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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...

引用

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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Improving the Robustness of 3D Human Pose Estimation: A Benchmark Dataset and Learning from Noisy Input

Improving the Robustness of 3D Human Pose Estimation: A Benc...

引用

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

作者： Trung-Hieu Hoang Mona Zehni Huy Phan Duc Minh Vo Minh N. Do University of Illinois at Urbana-Champaign USA VinUniversity Ha Noi Vietnam The University of Tokyo Japan

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Despite the promising performance of current 3D human pose estimation techniques, understanding and enhancing their robustness on challenging in-the-wild videos remain an open problem. In this work, we focus on building robust 2D-to-3D pose lifters. To this end, we develop two benchmark datasets, namely Human3.6M-C and HumanEva-I-C, to examine the resilience of video-based 3D pose lifters to a wide range of common video corruptions including temporary occlusion, motion blur, and pixel-level noise. We demonstrate the poor generalization of state-of-the-art 3D pose lifters in the presence of corruption and establish two techniques to tackle this issue. First, we introduce Temporal Additive Gaussian Noise (TAGN) as a simple yet effective 2D input pose data augmentation. Additionally, to incorporate the confidence scores output by the 2D pose detectors, we design a confidence-aware convolution (CA-Conv) block. Extensively tested on corrupted videos, the proposed strategies consistently boost the robustness of 3D pose lifters and serve as new baselines for future research.

关键词： Solid modeling Three-dimensional displays Convolution Pose estimation Benchmark testing Robustness pattern recognition

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Feature Corrective Transfer Learning: End-to-End Solutions to Object Detection in Non-Ideal Visual Conditions

Feature Corrective Transfer Learning: End-to-End Solutions t...

引用

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

作者： Chuheng Wei Guoyuan Wu Matthew J. Barth University of California Riverside

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

A significant challenge in the field of object detection lies in the system’s performance under non-ideal imaging conditions, such as rain, fog, low illumination, or raw Bayer images that lack ISP processing. Our study introduces ‘Feature Corrective Transfer Learning’, a novel approach that leverages transfer learning and a bespoke loss function to facilitate the end-to-end detection of objects in these challenging scenarios without the need to convert non-ideal images into their RGB counterparts. In our methodology, we initially train a comprehensive model on a pristine RGB image dataset. Subsequently, non-ideal images are processed by comparing their feature maps against those from the initial ideal RGB model. This comparison employs the Extended Area Novel Structural Discrepancy Loss (EANSDL), a novel loss function designed to quantify similarities and integrate them into the detection loss. This approach refines the model’s ability to perform object detection across varying conditions through direct feature map correction, encapsulating the essence of Feature Corrective Transfer Learning. Experimental validation on variants of the KITTI dataset demonstrates a significant improvement in mean Average Precision (mAP), resulting in a 3.8-8.1% relative enhancement in detection under non-ideal conditions compared to the baseline model, and a less marginal performance difference within 1.3% of the mAP@[0.5:0.95] achieved under ideal conditions by the standard Faster RCNN algorithm.

关键词： Visualization computer vision Accuracy Computational modeling Surveillance Transfer learning Imaging

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A Method of Moments Embedding Constraint and its Application to Semi-Supervised Learning

A Method of Moments Embedding Constraint and its Application...

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

作者： Michael Majurski Sumeet Menon Parniyan Farvardin David Chapman Information Technology Lab NIST University of Maryland University of Maryland University of Miami

