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HNN: Hierarchical Noise-Deinterlace Net Towards Image Denoising

HNN: Hierarchical Noise-Deinterlace Net Towards Image Denois...

引用

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

作者： Amogh Joshi Nikhil Akalwadi Chinmayee Mandi Chaitra Desai Ramesh Ashok Tabib Ujwala Patil Uma Mudenagudi Center of Excellence in Visual Intelligence (CEVI) KLE Technological University Hubballi Karnataka India School of Computer Science and Engineering KLE Technological University Hubballi Karnataka India School of Electronics and Communication Engineering KLE Technological University Hubballi Karnataka India

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In this paper, we propose a hierarchical framework for image denoising and term it Hierarchical Noise-Deinterlace Net (HNN). Image denoising techniques aim to recover clean images from noisy observations by reducing unwanted noise and artifacts to enhance the clarity and introduce spatial coherence. Images captured during challenging scenarios suffer from granular noise, inducing fine-scale variations in the image, which occur due to the limitations of imaging technology or environmental conditions. This granular noise can significantly degrade the quality of the image, making it less useful for applications like object detection, image restoration/enhancement, face detection, and image super-resolution. From literature, we infer learning global-local features significantly contribute in reducing unwanted noise and artifacts within images. Typically, researchers rely on residual learning, Generative Adversarial Networks (GANs), and Attention Mechanisms to learn global-local features. However, these methods face challenges such as vanishing gradients, limited generalization of generators in GANs, lack of global context awareness and computation complexity in attention mechanisms leading to drop in performance. Towards this, we propose a hierarchical framework to process both global and local information across distinct levels of hierarchy. More specifically, we propose a hierarchical encoder-decoder network, with a distinct Global-Local Spatio-Contextual (GLSC) block for learning of fine-grained features and high-frequency details in an image. The proposed framework improves image denoising, as it allows the model to capture and utilize information from different scales, ensuring a comprehensive understanding of the image content. We demonstrate the efficacy of proposed HNN framework, on benchmark datasets in comparison with state-of-the-art methods with 5% (↑ in dB) increase in performance.

关键词： Attention mechanisms Noise Superresolution Spatial coherence Imaging Object detection Benchmark testing

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Multi-Explainable TemporalNet: An Interpretable Multimodal Approach using Temporal Convolutional Network for User-level Depression Detection

Multi-Explainable TemporalNet: An Interpretable Multimodal A...

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

作者： Anas Zafar Danyal Aftab Rizwan Qureshi Yaofeng Wang Hong Yan Fast School of Computing National University of Computer and Emerging Sciences Karachi Pakistan Technological University Dublin Dublin Ireland Center for Regenerative Medicine and Health Hong Kong Institute of Science and Innovation Chinese Academy of Sciences Hong Kong SAR China Department of Electrical Engineering City University of Hong Kong Hong Kong

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Multimodal depression detection through internet-based data such as social media platforms has been an important problem in the research community, aiming to predict human mental states for ensuring wellbeing of the society. Recently, attention-based networks have gained significant popularity for depression detection. However, existing multimodal methods primarily rely on images and text assuming no correlation between temporal aspects such as relative time of different posts or tweets, which is a crucial factor in deriving depression related behavior patterns. Moreover, they lack model interpretability resulting in limited understanding of how different features are contributing to the model’s final prediction. In this paper, we propose MultiExplainable TemporalNet (METN), a Temporal Convolution Network (TCN) based multi-modal transformer network with relative timestamp embeddings. We leverage pretrained foundation models for text and image embeddings and attention maps for model interpretability. We perform extensive experiments and ablation studies to validate the performance of METN for user-level depression detection task. Our model shows state-of-the-art results on various benchmarks, such as 0.945 F1 score on multimodal Twitter dataset, and 0.913 F1 score on multimodal Reddit dataset. We further demonstrate that our model enhances the accuracy of identifying depression in individuals who publicly post messages on social media platforms with enhanced interpretable compatibility. Code and models are available at Github. 1

关键词： Training Attention mechanisms Accuracy Social networking (online) Computational modeling Blogs Predictive models

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Unpaired Faces to Cartoons: Improving XGAN

Unpaired Faces to Cartoons: Improving XGAN

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

作者： Stev H. Ramos Joel Cabrera Daniel Ibá ñ ez Alejandro B. Jimé nez-Panta Cé sar Beltrá n-Castañ ó n Edwin Villanueva Pontifical Catholic University of Peru Lima Peru

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Domain Adaptation is a task that aims to translate an image from a source domain to a desired target domain. Current methods in domain adaptation use adversarial training based on Generative Adversarial Networks (GAN). In the present work, we focus on the task of domain adaptation from real faces to cartoon face images. We start from a baseline architecture called XGAN and introduce some improvements to it. Our proposed model is called W-XDGAN, which uses a form of GAN called Wasserstein-GAN, learns to approximate the Wasserstein Distance, and adds a denoiser to smooth the output cartoons. Whereas the original XGAN paper only presented a qualitative analysis, the advantages of this solution are demonstrated both quantitatively and qualitatively by comparing the results with models such as UNIT and original XGAN. Our code and models are publicly available at https://***/IAmigos/avatar-image-generator.

