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T2FNorm: Train-time Feature Normalization for OOD Detection in Image Classification

T2FNorm: Train-time Feature Normalization for OOD Detection ...

引用

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

作者： Sudarshan Regmi Bibek Panthi Sakar Dotel Prashnna K Gyawali Danail Stoyanov Binod Bhattarai University College London UK NepAl Applied Mathematics and Informatics Institute for Research Nepal Tribhuvan University Nepal West Virginia University USA University of Aberdeen UK

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Neural networks are notorious for being overconfident predictors, posing a significant challenge to their safe deployment in real-world applications. While feature normalization has garnered considerable attention within the deep learning literature, current train-time regularization methods for Out-of-Distribution(OOD) detection are yet to fully exploit this potential. Indeed, the naive incorporation of feature normalization within neural networks does not guarantee substantial improvement in OOD detection performance. In this work, we introduce T2FNorm, a novel approach to transforming features to hyperspherical space during training, while employing non-transformed space for OOD-scoring purposes. This method yields a surprising enhancement in OOD detection capabilities without compromising model accuracy in in-distribution(ID). Our investigation demonstrates that the proposed technique substantially diminishes the norm of the features of all samples, more so in the case of out-of-distribution samples, thereby addressing the prevalent concern of overconfidence in neural networks. The proposed method also significantly improves various post-hoc OOD detection methods.

关键词： Training Measurement Deep learning computer vision Accuracy conferences Neural networks

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DECNet: A Non-Contacting Dual-Modality Emotion Classification Network for Driver Health Monitoring

DECNet: A Non-Contacting Dual-Modality Emotion Classificatio...

引用

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

作者： Zhekang Dong Chenhao Hu Shiqi Zhou Liyan Zhu Junfan Wang Yi Chen Xudong Lv Xiaoyue Ji Hangzhou Dianzi University Zhejiang Provincial Key Laboratory of Equipment Electronics Zhejiang University Tsinghua University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Negative emotions have been identified as significant factors influencing driver behavior, easily leading to extremely serious traffic accidents. Hence, there is a pressing need to develop an automatic emotion classification method for driver health monitoring and road safety improvement. Most of the existing methods predominantly focus on single modalities, resulting in suboptimal classification performance due to the underutilization of heterogeneous information. In this work, we propose a novel non-contacting dual-modality driver emotion classification network (DECNet) to address these limitations. DECNet consists of three key modules: 1) facial video modality processing module; 2) driving behavior modality processing module; 3) fusion decision module. Meanwhile, we introduce a combined multi-task learning strategy within DECNet to improve the efficacy in the driver emotion classification task. To evaluate the effectiveness of the proposed DECNet, we conducted experiments on the PPB-Emo dataset, the experimental results showcase the superiority in terms of accuracy (⩾ 6.12% Acc-7) and F1-score (⩾ 7.25% F1-7) compared to existing state-of-the art methods. The model and code will be available at https://***/fqfqngxhs/***

关键词： Smart cities Pressing Feature extraction Multitasking Road safety Safety pattern recognition

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Enforcing Conditional Independence for Fair Representation Learning and Causal Image Generation

Enforcing Conditional Independence for Fair Representation L...

引用

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

作者： Jensen Hwa Qingyu Zhao Aditya Lahiri Adnan Masood Babak Salimi Ehsan Adeli Stanford University Weill Cornell Medicine University of California San Diego UST

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Conditional independence (CI) constraints are critical for defining and evaluating fairness in machine learning, as well as for learning unconfounded or causal representations. Traditional methods for ensuring fairness either blindly learn invariant features with respect to a protected variable (e.g., race when classifying sex from face images) or enforce CI relative to the protected attribute only on the model output (e.g., the sex label). Neither of these methods are effective in enforcing CI in high-dimensional feature spaces. In this paper, we focus on a nascent approach characterizing the CI constraint in terms of two Jensen-Shannon divergence terms, and we extend it to high-dimensional feature spaces using a novel dynamic sampling strategy. In doing so, we introduce a new training paradigm that can be applied to any encoder architecture. We are able to enforce conditional independence of the diffusion autoencoder latent representation with respect to any protected attribute under the equalized odds constraint and show that this approach enables causal image generation with controllable latent spaces. Our experimental results demonstrate that our approach can achieve high accuracy on downstream tasks while upholding equality of odds.

关键词： Training Representation learning computer vision Image synthesis conferences computer architecture Predictive models

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From 2D Portraits to 3D Realities: Advancing GAN Inversion for Enhanced Image Synthesis

From 2D Portraits to 3D Realities: Advancing GAN Inversion f...

