Zobrazeno 1 - 10
of 1 414
pro vyhledávání: '"Stojanov, P."'
3D object part segmentation is essential in computer vision applications. While substantial progress has been made in 2D object part segmentation, the 3D counterpart has received less attention, in part due to the scarcity of annotated 3D datasets, w
Externí odkaz:
http://arxiv.org/abs/2407.09648
Autor:
Long, Bria, Xiang, Violet, Stojanov, Stefan, Sparks, Robert Z., Yin, Zi, Keene, Grace E., Tan, Alvin W. M., Feng, Steven Y., Zhuang, Chengxu, Marchman, Virginia A., Yamins, Daniel L. K., Frank, Michael C.
Human children far exceed modern machine learning algorithms in their sample efficiency, achieving high performance in key domains with much less data than current models. This ''data gap'' is a key challenge both for building intelligent artificial
Externí odkaz:
http://arxiv.org/abs/2406.10447
Gene regulatory network inference (GRNI) is a challenging problem, particularly owing to the presence of zeros in single-cell RNA sequencing data: some are biological zeros representing no gene expression, while some others are technical zeros arisin
Externí odkaz:
http://arxiv.org/abs/2403.15500
We study the problem of single-image zero-shot 3D shape reconstruction. Recent works learn zero-shot shape reconstruction through generative modeling of 3D assets, but these models are computationally expensive at train and inference time. In contras
Externí odkaz:
http://arxiv.org/abs/2312.14198
This paper introduces Low-shot Object Learning with Mutual Exclusivity Bias (LSME), the first computational framing of mutual exclusivity bias, a phenomenon commonly observed in infants during word learning. We provide a novel dataset, comprehensive
Externí odkaz:
http://arxiv.org/abs/2312.03533
Autor:
Kong, Lingjing, Xie, Shaoan, Yao, Weiran, Zheng, Yujia, Chen, Guangyi, Stojanov, Petar, Akinwande, Victor, Zhang, Kun
Unsupervised domain adaptation is critical to many real-world applications where label information is unavailable in the target domain. In general, without further assumptions, the joint distribution of the features and the label is not identifiable
Externí odkaz:
http://arxiv.org/abs/2306.06510
The growing trend of Large Language Models (LLM) development has attracted significant attention, with models for various applications emerging consistently. However, the combined application of Large Language Models with semantic technologies for re
Externí odkaz:
http://arxiv.org/abs/2305.04676
We present ShapeClipper, a novel method that reconstructs 3D object shapes from real-world single-view RGB images. Instead of relying on laborious 3D, multi-view or camera pose annotation, ShapeClipper learns shape reconstruction from a set of single
Externí odkaz:
http://arxiv.org/abs/2304.06247
A hallmark of the deep learning era for computer vision is the successful use of large-scale labeled datasets to train feature representations for tasks ranging from object recognition and semantic segmentation to optical flow estimation and novel vi
Externí odkaz:
http://arxiv.org/abs/2211.15059
Autor:
Walter Leal Filho, Robert Stojanov, Christos Matsoukas, Roberto Ingrosso, James A. Franke, Francesco S.R. Pausata, Tommaso Grassi, Jaromír Landa, Cherif Harrouni
Publikováno v:
Ecological Indicators, Vol 166, Iss , Pp 112287- (2024)
Oases are vulnerable ecosystems that are affected by climate change. Using high-resolution climate models focusing on northern Africa, we investigate the changes in the agrosystems of oases. Projected air temperature changes under an extreme global w
Externí odkaz:
https://doaj.org/article/7ce1ad5668ec4e1d84c488c26d7adfae