Results 31 to 40 of about 38,613 (267)
Zero-Shot Semantic Segmentation
CoRR, 2019 Semantic segmentation models are limited in their ability to scale to large numbers of object classes. In this paper, we introduce the new task of zero-shot semantic segmentation: learning pixel-wise classifiers for never-seen object categories with zero training examples.
Bucher, Maxime +3 more
openaire +4 more sources
Ordinal Zero-Shot Learning [PDF]
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017 Zero-shot learning predicts new class even if no training data is available for that class. The solution to conventional zero-shot learning usually depends on side information such as attribute or text corpora. But these side information is not easy to obtain or use.
Zeng-Wei Huo, Xin Geng 0001
openaire +1 more source
A Survey of Zero-Shot Image Classification: Concepts, Developments, and Challenges
Tsinghua Science and TechnologyZero-shot learning is gaining increasing attention in the social computing community, primarily because it can enable models to effectively perform classification or regression tasks when new concepts continually emerge while lacking sufficient training ...
Shuai Xu +5 more
doaj +1 more source
Machine Learning and Knowledge ExtractionObjectives: To guide language model (LM) selection by comparing finetuning vs. zero-shot use, generic pretraining vs. domain-adjacent vs. further domain-specific pretraining, and bidirectional language models (BiLMs) such as BERT vs.
Lovedeep Gondara +5 more
doaj +1 more source
Characterizing Word Embeddings for Zero-Shot Sensor-Based Human Activity Recognition
Sensors, 2019 In this paper, we address Zero-shot learning for sensor activity recognition using word embeddings. The goal of Zero-shot learning is to estimate an unknown activity class (i.e., an activity that does not exist in a given training dataset) by learning to
Moe Matsuki, Paula Lago, Sozo Inoue
doaj +1 more source
CoRR, 2021 Performance of recommender systems (RS) relies heavily on the amount of training data available. This poses a chicken-and-egg problem for early-stage products, whose amount of data, in turn, relies on the performance of their RS. On the other hand, zero-shot learning promises some degree of generalization from an old dataset to an entirely new dataset.
Hao Ding 0003 +4 more
openaire +2 more sources
Toward Zero-Shot and Zero-Resource Multilingual Question Answering
IEEE Access, 2022 In recent years, multilingual question answering has been an emergent research topic and has attracted much attention. Although systems for English and other rich-resource languages that rely on various advanced deep learning-based techniques have been ...
Chia-Chih Kuo, Kuan-Yu Chen
doaj +1 more source
FEBS Letters, EarlyView.Inositol pyrophosphates are energy‐rich signaling molecules that perform critical functions in cells. Three different families of phosphatases hydrolyze the β phosphate of the inositol pyrophosphate molecules: two have narrow specificities and one is promiscuous.
Ronda J. Rolfes
wiley +1 more source
“Zero-Shot” Point Cloud Upsampling
2022 IEEE International Conference on Multimedia and Expo (ICME), 2022 Recent supervised point cloud upsampling methods are restricted by the size of training data and are limited in terms of covering all object shapes. Besides the challenges faced due to data acquisition, the networks also struggle to generalize on unseen records.
Kaiyue Zhou +2 more
openaire +2 more sources