Results 11 to 20 of about 59,151 (296)

Rethinking Matching-Based Few-Shot Action Recognition

, 2023
Few-shot action recognition, i.e. recognizing new action classes given only a few examples, benefits from incorporating temporal information. Prior work either encodes such information in the representation itself and learns classifiers at test time, or obtains frame-level features and performs pairwise temporal matching.
Juliette Bertrand, Yannis Kalantidis, Giorgos Tolias   +2 more
openaire   +3 more sources

Loss Architecture Search for Few-Shot Object Recognition

Complexity, 2020
Few-shot object recognition, which exploits a set of well-labeled data to build a classifier for new classes that have only several samples per class, has received extensive attention from the machine learning community. In this paper, we investigate the
Jun Yue, Zelang Miao, Yueguang He, Nianchun Du   +3 more
doaj   +2 more sources

A Comprehensive Review of Few-Shot Action Recognition

International Journal of Computer Vision
Few-shot action recognition aims to address the high cost and impracticality of manually labeling complex and variable video data in action recognition. It requires accurately classifying human actions in videos using only a few labeled examples per class. Compared to few-shot learning in image scenarios, few-shot action recognition is more challenging
Yuyang Wanyan, Xiaoshan Yang, Weiming Dong, Changsheng Xu   +3 more
openaire   +3 more sources

Shaping Visual Representations With Attributes for Few-Shot Recognition

IEEE Signal Processing Letters, 2022
Few-shot recognition aims to recognize novel categories under low-data regimes. Some recent few-shot recognition methods introduce auxiliary semantic modality, i.e., category attribute information, into representation learning, which enhances the feature discrimination and improves the recognition performance.
Haoxing Chen, Huaxiong Li, Yaohui Li, Chunlin Chen 0001   +3 more
openaire   +3 more sources

Worst Case Matters for Few-Shot Recognition

, 2022
Accepted by ...
Minghao Fu 0001, Yun-Hao Cao, Jianxin Wu 0001   +2 more
openaire   +3 more sources

Temporal-Relational CrossTransformers for Few-Shot Action Recognition [PDF]

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021
Accepted in CVPR ...
Toby Perrett, Alessandro Masullo, Tilo Burghardt, Majid Mirmehdi, Dima Damen   +4 more
openaire   +5 more sources

HiTIM: Hierarchical Task Information Mining for Few-Shot Action Recognition

Applied Sciences, 2023
Although the existing few-shot action recognition methods have achieved impressive results, they suffer from two major shortcomings. (a) During feature extraction, few-shot tasks are not distinguished and task-irrelevant features are obtained, resulting ...
Li Jiang, Jiahao Yu, Yuanjie Dang, Peng Chen, Ruohong Huan   +4 more
doaj   +2 more sources

Few-shot Video Action Recognition Based on Two-stage Spatio-Temporal Alignment [PDF]

Jisuanji kexue
Few-shot video action recognition aims to construct efficient learning models using limited training samples,thereby reducing the dependence of traditional action recognition on large-scale and finely annotated datasets.At present,most few-shot learning ...
WANG Jia, XIA Ying, FENG Jiangfan
doaj   +2 more sources

Hybrid attentive prototypical network for few-shot action recognition

Complex & Intelligent Systems
Most previous few-shot action recognition works tend to process video temporal and spatial features separately, resulting in insufficient extraction of comprehensive features.
Zanxi Ruan, Yingmei Wei, Yanming Guo, Yuxiang Xie   +3 more
doaj   +2 more sources

CLIP-Driven Few-Shot Species-Recognition Method for Integrating Geographic Information

Remote Sensing
Automatic recognition of species is important for the conservation and management of biodiversity. However, since closely related species are visually similar, it is difficult to distinguish them by images alone.
Lei Liu, Linzhe Yang, Feng Yang, Feixiang Chen, Fu Xu   +4 more
doaj   +2 more sources