关键词： Training computer vision Codes Computational modeling conferences computer architecture Generative adversarial networks

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The 8th AI City Challenge

The 8th AI City Challenge

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

作者： Shuo Wang David C. Anastasiu Zheng Tang Ming-Ching Chang Yue Yao Liang Zheng Mohammed Shaiqur Rahman Meenakshi S. Arya Anuj Sharma Pranamesh Chakraborty Sanjita Prajapati Quan Kong Norimasa Kobori Munkhjargal Gochoo Munkh-Erdene Otgonbold Fady Alnajjar Ganzorig Batnasan Ping-Yang Chen Jun-Wei Hsieh Xunlei Wu Sameer Satish Pusegaonkar Yizhou Wang Sujit Biswas Rama Chellappa NVIDIA Corporation CA USA Santa Clara University CA USA University at Albany SUNY NY USA Australian National University Australia Iowa State University IA USA Indian Institute of Technology Kanpur India Woven by Toyota Japan United Arab Emirates University UAE Emirates Center for Mobility Research UAE National Yang-Ming Chiao-Tung University Taiwan Johns Hopkins University MD USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The eighth AI City Challenge highlighted the convergence of computer vision and artificial intelligence in areas like retail, warehouse settings, and Intelligent Traffic Systems (ITS), presenting significant research opportunities. The 2024 edition featured five tracks, attracting unprecedented interest from 726 teams in 47 countries and regions. Track 1 dealt with multi-target multi-camera (MTMC) people tracking, highlighting significant enhancements in camera count, character number, 3D annotation, and camera matrices, alongside new rules for 3D tracking and online tracking algorithm encouragement. Track 2 introduced dense video captioning for traffic safety, focusing on pedestrian accidents using multi-camera feeds to improve insights for insurance and prevention. Track 3 required teams to classify driver actions in a naturalistic driving analysis. Track 4 explored fish-eye camera analytics using the FishEye8K dataset. Track 5 focused on motorcycle helmet rule violation detection. The challenge utilized two leaderboards to showcase methods, with participants setting new benchmarks, some surpassing existing state-of-the-art achievements.

关键词： computer vision Three-dimensional displays Pedestrians Prevention and mitigation Urban areas Cameras Safety

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Source-Free Domain Adaptation of Weakly-Supervised Object Localization Models for Histology

Source-Free Domain Adaptation of Weakly-Supervised Object Lo...

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

作者： Alexis Guichemerre Soufiane Belharbi Tsiry Mayet Shakeeb Murtaza Pourya Shamsolmoali Luke McCaffrey Eric Granger Dept. of Systems Engineering LIVIA ETS Montreal Canada INSA LITIS UR 4108 Rouen France School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast UK Dept. of Oncology Goodman Cancer Research Centre McGill University Montreal Canada

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Given the emergence of deep learning, digital pathology has gained popularity for cancer diagnosis based on histology images. Deep weakly supervised object localization (WSOL) models can be trained to classify histology images according to cancer grade and identify regions of interest (ROIs) for interpretation, using inexpensive global image-class annotations. A WSOL model initially trained on some labeled source image data can be adapted using unlabeled target data in cases of significant domain shifts caused by variations in staining, scanners, and cancer type. In this paper, we focus on source-free (unsupervised) domain adaptation (SFDA), a challenging problem where a pre-trained source model is adapted to a new target domain without using any source domain data for privacy and efficiency reasons. SFDA of WSOL models raises several challenges in histology, most notably because they are not intended to adapt for both classification and localization tasks. In this paper, 4 state-of-the-art SFDA methods, each one representative of a main SFDA family, are compared for WSOL in terms of classification and localization accuracy. They are the SFDA-Distribution Estimation, Source HypOthesis Transfer, Cross-Domain Contrastive Learning, and Adaptively Domain Statistics Alignment. Experimental results on the challenging Glas (smaller, breast cancer) and Came-lyon16 (larger, colon cancer) histology datasets indicate that these SFDA methods typically perform poorly for localization after adaptation when optimized for classification. Code: ***/AlexisGuichemerreCode/survey_hist_wsol_sfda

关键词： Location awareness Deep learning Adaptation models Data privacy Histopathology Estimation Contrastive learning

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MoCap-to-Visual Domain Adaptation for Efficient Human Mesh Estimation from 2D Keypoints

MoCap-to-Visual Domain Adaptation for Efficient Human Mesh E...

