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检索条件"任意字段=1994 IEEE Computer-Society Conference on Computer Vision and Pattern Recognition"
22908 条 记 录,以下是51-60 订阅
Hairy Ground Truth Enhancement for Semantic Segmentation
Hairy Ground Truth Enhancement for Semantic Segmentation
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Fischer, Sophie Voiculescu, Irina Univ Oxford Dept Comp Sci Oxford England
Semantic segmentation is a key task within applications of machine learning for medical imaging, requiring large amounts of medical scans annotated by clinicians. The high cost of data annotation means that models nee... 详细信息
来源: 评论
DELTA: Decoupling Long-Tailed Online Continual Learning
DELTA: Decoupling Long-Tailed Online Continual Learning
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Raghavan, Siddeshwar He, Jiangpeng Zhu, Fengqing Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA
A significant challenge in achieving ubiquitous Artificial Intelligence is the limited ability of models to rapidly learn new information in real-world scenarios where data follows long-tailed distributions, all while... 详细信息
来源: 评论
One Embedding to Predict Them All: Visible and Thermal Universal Face Representations for Soft Biometric Estimation via vision Transformers
One Embedding to Predict Them All: Visible and Thermal Unive...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Mirabet-Herranz, Nelida Galdi, Chiara Dugelay, Jean-Luc EURECOM Campus SophiaTech450 Route Chappes F-06410 Biot France
Human faces encode a vast amount of information including not only uniquely distinctive features of the individual but also demographic information such as a person's age, gender, and weight. Such information is r... 详细信息
来源: 评论
Towards Engineered Safe AI with Modular Concept Models
Towards Engineered Safe AI with Modular Concept Models
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Heidemann, Lena Kurzidem, Iwo Monnet, Maureen Roscher, Karsten Guennemann, Stephan Fraunhofer IKS Munich Germany Tech Univ Munich Munich Germany
The inherent complexity and uncertainty of Machine Learning (ML) makes it difficult for ML-based computer vision (CV) approaches to become prevalent in safety-critical domains like autonomous driving, despite their hi... 详细信息
来源: 评论
Domain Targeted Synthetic Plant Style Transfer using Stable Diffusion, LoRA and ControlNet
Domain Targeted Synthetic Plant Style Transfer using Stable ...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Hartley, Zane K. J. Lind, Rob J. Pound, Michael P. French, Andrew P. Univ Nottingham Wollaton Rd Nottingham NG8 1BB England Syngenta Jealotts Hill Int Res Ctr Warfield England
Synthetic images can help alleviate much of the cost in the creation of training data for plant phenotyping-focused AI development. Synthetic-to-real style transfer is of particular interest to users of artificial dat... 详细信息
来源: 评论
Classifier Guided Cluster Density Reduction for Dataset Selection
Classifier Guided Cluster Density Reduction for Dataset Sele...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Chang, Cheng Long, Keyu Li, Zijian Rai, Himanshu Layer 6 AI Toronto ON Canada
In this paper, we address the challenge of selecting an optimal dataset from a source pool with annotations to enhance performance on a target dataset derived from a different source. This is important in scenarios wh... 详细信息
来源: 评论
A Perspective on Deep vision Performance with Standard Image and Video Codecs
A Perspective on Deep Vision Performance with Standard Image...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Reich, Christoph Hahn, Oliver Cremers, Daniel Roth, Stefan Debnath, Biplob Tech Univ Darmstadt Darmstadt Germany Tech Univ Munich Munich Germany NEC Labs Amer Inc San Jose CA 95110 USA Hessian Ctr AI Hessian AI Darmstadt Germany Munich Ctr Machine Learning MCML Munich Germany
Resource-constrained hardware, such as edge devices or cell phones, often rely on cloud servers to provide the required computational resources for inference in deep vision models. However, transferring image and vide... 详细信息
来源: 评论
ALINA: Advanced Line Identification and Notation Algorithm
ALINA: Advanced Line Identification and Notation Algorithm
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Khan, Mohammed Abdul Hafeez Ganeriwala, Parth Bhattacharyya, Siddhartha Neogi, Natasha Muthalagu, Raja Florida Inst Technol Melbourne FL 32901 USA NASA Langley Res Ctr Hampton VA 23665 USA BITS Pilani Dubai Campus Dubai U Arab Emirates
Labels are the cornerstone of supervised machine learning algorithms. Most visual recognition methods are fully supervised, using bounding boxes or pixel-wise segmentations for object localization. Traditional labelin... 详细信息
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VMRNN: Integrating vision Mamba and LSTM for Efficient and Accurate Spatiotemporal Forecasting
VMRNN: Integrating Vision Mamba and LSTM for Efficient and A...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Tang, Yujin Dong, Peijie Tang, Zhenheng Chu, Xiaowen Liang, Junwei Hong Kong Univ Sci & Technol Guangzhou AI Thrust Guangzhou Peoples R China Hong Kong Univ Sci & Technol Guangzhou DSA Thrust Guangzhou Peoples R China Hong Kong Baptist Univ Dept Comp Sci Hong Kong Peoples R China Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Hong Kong Peoples R China
Combining Convolutional Neural Networks (CNNs) or vision Transformers(ViTs) with Recurrent Neural Networks (RNNs) for spatiotemporal forecasting has yielded unparalleled results in predicting temporal and spatial dyna... 详细信息
来源: 评论
VLM-PL: Advanced Pseudo Labeling approach for Class Incremental Object Detection via vision-Language Model
VLM-PL: Advanced Pseudo Labeling approach for Class Incremen...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Kim, Junsu Ku, Yunhoe Kim, Jihyeon Cha, Junuk Baek, Seungryul UNIST Ulsan South Korea MODULABS Seoul South Korea
In the field of Class Incremental Object Detection (CIOD), creating models that can continuously learn like humans is a major challenge. Pseudo-labeling methods, although initially powerful, struggle with multi-scenar... 详细信息
来源: 评论