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

作者： Wonseok Oh Youngjoo Jo Electrical and Computer Engineering University of Michigan ETRI

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Image synthesis using StyleGAN has shown remarkable results in 2D portrait image generation. The works of the GAN inversion to manipulate the real image using StyleGAN latent space also show remarkable achievements. 2D GAN inversion has successfully manipulated global attributes such as facial expressions and gender. However, preserving the hairstyle and identity was difficult according to the pose change. We introduce the 3D GAN inversion encoder to make a high-resolution 3D image based on the Geometry Aware 3D Generative Adversarial Network, known as EG3D, which allows explicit control over the pose of the real image subject with multi-view consistency. Our network projects the single 2D portrait images to novel latent space for 3D GAN inversion for the tri-plane of EG3D. We also present multi-view cycle loss, which aims to increase multi-view consistency. By leveraging the new latent space and loss for 3D GAN inversion, our network can successfully convert 2D portrait images into 3D fast.

关键词： Visualization Three-dimensional displays Image synthesis Computational modeling Semantics Aerospace electronics Generative adversarial networks

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Fake it to make it: Using synthetic data to remedy the data shortage in joint multi-modal speech-and-gesture synthesis

Fake it to make it: Using synthetic data to remedy the data ...

引用

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

作者： Shivam Mehta Anna Deichler Jim O’Regan Birger Moëll Jonas Beskow Gustav Eje Henter Simon Alexanderson KTH Royal Institute of Technology Sweden Motorica AB Sweden

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Although humans engaged in face-to-face conversation simultaneously communicate both verbally and non-verbally, methods for joint and unified synthesis of speech audio and co-speech 3D gesture motion from text are a new and emerging field. These technologies hold great promise for more human-like, efficient, expressive, and robust synthetic communication, but are currently held back by the lack of suitably large datasets, as existing methods are trained on parallel data from all constituent modalities. Inspired by student-teacher methods, we propose a straightforward solution to the data shortage, by simply synthesising additional training material. Specifically, we use uni-modal synthesis models trained on large datasets to create multi-modal (but synthetic) parallel training data, and then pre-train a joint synthesis model on that material. In addition, we propose a new synthesis architecture that adds better and more controllable prosody modelling to the state-of-the-art method in the field. Our results confirm that pre-training on large amounts of synthetic data improves the quality of both the speech and the motion synthesised by the multi-modal model, with the proposed architecture yielding further benefits when pre-trained on the synthetic data.

关键词： Training computer vision Three-dimensional displays conferences Training data computer architecture Oral communication

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Knowledge Transfer Dehazing Network for NonHomogeneous Dehazing

Knowledge Transfer Dehazing Network for NonHomogeneous Dehaz...

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

作者： Haiyan Wu Jing Liu Yuan Xie Yanyun Qu Lizhuang Ma School of Computer Science and Technology East China Normal University Shanghai China School of Information Science and Engineering Xiamen University Fujian China

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Single image dehazing is an ill-posed problem that has recently drawn important attention. It is a challenging image process task, especially in nonhomogeneous scene. However, the existing dehazing methods are commonly designed to handle homogeneous haze which is easily violated in practice, due to the unknown haze distribution of real world. In this paper, we propose a knowledge transfer method that utilizes abundant clear images to train a teacher network to provide strong and robust image prior. The derived architecture is referred to as the Knowledge Transform Dehaze Network (KTDN), which consists of the teacher network and the dehazing network with identical architecture. Through the supervision between intermediate features, the dehazing network is encouraged to imitate the teacher network. In addition, we use attention mechanism to combine channel attention with pixel attention to capture effective information, and employ an enhancing module to refine detail textures. Extensive experimental results on synthetic and real scene datasets demonstrates that the proposed method outperforms the state-of-the-arts in both quantitative and qualitative evaluations. The KTDN ranks 2 nd in NTIRE-2020 NonHomogeneous Dehazing Challenge [4], [5].

关键词： Task analysis Training Knowledge engineering Convolution Knowledge transfer Image color analysis Atmospheric modeling

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Trident Dehazing Network

Trident Dehazing Network

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

作者： Jing Liu Haiyan Wu Yuan Xie Yanyun Qu Lizhuang Ma School of Computer Science and Technology East China Normal University China School of Information Science and Engineering Xiamen University China

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Most existing dehazing methods are not robust to nonhomogeneous haze. Meanwhile, the information of dense haze region is usually unknown and hard to estimate, leading to blurry in dehaze result for those regions. Focusing on these two issues, we propose a novel coarse-to-fine model, namely Trident Dehazing Network (TDN), to learn the hazy to hazy- free image mapping with automatic haze density recognition. In detail, TDN is composed of three sub-nets: the Encoder-Decoder Net (EDN) is the main net of TDN to reconstruct the coarse hazy-free feature; the Detail Refinement sub-Net (DRN) helps to refine the high frequency details that was easily lost in the pooling layers in the encoder; and the Haze Density Map Generation sub-Net (HDMGN) can automatically distinguish the thick haze region with thin one, to prevent over-dehazing or under-dehazing in regions of different haze density. Moreover, we propose a frequency domain loss function to make supervision of different frequency band more uniform. Extensive experimental results on synthetic and real datasets demonstrate that our proposed TDN outperforms the state-of-the-arts with better fidelity and perceptual, generalizing well on both dense haze and nonhomogeneous haze scene. Our method won the first place in NTIRE2020 nonhomogeneous dehazing challenge.

