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检索条件"机构=Institute of Computer Vision and Machine Learning"
79 条 记 录,以下是11-20 订阅
排序:
vision-Language Guidance for LiDAR-based Unsupervised 3D Object Detection
arXiv
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arXiv 2024年
作者: Fruhwirth-Reisinger, Christian Lin, Wei Malić, Dušan Bischof, Horst Possegger, Horst Christian Doppler Laboratory for Embedded Machine Learning Austria Institute of Computer Graphics and Vision Graz University of Technology Austria Institute for Machine Learning Johannes Kepler University Linz Austria
Accurate 3D object detection in LiDAR point clouds is crucial for autonomous driving systems. To achieve state-of-the-art performance, the supervised training of detectors requires large amounts of human-annotated dat... 详细信息
来源: 评论
RCCNet: An Efficient Convolutional Neural Network for Histological Routine Colon Cancer Nuclei Classification
RCCNet: An Efficient Convolutional Neural Network for Histol...
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International Conference on Control, Automation, Robotics and vision
作者: S H Shabbeer Basha Soumen Ghosh Kancharagunta Kishan Babu Shiv Ram Dubey Viswanath Pulabaigari Snehasis Mukherjee Computer Vision and Machine Learning Groups Indian Institute of Information Technology Sri City Andhra Pradesh India
Efficient and precise classification of histological cell nuclei is of utmost importance due to its potential applications in the field of medical image analysis. It would facilitate the medical practitioners to bette... 详细信息
来源: 评论
Determining Mice Sex from Chest X-rays using Deep learning
Determining Mice Sex from Chest X-rays using Deep Learning
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International Conference on Cyberspace (CYBER)
作者: Abiodun Ajiboye Kola Babalola Institute of Computer Vision and Machine Learning Lagos Nigeria European Molecular Biology Laboratory European Bioinformatics Institute Cambridgshire UK
This Following on from work by Babalola et al. It is shown that the sex of mice can be determined from x-ray images of the chest region alone using convolutional neural networks. The anatomical differences that may be... 详细信息
来源: 评论
The Norm Must Go On: Dynamic Unsupervised Domain Adaptation by Normalization
arXiv
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arXiv 2021年
作者: Mirza, Muhammad Jehanzeb Micorek, Jakub Possegger, Horst Bischof, Horst Institute for Computer Graphics and Vision Graz University of Technology Austria Christian Doppler Laboratory for Embedded Machine Learning Austria
Domain adaptation is crucial to adapt a learned model to new scenarios, such as domain shifts or changing data distributions. Current approaches usually require a large amount of labeled or unlabeled data from the shi... 详细信息
来源: 评论
RCCNet: An efficient convolutional neural network for histological routine colon cancer nuclei classification
arXiv
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arXiv 2018年
作者: Basha, S.H. Shabbeer Ghosh, Soumen Babu, Kancharagunta Kishan Dubey, Shiv Ram Pulabaigari, Viswanath Mukherjee, Snehasis Computer Vision and Machine Learning Groups Indian Institute of Information Technology Sri CityAndhra Pradesh517646 India
Efficient and precise classification of histological cell nuclei is of utmost importance due to its potential applications in the field of medical image analysis. It would facilitate the medical practitioners to bette... 详细信息
来源: 评论
Constrained parametric min-cuts for automatic object segmentation
Constrained parametric min-cuts for automatic object segment...
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Conference on computer vision and Pattern Recognition (CVPR)
作者: Joao Carreira Cristian Sminchisescu Computer Vision and Machine Learning Group Institute for Numerical Simulation Faculty of Mathematics and Natural Sciences University of Bonn Germany
We present a novel framework for generating and ranking plausible objects hypotheses in an image using bottom-up processes and mid-level cues. The object hypotheses are represented as figure-ground segmentations, and ... 详细信息
来源: 评论
SAILOR: Scaling Anchors via Insights into Latent Object Representation
arXiv
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arXiv 2022年
作者: Malić, Dušan Fruhwirth-Reisinger, Christian Possegger, Horst Bischof, Horst Institute of Computer Graphics and Vision Graz University of Technology Austria Christian Doppler Laboratory for Embedded Machine Learning Austria
LiDAR 3D object detection models are inevitably biased towards their training dataset. The detector clearly exhibits this bias when employed on a target dataset, particularly towards object sizes. However, object size... 详细信息
来源: 评论
Object recognition as ranking holistic figure-ground hypotheses
Object recognition as ranking holistic figure-ground hypothe...
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Conference on computer vision and Pattern Recognition (CVPR)
作者: Fuxin Li Joao Carreira Cristian Sminchisescu Computer Vision and Machine Learning Group Institute for Numerical Simulation Faculty of Mathematics and Natural Sciences University of Bonn Germany
We present an approach to visual object-class recognition and segmentation based on a pipeline that combines multiple, holistic figure-ground hypotheses generated in a bottom-up, object independent process. Decisions ... 详细信息
来源: 评论
An information-rich sampling technique over spatio-temporal CNN for classification of human actions in videos
arXiv
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arXiv 2020年
作者: Shabbeer Basha, S.H. Pulabaigari, Viswanath Mukherjee, Snehasis Computer Vision and Machine Learning Groups Indian Institute of Information Technology Sri City Andhra Pradesh517646 India
We propose a novel scheme for human action recognition in videos, using a 3-dimensional Convolutional Neural Network (3D CNN) based classifier. Traditionally in deep learning based human activity recognition approache... 详细信息
来源: 评论
AutoFCL: Automatically tuning fully connected layers for handling small dataset
arXiv
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arXiv 2020年
作者: Shabbeer Basha, S.H. Vinakota, Sravan Kumar Dubey, Shiv Ram Pulabaigari, Viswanath Mukherjee, Snehasis Computer Vision and Machine Learning Groups Indian Institute of Information Technology Sri City Andhra Pradesh517646 India
Deep Convolutional Neural Networks (CNN) have evolved as popular machine learning models for image classification during the past few years, due to their ability to learn the problem-specific features directly from th... 详细信息
来源: 评论