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Discriminative deep learning models with a linear+softmax final layer have a problem: the latent space only predicts the conditional probabilities p(Y |X) but not the full joint distribution p(Y,X), which necessitates a generative approach. The conditional probability cannot detect outliers, causing outlier sensitivity in softmax networks. This exacerbates model over-confidence impacting many problems, such as hallucinations, confounding biases, and dependence on large datasets. To address this we introduce a novel embedding constraint based on the Method of Moments (MoM). We investigate the use of polynomial moments ranging from 1st through 4th order hyper-covariance matrices. Furthermore, we use this embedding constraint to train an Axis-Aligned Gaussian Mixture Model (AAGMM) final layer, which learns not only the conditional, but also the joint distribution of the latent space. We apply this method to the domain of semi-supervised image classification by extending FlexMatch with our technique. We find our MoM constraint with the AAGMM layer is able to match the reported FlexMatch accuracy, while also modeling the joint distribution, thereby reducing outlier sensitivity. We also present a preliminary outlier detection strategy based on Mahalanobis distance and discuss future improvements to this strategy. Code is available at: https://***/mmajurski/ssl-gmm

关键词： Deep learning Sensitivity Semisupervised learning Predictive models Polynomials Distance measurement pattern recognition

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Classification of 2D Ultrasound Breast Cancer Images with Deep Learning

Classification of 2D Ultrasound Breast Cancer Images with De...

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

作者： Jack Ellis Kofi Appiah Emmanuel Amankwaa-Frempong Sze Chai Kwok Department of Computer Science University of York York UK Sweden-Ghana Medical Center Accra Ghana Division of Natural & Applied Sciences Duke Kunshan University China Affiliated Mental Health Center School of Psychology & Cognitive Sci. ECNU China Shanghai Changning Mental Health Center Shanghai China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Breast cancer is the second most prevalent form of cancer and is the “leading cause of most cancer-related deaths in women”. Most women living in low- and middle-income countries (LMIC) have limited access to the existing poor health systems, restricted access to treatment facilities, and in general lack of breast cancer screening programmes. The likelihood of women living in LMIC attending a health facility with advanced-stage breast cancer is very high and the chances of them being able to afford treatment at that stage, even if the treatment is available, is very low. In this work, we evaluate the capabilities of deep learning as a classification tool with the aim of detecting cancerous ultrasound breast images. We aim to deploy a simple classifier on a mobile device with an inexpensive handheld ultrasound imaging system to pick up breast cancer cases that will need medical attention. We demonstrate in this work that with minimal ultrasound images, a de novo system trained from scratch can achieve accuracy of close to 64% and about 78% when the same model is pre-trained.

关键词： Deep learning Ultrasonic imaging Accuracy Computational modeling Transfer learning Breast cancer Mobile handsets

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Simple In-place Data Augmentation for Surveillance Object Detection

Simple In-place Data Augmentation for Surveillance Object De...

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

作者： Munkh-Erdene Otgonbold Ganzorig Batnasan Munkhjargal Gochoo Department of Computer Science and Software Engineering United Arab Emirates University UAE Department of Electronics Mongolian University of Science and Technology Mongolia Emirates Center for Mobility Research United Arab Emirates University UAE

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Motivated by the need to improve model performance in traffic monitoring tasks with limited labeled samples, we propose a straightforward augmentation technique tailored for object detection datasets, specifically designed for stationary camera-based applications. Our approach focuses on placing objects in the same positions as the originals to ensure its effectiveness. By applying in-place augmentation on objects from the same camera input image, we address the challenge of overlapping with original and previously selected objects. Through extensive testing on two traffic monitoring datasets, we illustrate the efficacy of our augmentation strategy in improving model performance, particularly in scenarios with limited labeled samples and imbalanced class distributions. Notably, our method achieves comparable performance to models trained on the entire dataset while utilizing only 8.5 percent of the original data. Moreover, we report significant improvements, with mAP@.5 increasing from 0.4798 to 0.5025, and the mAP@.5:.95 rising from 0.29 to 0.3138 on the FishEye8K dataset. These results highlight the potential of our augmentation approach in enhancing object detection models for traffic monitoring applications.

