检索结果-内蒙古大学图书馆

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

来源：评论

学校读者我要写书评

暂无评论

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

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Correction to: Machine translation from signed to spoken languages: state of the art and challenges (Universal Access in the Information Society, (2023), 10.1007/s10209-023-00992-1)

引用

Universal Access in the Information Society 2024年第1期24卷 953-957页

作者： De Coster, Mathieu Shterionov, Dimitar Van Herreweghe, Mieke Dambre, Joni IDLab-AIRO Ghent University - imec Technologiepark-Zwijnaarde 126 Ghent9052 Belgium Tilburg University Warandelaan 2 Tilburg5037 AB Netherlands Ghent University Blandijnberg 2 Ghent9000 Belgium

In this article the incorrect Fig. 7 appear as below: (Figure presented.) The Fig. 7 should have appeared as shown below. (Figure presented.) Transformers tend to outperform RNNs on different SLT tasks in terms of BLEU-4 score on the RWTH-PHOENIX-Weather 2014T dataset In this article the incorrect Fig. 8 appear as below: (Figure presented.) The Fig. 8 should have appeared as shown below. (Figure presented.) Evolution of model scores on the RWTH-PHOENIX-Weather 2014T per task In this article the incorrect Table 6 appear as below: (Figure presented.) The Table 6 should have appeared as shown below. (Figure presented.) The Table 7 appears incorrectly as below. (Figure presented.) The correct Table 7 should be shown as below. (Figure presented.) This erratum contains changes to the findings from two of the cited papers. For citations [88] and [99]: [88] Chen, Y., Zuo, R., Wei, F., Wu, Y., Liu, S., & Mak, B.: Two-stream network for sign language recognition and translation. Advances in Neural Information Processing Systems, 35, 17043–17056 (2022) [99] Chen, Y., Wei, F., Sun, X., Wu, Z., Lin, S.: A simple multi-modality transfer learning baseline for sign language translation. In: Proceedings of the ieee/cvf conference on computer vision and pattern recognition, pp. 5120–5130 (2022) The original article has been corrected. © 2024, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词：

来源：评论

学校读者我要写书评

暂无评论

The 8th AI City Challenge

The 8th AI City Challenge

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

pCON: Polarimetric Coordinate Networks for Neural Scene Representations

pCON: Polarimetric Coordinate Networks for Neural Scene Repr...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Henry Peters Yunhao Ba Achuta Kadambi Computer Science Department University of California Los Angeles (UCLA) Electrical and Computer Engineering Department UCLA

Neural scene representations have achieved great success in parameterizing and reconstructing images, but current state of the art models are not optimized with the preservation of physical quantities in mind. While current architectures can reconstruct color images correctly, they create artifacts when trying to fit maps of polar quantities. We propose polarimetric coordinate networks (pCON), a new model architecture for neural scene representations aimed at preserving polarimetric information while accurately parameterizing the scene. Our model removes artifacts created by current coordinate network architectures when reconstructing three polarimetric quantities of interest. All code and data can be found at this link: http5://***/***.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Infinite Photorealistic Worlds Using Procedural Generation

Infinite Photorealistic Worlds Using Procedural Generation

引用

conference on computer vision and pattern recognition (CVPR)

作者： Alexander Raistrick Lahav Lipson Zeyu Ma Lingjie Mei Mingzhe Wang Yiming Zuo Karhan Kayan Hongyu Wen Beining Han Yihan Wang Alejandro Newell Hei Law Ankit Goyal Kaiyu Yang Jia Deng Department of Computer Science Princeton University

We introduce Infinigen, a procedural generator of photorealistic 3D scenes of the natural world. Infinigen is entirely procedural: every asset, from shape to texture, is generated from scratch via randomized mathematical rules, using no external source and allowing infinite variation and composition. Infinigen offers broad coverage of objects and scenes in the natural world including plants, animals, terrains, and natural phenomena such as fire, cloud, rain, and snow. Infinigen can be used to generate unlimited, diverse training data for a wide range of computer vision tasks including object detection, semantic segmentation, optical flow, and 3D reconstruction. We expect Infinigen to be a useful resource for computer vision research and beyond. Please visit *** for videos, code and pre-generated data.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Initialization Noise in Image Gradients and Saliency Maps

Initialization Noise in Image Gradients and Saliency Maps

引用

conference on computer vision and pattern recognition (CVPR)

作者： Ann-Christin Woerl Jan Disselhoff Michael Wand Institute of Computer Science Johannes Gutenberg University Mainz Germany

In this paper, we examine gradients of logits of image classification CNNs by input pixel values. We observe that these fluctuate considerably with training randomness, such as the random initialization of the networks. We extend our study to gradients of intermediate layers, obtained via GradCAM, as well as popular network saliency estimators such as DeepLIFT, SHAP, LIME, Integrated Gradients, and SmoothGrad. While empirical noise levels vary, qualitatively different attributions to image features are still possible with all of these, which comes with implications for interpreting such attributions, in particular when seeking data-driven explanations of the phenomenon generating the data. Finally, we demonstrate that the observed artefacts can be removed by marginalization over the initialization distribution by simple stochastic integration.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Alias-Free Convnets: Fractional Shift Invariance via Polynomial Activations

Alias-Free Convnets: Fractional Shift Invariance via Polynom...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Hagay Michaeli Tomer Michaeli Daniel Soudry Department of Electrical and Computer Engineering Technion Haifa Israel

Although CNNs are believed to be invariant to translations, recent works have shown this is not the case due to aliasing effects that stem from down-sampling layers. The existing architectural solutions to prevent the aliasing effects are partial since they do not solve those effects that originate in non-linearities. We propose an extended antialiasing method that tackles both down-sampling and nonlinear layers, thus creating truly alias-free, shift-invariant CNNs 1 1 Our code is available at ***/hmichaeli/alias_free_convnets/.. We show that the presented model is invariant to integer as well as fractional (i.e., sub-pixel) translations, thus outperforming other shift-invariant methods in terms of robustness to adversarial translations.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：