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CycleGANAS: Differentiable Neural Architecture Search for CycleGAN

CycleGANAS: Differentiable Neural Architecture Search for Cy...

引用

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

作者： Taegun An Changhee Joo Department of Computer Science and Engineering Korea University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We develop a Neural Architecture Search (NAS) framework for CycleGAN that carries out unpaired image-to-image translation task. Extending previous NAS techniques for Generative Adversarial Networks (GANs) to CycleGAN is not straightforward due to the task difference and greater search space. We design architectures that consist of a stack of simple ResNet-based cells and develop a search method that effectively explore the large search space. We show that our framework, called CycleGANAS, not only effectively discovers high-performance architectures that either match or surpass the performance of the original CycleGAN, but also successfully addresses the data imbalance by individual architecture search for each translation direction. To our best knowledge, it is the first NAS result for CycleGAN and shed light on NAS for more complex structures. Our code is at https://***/antaegun20/CycleGANAS. 1

关键词： Dimensionality reduction Search methods Stacking Neural networks computer architecture Generative adversarial networks Generators

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IQMA Network: Image Quality Multi-scale Assessment Network

IQMA Network: Image Quality Multi-scale Assessment Network

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

作者： Guo, Haiyang Bin, Yi Hou, Yuqing Zhang, Qing Luo, Hengliang Meituan Beijing Peoples R China

ISBN: (纸本)9781665448994

Image Quality Assessment (IQA), which aims to provide computational models for automatically predicting perceptual image quality, is an important computer vision task with many applications. In recent years, a variety of IQA methods have been proposed based on different metric designs, which measure the quality of images affected by various types of distortion. However, with the rapid development of Generative Adversarial Networks (GAN), a new challenge has been brought to the IQA community. Especially, the GAN-based Image Reconstruction (IR) methods overfit the traditional PSNR-based IQA methods by generating images with sharper edges and texture-like noises, leading the outputs to be similar to the reference image in appearance but with loss of details. In this paper, we propose a bilateral-branch multi-scale image quality estimation network, named IQMA network. The two branches are designed with Feature Pyramid Network (FPN)-like architecture, extracting multi-scale features for patches of the reference image and corresponding patches of the distorted image separately. Then features of the same scale from both branches are sent into several scale-specific feature fusion modules. Each module performs feature fusion and a novelly designed pooling operation for corresponding features. Then several score regression modules are used to learn a quality score for each scale. Finally, image scores for different scales are fused as the quality score of the image. IQMA network has achieved 1st place on the NTIRE 21 IQA public leaderboard and 2nd place on the NTIRE 21 IQA private leaderboard, and consistently outperforms existing state-of-the-art (SOTA) methods on LIVE and TID2013.

关键词： Image quality computer vision Image edge detection computer architecture Predictive models Feature extraction Generative adversarial networks

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A Tale of Two CILs: The Connections between Class Incremental Learning and Class Imbalanced Learning, and Beyond

A Tale of Two CILs: The Connections between Class Incrementa...

引用

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

作者： He, Chen Wang, Ruiping Chen, Xilin Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc CAS Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

ISBN: (纸本)9781665448994

Catastrophic forgetting, the main challenge of Class Incremental Learning, is closely related to the classifier's bias due to imbalanced data, and most researchers resort to empirical techniques to remove the bias. Such anti-bias tricks share many ideas with the field of Class Imbalanced Learning, which encourages us to reflect on why these tricks work, and how we can design more principled solutions from a different perspective. In this paper, we comprehensively analyze the connections and seek possible collaborations between these two fields, i.e. Class Incremental Learning and Class Imbalanced Learning. Specifically, we first provide a panoramic view of recent bias correction tricks from the perspective of handling class imbalance. Then, we show that an adapted post-scaling technique which originates from Class Imbalanced Learning is on par with or even outperforms SOTA Class Incremental Learning method. Visualization via violin plots and polar charts further sheds light on how SOTA methods address the class imbalance problem from a more intuitive geometric perspective. These findings may encourage further infiltration between the two closely connected fields, but also raise concerns about whether it is correct that Class Incremental Learning degenerates into a class imbalance problem.

关键词： Learning systems computer vision conferences Collaboration pattern recognition

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Hierarchical NeuroSymbolic Approach for Comprehensive and Explainable Action Quality Assessment

Hierarchical NeuroSymbolic Approach for Comprehensive and Ex...

引用

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

作者： Lauren Okamoto Paritosh Parmar Princeton University IHPC A*STAR Singapore

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Action quality assessment (AQA) applies computer vision to quantitatively assess the performance or execution of a human action. Current AQA approaches are end-to-end neural models, which lack transparency and tend to be biased because they are trained on subjective human judgements as ground-truth. To address these issues, we introduce a neuro-symbolic paradigm for AQA, which uses neural networks to abstract interpretable symbols from video data and makes quality assessments by applying rules to those symbols. We take diving as the case study. We found that domain experts prefer our system and find it more informative than purely neural approaches to AQA in diving. Our system also achieves state-of-the-art action recognition and temporal segmentation, and automatically generates a detailed report that breaks the dive down into its elements and provides objective scoring with visual evidence. As verified by a group of domain experts, this report may be used to assist judges in scoring, help train judges, and provide feedback to divers. Annotated training data and code: https://***/laurenok24/NSAQA.

关键词： computer vision Visualization Codes conferences Computational modeling Neural networks Symbols

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DW-GAN: A Discrete Wavelet Transform GAN for NonHomogeneous Dehazing

DW-GAN: A Discrete Wavelet Transform GAN for NonHomogeneous ...

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

作者： Fu, Minghan Liu, Huan Yu, Yankun Chen, Jun Wang, Keyan McMaster Univ Dept Elect & Comp Engn Hamilton ON Canada Xidian Univ State Key Lab Integrated Serv Networks Xian Peoples R China

ISBN: (纸本)9781665448994

Hazy images are often subject to color distortion, blurring, and other visible quality degradation. Some existing CNN-based methods have great performance on removing homogeneous haze, but they are not robust in non-homogeneous case. The reasons are mainly in two folds. Firstly, due to the complicated haze distribution, texture details are easy to be lost during the dehazing process. Secondly, since the training pairs are hard to be collected, training on limited data can easily lead to over-fitting problem. To tackle these two issues, we introduce a novel dehazing network using 2D discrete wavelet transform, namely DW-GAN. Specifically, we propose a two-branch network to deal with the aforementioned problems. By utilizing wavelet transform in DWT branch, our proposed method can retain more high frequency knowledge in feature maps. In order to prevent over-fitting, ImageNet pre-trained Res2Net is adopted in the knowledge adaptation branch. Owing to the robust feature representations of ImageNet pre-training, the generalization ability of our network is improved dramatically. Finally, a patch-based discriminator is used to reduce artifacts of the restored images. Extensive experimental results demonstrate that the proposed method outperforms the state-of-the-arts quantitatively and qualitatively.

关键词： Training Wavelet domain Image color analysis Frequency-domain analysis Generative adversarial networks Discrete wavelet transforms pattern recognition

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

Hierarchical Spatial Pyramid Network For Cervical Precancero...

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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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Class-Incremental Learning with Generative Classifiers

Class-Incremental Learning with Generative Classifiers

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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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A Robust MTMC Tracking System for AI-City Challenge 2021

A Robust MTMC Tracking System for AI-City Challenge 2021

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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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Augmented Self-Mask Attention Transformer for Naturalistic Driving Action recognition

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

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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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Localized Latent Updates for Fine-Tuning vision-Language Models

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

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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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