into a detection dataset through a weakly supervised object localization method based on Class Activation Maps to directly pre-train a detector, making the pre-trained model location-aware and capable of predicting bounding boxes. We show that DAP can outperform the traditional classification pre-training in terms of both sample efficiency and convergence speed in downstream detection tasks including VOC and COCO. In particular, DAP boosts the detection accuracy by a large margin when the number of examples in the downstream task is small.

关键词： Training Location awareness computer vision Transforms Object detection Detectors Predictive models

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Learning Graphs for Knowledge Transfer with Limited Labels

Learning Graphs for Knowledge Transfer with Limited Labels

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

作者： Ghosh, Pallabi Saini, Nirat Davis, Larry S. Shrivastava, Abhinav Univ Maryland College Pk MD 20742 USA

ISBN: (纸本)9781665445092

Fixed input graphs are a mainstay in approaches that utilize Graph Convolution Networks (GCNs) for knowledge transfer. The standard paradigm is to utilize relationships in the input graph to transfer information using GCNs from training to testing nodes in the graph;for example, the semi-supervised, zero-shot, and few-shot learning setups. We propose a generalized framework for learning and improving the input graph as part of the standard GCN-based learning setup. Moreover, we use additional constraints between similar and dissimilar neighbors for each node in the graph by applying triplet loss on the intermediate layer output. We present results of semi-supervised learning on Citeseer, Cora, and Pubmed benchmarking datasets, and zero/few-shot action recognition on UCF101 and HMDB51 datasets, significantly outperforming current approaches. We also present qualitative results visualizing the graph connections that our approach learns to update.

关键词： Training Visualization computer vision Convolution Semisupervised learning Benchmark testing pattern recognition

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Research on Moving Object Real-time recognition based on Deep Neural Network 24

Research on Moving Object Real-time Recognition based on Dee...

引用

2024 International conference on Machine Learning, pattern recognition and Automation Engineering, MLPRAE 2024

作者： Xia, Tongtong Computer Science and Control Systems Bauman Moscow State Technical University Russia

ISBN: (纸本)9798400709876

Object recognition represents a significant area of investigation within the field of computer vision, with applications spanning industrial detection, traffic supervision, remote sensing, biomedicine and numerous other domains. As information science and technology advance, the accuracy and speed of object recognition continue to improve. In this work, a real-time moving object recognition model based on an improved convolutional neural network (CNN) was proposed. By optimising the structure of the convolutional layer and the pooling layer, the model demonstrates enhanced capabilities for the detection and classification of moving objects within the video frame. A multi-scale feature extraction and attention mechanism is introduced to enhance the model's object recognition performance at different scales and dynamic backgrounds. The extraction of features at multiple scales enables the model to capture both large-scale and small-scale object features, thereby enhancing its ability to detect objects of varying sizes. The attention mechanism enables the model to allocate more precise attention to crucial features in the presence of intricate backgrounds and motion blurring, through the dynamic adjustment of the relative importance of each feature region. This enhances the resilience and precision of the recognition process. The combination of these two techniques enables the model to process a diverse range of complex scenarios in real-world applications with greater efficiency and accuracy. The experimental results demonstrate that the enhanced model exhibits superior performance compared to the established benchmark model on publicly available datasets, exhibiting enhanced accuracy and real-time processing capabilities. © 2024 ACM.

关键词： Convolutional neural networks

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MLSD-GAN - Generating Strong High Quality Face Morphing Attacks Using Latent Semantic Disentanglement 2

MLSD-GAN - Generating Strong High Quality Face Morphing Atta...

引用

2nd ieee International conference on computer vision and Machine Intelligence, CVMI 2023

作者： Aravinda Reddy, P.N. Ramachandra, Raghavendra Rao, Krothapalli Sreenivasa Mitra, Pabitra Iit Kharagpur Advanced Technology Development Centre West Bengal Kharagpur India Norway Iit Kharagpur Department of Computer Science and Engineering West Bengal Kharagpur India

ISBN: (纸本)9798350305142

Face-morphing attacks are a growing concern for biometric researchers, as they can be used to fool face recognition systems (FRS). These attacks can be generated at the image level (supervised) or representation level (unsupervised). Previous unsupervised morphing attacks have relied on generative adver-sarial networks (GANs). More recently, researchers have used linear interpolation of StyleGAN-encoded images to generate morphing attacks. In this paper, we propose a new method for generating high-quality morphing attacks using StyleGAN disentanglement. Our approach, called MLSD-GAN, spherically interpolates the disentangled latents to produce realistic and diverse morphing attacks. We evaluate the vulnerability of MLSD-GAN on two deep-learning-based FRS techniques. The results show that MLSD-GAN poses a significant threat to FRS, as it can generate morphing attacks that are highly effective at fooling these systems. © 2023 ieee.

关键词： Face recognition

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Chinese Nested Named Entity recognition in the Medical Field Based on BiLSTM and Multi-Head Global Pointer 5

Chinese Nested Named Entity Recognition in the Medical Field...