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

作者： Bedirhan Uguz Ozhan Suat Batuhan Karagoz Emre Akbas Department of Computer Engineering Middle East Technical University Ankara Turkey METU ROMER Robotics Center Middle East Technical University Ankara Turkey

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper presents Key2Mesh, a model that takes a set of 2D human pose keypoints as input and estimates the corresponding body mesh. Since this process does not involve any visual (i.e. RGB image) data, the model can be trained on large-scale motion capture (MoCap) datasets, thereby overcoming the scarcity of image datasets with 3D labels. To enable the model’s application on RGB images, we first run an off-the-shelf 2D pose estimator to obtain the 2D keypoints, and then feed these 2D keypoints to Key2Mesh. To improve the performance of our model on RGB images, we apply an adversarial domain adaptation (DA) method to bridge the gap between the MoCap and visual domains. Crucially, our DA method does not require 3D labels for visual data, which enables adaptation to target sets without the need for costly labels. We evaluate Key2Mesh for the task of estimating 3D human meshes from 2D key-points, in the absence of RGB and mesh label pairs. Our results on widely used H3.6M and 3DPW datasets show that Key2Mesh sets the new state-of-the-art by outperforming other models in PA-MPJPE for both datasets, and in MPJPE and PVE for the 3DPW dataset. Thanks to our model’s simple architecture, it operates at least 12× faster than the prior state-of-the-art model, LGD [44]. Additional qualitative samples and code are available on the project website: https://***/.

关键词： Training Adaptation models Visualization Solid modeling Three-dimensional displays Computational modeling Training data

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Self-Supervised Learning of Pose-Informed Latents

Self-Supervised Learning of Pose-Informed Latents

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

作者： Raphaë l Jean Pierre-Luc St-Charles Sö ren Pirk Simon Brodeur Menya Solutions Mila Mila AMLRT Google Research

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Siamese network architectures trained for self-supervised instance recognition can learn powerful visual representations that are useful in various tasks. Many such approaches maximize the similarity between representations of augmented images of the same object. In this paper, we depart from traditional self-supervised learning benchmarks by defining a novel methodology for new challenging tasks such as zero shot pose estimation. Our goal is to show that common Siamese networks can effectively be trained on frame pairs from video sequences to generate pose-informed representations. Unlike parallel efforts that focus on introducing new image-space operators for data augmentation, we argue that extending the augmentation strategy by using different frames of a video leads to more powerful representations. To show the effectiveness of this approach, we use the Objectron and UCF101 datasets to learn representations and evaluate them on pose estimation, action recognition, and object re-identification. Furthermore, we carefully validate our method against a number of baselines.

关键词： Training computer vision Visualization Pose estimation Video sequences Self-supervised learning Benchmark testing

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Continual Hippocampus Segmentation with Transformers

Continual Hippocampus Segmentation with Transformers

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

作者： Amin Ranem Camila Gonzá lez Anirban Mukhopadhyay GRIS Technical University of Darmstadt Darmstadt Germany

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

In clinical settings, where acquisition conditions and patient populations change over time, continual learning is key for ensuring the safe use of deep neural networks. Yet most existing work focuses on convolutional architectures and image classification. Instead, radiologists prefer to work with segmentation models that outline specific regions-of-interest, for which Transformer-based architectures are gaining traction. The self-attention mechanism of Transformers could potentially mitigate catastrophic forgetting, opening the way for more robust medical image segmentation. In this work, we explore how recently-proposed Transformer mechanisms for semantic segmentation behave in sequential learning scenarios, and analyse how best to adapt continual learning strategies for this setting. Our evaluation on hippocampus segmentation shows that Transformer mechanisms mitigate catastrophic forgetting for medical image segmentation compared to purely convolutional architectures, and demonstrates that regularising ViT modules should be done with caution.

关键词： Image segmentation Semantics Sociology computer architecture Artificial neural networks Transformers Task analysis

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NTIRE 2023 Challenge on Efficient Super-Resolution: Methods and Results

NTIRE 2023 Challenge on Efficient Super-Resolution: Methods ...

