Results 41 to 50 of about 1,703,719 (384)
Customized Segment Anything Model for Medical Image Segmentation [PDF]
arXiv.org, 2023 We propose SAMed, a general solution for medical image segmentation. Different from the previous methods, SAMed is built upon the large-scale image segmentation model, Segment Anything Model (SAM), to explore the new research paradigm of customizing ...
Kaiwen Zhang, Dong Liu
semanticscholar +1 more source
On the Segmentation of Markets [PDF]
Journal of Political Economy, 2007 This paper endogenizes the market structure of an economy with heterogeneous agents who want to form bilateral matches in the presence of search frictions and when utility is nontransferable. There exist infinitely many marketplaces, and each agent chooses which marketplace to be in: agents get to choose not only whom to match with but also whom they ...
Jacquet, Nicolas Laurent +1 more
openaire +5 more sources
Text Segmentation by Cross Segment Attention [PDF]
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020 Document and discourse segmentation are two fundamental NLP tasks pertaining to breaking up text into constituents, which are commonly used to help downstream tasks such as information retrieval or text summarization. In this work, we propose three transformer-based architectures and provide comprehensive comparisons with previously proposed approaches
Gonçalo Simões +3 more
openaire +3 more sources
Object-Based Flood Mapping and Affected Rice Field Estimation with Landsat 8 OLI and MODIS Data
Remote Sensing, 2015 Cambodia is one of the most flood-prone countries in Southeast Asia. It is geographically situated in the downstream region of the Mekong River with a lowland floodplain in the middle, surrounded by plateaus and high mountains.
Phuong D. Dao, Yuei-An Liou
doaj +1 more source
BreaCNet: A high-accuracy breast thermogram classifier based on mobile convolutional neural network
Mathematical Biosciences and Engineering, 2022 The presence of a well-trained, mobile CNN model with a high accuracy rate is imperative to build a mobile-based early breast cancer detector. In this study, we propose a mobile neural network model breast cancer mobile network (BreaCNet) and its ...
Roslidar Roslidar +6 more
doaj +1 more source
Segmentation by transduction [PDF]
2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008 This paper addresses the problem of segmenting an image into regions consistent with user-supplied seeds (e.g., a sparse set of broad brush strokes). We view this task as a statistical transductive inference, in which some pixels are already associated with given zones and the remaining ones need to be classified.
Duchenne, Olivier +4 more
openaire +4 more sources
Cellpose: a generalist algorithm for cellular segmentation
Nature Methods, 2020 Many biological applications require the segmentation of cell bodies, membranes and nuclei from microscopy images. Deep learning has enabled great progress on this problem, but current methods are specialized for images that have large training datasets.
C. Stringer +3 more
semanticscholar +1 more source
CCNet: Criss-Cross Attention for Semantic Segmentation [PDF]
IEEE International Conference on Computer Vision, 2018 Full-image dependencies provide useful contextual information to benefit visual understanding problems. In this work, we propose a Criss-Cross Network (CCNet) for obtaining such contextual information in a more effective and efficient way.
Zilong Huang +6 more
semanticscholar +1 more source
Prosodic cues enhance rule learning by changing speech segmentation mechanisms
Frontiers in Psychology, 2015 Prosody has been claimed to have a critical role in the acquisition of grammatical information from speech. The exact mechanisms by which prosodic cues enhance learning are fully unknown.
Ruth eDe Diego-Balaguer +9 more
doaj +1 more source
BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation [PDF]
European Conference on Computer Vision, 2018 Semantic segmentation requires both rich spatial information and sizeable receptive field. However, modern approaches usually compromise spatial resolution to achieve real-time inference speed, which leads to poor performance.
Changqian Yu +5 more
semanticscholar +1 more source