Few-shot vision recognition and generation for the open-world
Deep Neural Networks (DNNs) have achieved remarkable success across various computer vision tasks, but their reliance on extensive labeled datasets limits their applicability in data-scarce scenarios. Few-shot learning offers a promising solution by enabling models to learn from minimal data, yet tr...
Saved in:
Main Author: | Song, Nan |
---|---|
Other Authors: | Lin Guosheng |
Format: | Thesis-Doctor of Philosophy |
Language: | English |
Published: |
Nanyang Technological University
2024
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/181293 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
FEW-SHOT IMAGE RECOGNITION AND OBJECT DETECTION
by: LI YITING
Published: (2023) -
Anti-spoofing few-shot learning model for face recognition
by: Ang, Ting Feng
Published: (2024) -
Multimodal few-shot classification without attribute embedding
by: Chang, Jun Qing, et al.
Published: (2024) -
MuLAN: multi-level attention-enhanced matching network for few-shot knowledge graph completion
by: Li, Qianyu, et al.
Published: (2024) -
Few-shot learning in Wi-Fi-based indoor positioning
by: Xie, Feng, et al.
Published: (2024)