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

作者： Yawei Li Yulun Zhang Radu Timofte Luc Van Gool Lei Yu Youwei Li Xinpeng Li Ting Jiang Qi Wu Mingyan Han Wenjie Lin Chengzhi Jiang Jinting Luo Haoqiang Fan Shuaicheng Liu Yucong Wang Minjie Cai Mingxi Li Yuhang Zhang Xian-Jun Fan Yankai Sheng Yanyu Mao Nihao Zhang Qian Wang Mingjun Zheng Long Sun Jinshan Pan Jiangxin Dong Jinhui Tang Zhongbao Yang Yan Wang Erlin Pan Qixuan Cai Xinan Dai Magauiya Zhussip Nikolay Kalyazin Dmitry Vyal Xueyi Zou Youliang Yan Heaseo Chung Jin Zhang Gaocheng Yu Feng Zhang Hongbin Wang Bohao Liao Zhibo Du Yu-Liang Wu Gege Shi Long Peng Yang Wang Yang Cao Zhengjun Zha Zhi-Kai Huang Yi-Chung Chen Yuan-Chun Chiang Hao-Hsiang Yang Wei-Ting Chen Hua-En Chang I-Hsiang Chen Chia-Hsuan Hsieh Sy-Yen Kuo Xin Liu Jiahao Pan Hongyuan Yu Weichen Yu Lin Ge Jiahua Dong Yajun Zou Zhuoyuan Wu Binnan Han Xiaolin Zhang Heng Zhang Xuanwu Yin Kunlong Zuo Weijian Deng Hongjie Yuan Zengtong Lu Mingyu Ouyang Wenzhuo Ma Nian Liu Hanyou Zheng Yuantong Zhang Junxi Zhang Zhenzhong Chen Garas Gendy Nabil Sabor Jingchao Hou Guanghui He Yurui Zhu Xi Wang Xueyang Fu Zheng-Jun Zha Daheng Yin Mengyang Liu Baijun Chen Ao Li Lei Luo Kangjun Jin Ce Zhu Xiaoming Zhang Chengxing Xie Linze Li Haiteng Meng Tianlin Zhang Tianrui Li Xiaole Zhao Zhao Zhang Baiang Li Huan Zheng Suiyi Zhao Yangcheng Gao Jiahuan Ren Kang Hu Jingpeng Shi Zhijian Wu Dingjiang Huang Jinchen Zhu Hui Li Qianru Xv Tianle Liu Shizhuang Weng Gang Wu Junpeng Jiang Xianming Liu Junjun Jiang Mingjian Zhang Jing Hu Chengxu Wu Qinrui Fan Chengming Feng Ziwei Luo Shu Hu Siwei Lyu Xi Wu Xin Wang Computer Vision Lab ETH Zurich

This paper reviews the NTIRE 2023 challenge on efficient single-image super-resolution with a focus on the proposed solutions and results. The aim of this challenge is to devise a network that reduces one or several aspects such as runtime, parameters, FLOPs, activations, memory footprint, and depth of RFDN while at least maintaining the PSNR of 29.00dB on DIV2K validation datasets. The challenge had 272 registered participants, and 35 teams made valid submissions. They gauge the state-of-the-art for efficient single-image super-resolution.

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Unpaired Real-World Super-Resolution with Pseudo Controllable Restoration

Unpaired Real-World Super-Resolution with Pseudo Controllabl...

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

作者： André s Romero Luc Van Gool Radu Timofte Computer Vision Lab ETH Z&#x00FC rich KU Leuven University of W&#x00FC rzburg

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Current super-resolution methods rely on the bicubic down-sampling assumption in order to develop the ill-posed reconstruction of the low-resolution image. Not surprisingly, these approaches fail when using real-world low-resolution images due to the presence of artifacts and intrinsic noise absent in the bicubic setup. Consequently, attention is increasingly paid to techniques that alleviate this problem and super-resolve real-world images. As acquiring paired real-world datasets is a challenging problem, real-world super-resolution solutions are traditionally tackled as a blind problem or as an unpaired data-driven problem. The former makes assumptions about the downsampling operations, the latter uses unpaired training to learn the real distributions. Recently, blind approaches have dominated this problem by assuming a diverse bank of degradations, whereas the unpaired solutions have shown under-performance due to the two-staged training. In this paper, we propose an unpaired real-world super-resolution method that performs on par, or even better than blind paired approaches by introducing a pseudo-controllable restoration module in a fully end-to-end system.

关键词： Training Degradation computer vision conferences Superresolution Image restoration pattern recognition

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