关键词： Image segmentation Surveillance Scalability conferences Object detection Data augmentation Cameras

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NTIRE 2021 Learning the Super-Resolution Space Challenge

NTIRE 2021 Learning the Super-Resolution Space Challenge

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

作者： Lugmayr, Andreas Danelljan, Martin Timofte, Radu Busch, Christoph Chen, Yang Cheng, Jian Chudasama, Vishal Gang, Ruipeng Gao, Shangqi Gao, Kun Gong, Laiyun Han, Qingrui Huang, Chao Jin, Zhi Jo, Younghyun Kim, Seon Joo Kim, Younggeun Lee, Seungjun Lei, Yuchen Li, Chu-Tak Li, Chenghua Li, Ke Liu, Zhi-Song Liu, Youming Nan, Nan Park, Seung-Ho Patel, Heena Peng, Shichong Prajapati, Kalpesh Qi, Haoran Raja, Kiran Ramachandra, Raghavendra Siu, Wan-Chi Son, Donghee Song, Ruixia Upla, Kishor Wang, Li-Wen Wang, Yatian Wang, Junwei Wu, Qianyu Xu, Xinhua Yang, Sejong Yuan, Zhen Zhang, Liting Zhang, Huanrong Zhang, Junkai Zhang, Yifan Zhang, Zhenzhou Zhou, Hangqi Zhu, Aichun Zhuang, Xiahai Zou, Jiaxin Swiss Fed Inst Technol Zurich Switzerland Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Zhejiang Univ Hangzhou Peoples R China NetEaseYunXin Beijing Peoples R China Yonsei Univ Seoul South Korea Seoul Natl Univ Seoul South Korea Univ Ulsan Coll Med Asan Med Ctr Ulsan South Korea Lomin Inc Portland OR USA Fudan Univ Shanghai Peoples R China Simon Fraser Univ Burnaby BC Canada Caritas Inst Higher Educ Hong Kong Peoples R China Hong Kong Polytech Univ Hong Kong Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China North China Univ Technol Beijing Peoples R China Univ Elect Sci & Technol China Chengdu Peoples R China CASIA Beijing Peoples R China NRTA Beijing Peoples R China NCUT Beijing Peoples R China Nanjing Tech Univ Sch Comp Sci & Technol Nanjing Peoples R China Southeast Univ Lab Image Sci & Technol Nanjing Peoples R China SVNIT Surat Gujarat India NTNU Trondheim Norway

ISBN: (纸本)9781665448994

This paper reviews the NTIRE 2021 challenge on learning the super-Resolution space. It focuses on the participating methods and final results. The challenge addresses the problem of learning a model capable of predicting the space of plausible super-resolution (SR) images, from a single low-resolution image. The model must thus be capable of sampling diverse outputs, rather than just generating a single SR image. The goal of the challenge is to spur research into developing learning formulations and models better suited for the highly ill-posed SR problem. And thereby advance the state-of-the-art in the broader SR field. In order to evaluate the quality of the predicted SR space, we propose a new evaluation metric and perform a comprehensive analysis of the participating methods. The challenge contains two tracks: 4x and 8 x scale factor. In total, 11 teams competed in the final testing phase.

关键词： Measurement computer vision conferences Superresolution Predictive models pattern recognition

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Exploring the Role of Audio in Video Captioning

Exploring the Role of Audio in Video Captioning

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

作者： Yuhan Shen Linjie Yang Longyin Wen Haichao Yu Ehsan Elhamifar Heng Wang Northeastern University ByteDance

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Recent focus in video captioning has been on designing architectures that can consume both video and text modalities, and using large-scale video datasets with text transcripts for pre-training, such as HowTo100M. Though these approaches have achieved significant improvement, the audio modality is often ignored in video captioning. In this work, we present an audio-visual framework, which aims to fully exploit the potential of the audio modality for captioning. Instead of relying on text transcripts extracted via automatic speech recognition (ASR), we argue that learning with raw audio signals can be more beneficial, as audio has additional information including acoustic events, speaker identity, etc. Our contributions are twofold. First, we observed that the model overspecializes to the audio modality when pre-training with both video and audio modality, since the ground truth (i.e., text transcripts) can be solely predicted using audio. We proposed a Modality Balanced Pre-training (MBP) loss to mitigate this issue and significantly improve the performance on downstream tasks. Second, we slice and dice different design choices of the cross-modal module, which may become an information bottleneck and generate inferior results. We proposed new local-global fusion mechanisms to improve information exchange across audio and video. We demonstrate significant improvements by leveraging the audio modality on four datasets, and even outperform the state of the art on some metrics without relying on the text modality as the input.