关键词： Image reconstruction Frequency-domain analysis Feature extraction Training Atmospheric modeling Convolution

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CascadeTabNet: An approach for end to end table detection and structure recognition from image-based documents

CascadeTabNet: An approach for end to end table detection an...

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

作者： Devashish Prasad Ayan Gadpal Kshitij Kapadni Manish Visave Kavita Sultanpure Pune Institute of Computer Technology India

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

An automatic table recognition method for interpretation of tabular data in document images majorly involves solving two problems of table detection and table structure recognition. The prior work involved solving both problems independently using two separate approaches. More recent works signify the use of deep learning-based solutions while also attempting to design an end to end solution. In this paper, we present an improved deep learning-based end to end approach for solving both problems of table detection and structure recognition using a single Convolution Neural Network (CNN) model. We propose CascadeTabNet: a Cascade mask Region-based CNN High-Resolution Network (Cascade mask R-CNN HRNet) based model that detects the regions of tables and recognizes the structural body cells from the detected tables at the same time. We evaluate our results on ICDAR 2013, ICDAR 2019 and Table-Bank public datasets. We achieved 3rd rank in ICDAR 2019 post-competition results for table detection while attaining the best accuracy results for the ICDAR 2013 and Table-Bank dataset. We also attain the highest accuracy results on the ICDAR 2019 table structure recognition dataset. Additionally, we demonstrate effective transfer learning and image augmentation techniques that enable CNNs to achieve very accurate table detection results. Code and dataset has been made available at: https://***/DevashishPrasad/CascadeTabNet.

关键词： Task analysis Image segmentation Semantics Head Predictive models Analytical models Image recognition

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Image-caption difficulty for efficient weakly-supervised object detection from in-the-wild data

Image-caption difficulty for efficient weakly-supervised obj...

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

作者： Giacomo Nebbia Adriana Kovashka University of Pittsburgh

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In recent years, we have witnessed the collection of larger and larger multi-modal, image-caption datasets: from hundreds of thousands such pairs to hundreds of millions. Such datasets allow researchers to build powerful deep learning models, at the cost of requiring intensive computational resources. In this work, we ask: can we use such datasets efficiently without sacrificing performance? We tackle this problem by extracting difficulty scores from each image-caption sample, and by using such scores to make training more effective and efficient. We compare two ways to use difficulty scores to influence training: filtering a representative subset of each dataset and ordering samples through curriculum learning. We analyze and compare difficulty scores extracted from a single modality—captions (i.e., caption length and number of object mentions) or images (i.e., region proposals’ size and number)—or based on alignment of image-caption pairs (i.e., CLIP and concreteness). We focus on Weakly-Supervised Object Detection where image-level labels are extracted from captions. We discover that (1) combining filtering and curriculum learning can achieve large gains in performance, but not all methods are stable across experimental settings, (2) singlemodality scores often outperform alignment-based ones, (3) alignment scores show the largest gains when training time is limited.

关键词： Training Deep learning computer vision Costs Filtering conferences Computational modeling

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Transfer Learning for Classifying Single Hand Gestures on Comprehensive Bharatanatyam Mudra Dataset

Transfer Learning for Classifying Single Hand Gestures on Co...

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

作者： Anuja P. Parameshwaran Heta P. Desai Rajshekhar Sunderraman Michael Weeks Georgia State University

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

For any dance form, either classical or folk, visual expressions - facial expressions and hand gestures play a key role in conveying the storyline of the accompanied music to the audience. Bharatanatyam - a classical dance form which has origins from the southern states of India, is on the verge of being completely automated partly due to an acute dearth of qualified and dedicated teachers/gurus. In an honest effort to speed up this automation process and at the same time preserve the cultural heritage, we have chosen to identify and classify the single hand gestures/mudras/hastas against their true labels by using two variations of the convolutional neural networks (CNNs) that demonstrates the exceeding effectiveness of transfer learning irrespective of the domain difference between the pre-training and the training dataset. This work is primarily aimed at 1) building a novel dataset of 2D single hand gestures belonging to 27 classes that were collected from Google search engine (Google images), YouTube videos (dynamic and with background considered) and professional artists under staged environment constraints (plain backgrounds), 2) exploring the effectiveness of Convolutional Neural Networks in identifying and classifying the single hand gestures by optimizing the hyperparameters, and 3) evaluating the impacts of transfer learning and double transfer learning, which is a novel concept explored in this paper for achieving higher classification accuracy.

关键词： Gesture recognition Assistive technology Training computer architecture Hidden Markov models Convolutional neural networks Google

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