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5th ieee International conference on pattern recognition and Machine Learning, PRML 2024

作者： Zhao, Xinke Yilahun, Hankiz Hamdulla, Askar School of Computer Science and Technology Xinjiang University Urumqi China

ISBN: (纸本)9798350355925

The technology of named entity recognition has been gradually refined. However, current research is primarily focused on non-nested named entity recognition, with less attention devoted to nested named entity recognition (NNER). The challenge of accurately extracting nested entities from complex medical texts remains. To address the issue of low accuracy in identifying nested entities in medical named entity recognition, we propose a Chinese nested named entity recognition model (Multi-ABGP) based on adversarial training, bidirectional long short-term memory (BiLSTM) and multi-headed global pointer network. This model employs a BiLSTM network to extract bidirectional and long-distance text features, enhancing the ability of the global pointer network to extract features from longer text. Furthermore, adversarial training is employed to reduce the impact of noise on the model's performance and enhance its robustness. This enables the model to ignore irrelevant features introduced by noise and instead focus on entity-related features. Finally, a multi-head global pointer network is utilized to fuse the features of multiple global pointer networks. This reduces the erroneous prediction of a single pointer network and improves the overall performance of the model. The experimental results demonstrate that the proposed model achieves Micro-F1, Micro-P, and Micro-R values of 73.71%, 74.96%, and 72.50%, respectively, on the CMeEE-V2 dataset of Chinese nested named entity recognition in the medical specialty. This significantly improves the effectiveness of Chinese nested entity recognition in the medical field. And we also compare it with the large language model (LLM), the experimental results show that the model outperforms the ChatGLM in the nested entity recognition task. Additionally, it exhibits favorable results on the non-nested entity recognition dataset People's Daily. © 2024 ieee.

关键词： Data mining

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SelfAugment: Automatic Augmentation Policies for Self-Supervised Learning

SelfAugment: Automatic Augmentation Policies for Self-Superv...

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

作者： Reed, Colorado J. Metzger, Sean Srinivas, Aravind Darrell, Trevor Keutzer, Kurt Univ Calif Berkeley BAIR Dept Comp Sci Berkeley CA 94720 USA Weill Neurosci Inst Grad Grp Bioengn Berkeley UCSF San Francisco CA USA UCSF Neurol Surg San Francisco CA USA

ISBN: (纸本)9781665445092

A common practice in unsupervised representation learning is to use labeled data to evaluate the quality of the learned representations. This supervised evaluation is then used to guide critical aspects of the training process such as selecting the data augmentation policy. However, guiding an unsupervised training process through supervised evaluations is not possible for real-world data that does not actually contain labels (which may be the case, for example, in privacy sensitive fields such as medical imaging). Therefore, in this work we show that evaluating the learned representations with a self-supervised image rotation task is highly correlated with a standard set of supervised evaluations (rank correlation > 0.94). We establish this correlation across hundreds of augmentation policies, training settings, and network architectures and provide an algorithm (SelfAugment) to automatically and efficiently select augmentation policies without using supervised evaluations. Despite not using any labeled data, the learned augmentation policies perform comparably with augmentation policies that were determined using exhaustive supervised evaluations.

关键词： Training computer vision Data privacy Correlation Image recognition Network architecture pattern recognition

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Extracurricular Learning: Knowledge Transfer Beyond Empirical Distribution

Extracurricular Learning: Knowledge Transfer Beyond Empirica...

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

作者： Pouransari, Hadi Javaheripi, Mojan Sharma, Vinay Tuzel, Oncel Apple Cupertino CA 95014 USA UCSD La Jolla CA USA

ISBN: (纸本)9781665448994

Knowledge distillation has been used to transfer knowledge learned by a sophisticated model (teacher) to a simpler model (student). This technique is widely used to compress model complexity. However, in most applications the compressed student model suffers from an accuracy gap with its teacher. We propose extracurricular learning, a novel knowledge distillation method, that bridges this gap by (1) modeling student and teacher output distributions;(2) sampling examples from an approximation to the underlying data distribution;and (3) matching student and teacher output distributions over this extended set including uncertain samples. We conduct rigorous evaluations on regression and classification tasks and show that compared to the standard knowledge distillation, extracurricular learning reduces the gap by 46 % to 68 %. This leads to major accuracy improvements compared to the empirical risk minimization-based training for various recent neural network architectures: 16% regression error reduction on the MPIIGaze dataset, +3.4% to +9.1% improvement in top-1 classification accuracy on the CIFAR100 dataset, and +2.9% top-1 improvement on the ImageNet dataset.

关键词： Training Uncertainty Neural networks Estimation Data models pattern recognition Task analysis

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Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision

Semi-Supervised Semantic Segmentation with Cross Pseudo Supe...

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

作者： Chen, Xiaokang Yuan, Yuhui Zeng, Gang Wang, Jingdong Peking Univ Key Lab Machine Percept MOE Beijing Peoples R China Microsoft Res Asia Beijing Peoples R China Microsoft Res Beijing Peoples R China

ISBN: (纸本)9781665445092

In this paper, we study the semi-supervised semantic segmentation problem via exploring both labeled data and extra unlabeled data. We propose a novel consistency regularization approach, called cross pseudo supervision (CPS). Our approach imposes the consistency on two segmentation networks perturbed with different initialization for the same input image. The pseudo one-hot label map, output from one perturbed segmentation network, is used to supervise the other segmentation network with the standard cross-entropy loss, and vice versa. The CPS consistency has two roles: encourage high similarity between the predictions of two perturbed networks for the same input image, and expand training data by using the unlabeled data with pseudo labels. Experiment results show that our approach achieves the state-of-the-art semi-supervised segmentation performance on Cityscapes and PASCAL VOC 2012.

关键词： Training Image segmentation computer vision Semantics Training data pattern recognition Standards

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