关键词： Measurement computer vision Computational modeling conferences Predictive models Acoustics Data mining

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OGRMPI: An Efficient Multiview Integrated Multiplane Image based on Occlusion Guided Residuals

OGRMPI: An Efficient Multiview Integrated Multiplane Image b...

引用

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

作者： Dae Yeol Lee Guan-Ming Su Peng Yin Dolby Laboratories Sunnyvale CA USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Multiplane Image (MPI) is a volumetric scene representation method that uses multiple layers of texture (RGB) and alpha (A) planes. It offers high deployability due to its compatibility with standard codecs and low rendering complexity. While existing MPI methods have shown promising results, they are constrained by either a limited range of pose span or necessitates transmitting multiple MPIs. In this paper, we present a novel framework to generate an efficient single MPI that integrates the information from multiple views. The key idea is to utilize the surface opacity estimates (A) to locate and retrieve occluded RGB pixels from other camera views that share matching depth, which we call Occlusion Guided Residuals (OGR). Additionally, we introduce an inter-layer texture filler, which is a learned RGB texture on intermediate depth between MPI layers to deal with scenes with continuous depth with a limited number of MPI layers. We composite MPI using the aforementioned RGB textures and refine alpha layers through training with multiview rendering supervision. Thus, through iterations of training, we jointly optimize scene opacity (A) and textures (RGB) leading to an accurate MPI representation. Experiments on various multiview image and video datasets demonstrate that the proposed method achieves state-of-the-art performances with data efficiency. Notably, with just 16 layers, the proposed method attains performance on par with other methods that use twice the layer number or fuse multiple MPIs.

关键词： Training Interpolation computer vision Accuracy Three-dimensional displays Fuses conferences

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NeRF as Pretraining at Scale: Generalizable 3D-Aware Semantic Representation Learning from View Prediction

NeRF as Pretraining at Scale: Generalizable 3D-Aware Semanti...

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

作者： Wenyan Cong Hanxue Liang Zhiwen Fan Peihao Wang Yifan Jiang Dejia Xu A. Cengiz Oztireli Zhangyang Wang University of Texas at Austin University of Cambridge

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Cross-scene generalizable NeRF models, which could directly synthesize novel views using several source views of unseen scenes, are gaining prominence in the NeRF field. Discovering the potential signal of emerging capabilities in existing methods, we draw a parallel between BERT’s "drop-and-predict" Masked Language Model (MLM) pre-training and novel view synthesis (NVS) in generalizable NeRF. In this work, we pioneer the scaling up of NVS as an effective pretraining strategy in a multi-view context. To bolster generalizability in pretraining, we incorporate a large-scale, minimally annotated dataset and proportionally increase the model size, revealing a neural scaling law akin to that observed in BERT. We also introduce innovative hardness-aware training techniques to enhance robust feature learning. Our model, named "NPS", demonstrates remarkable generalizability in both zero-shot and few-shot novel view synthesis. It further shows emergent capabilities in downstream tasks like few-shot multi-view semantic segmentation and depth estimation. Significantly, NPS reduces the necessity of training separate models for each task, underlining its versatility and efficiency. This approach sets a new precedent in the NeRF field, and highlights the vast possibilities opened up by scaling up generalizable novel view synthesis.

关键词： Training Representation learning computer vision Semantic segmentation conferences Semantics Estimation

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