Image classification with limited data information
Image classification is a fundamental problem in image processing and computer vision. Recent algorithms have achieved significantly better results by learning deep features from large-scale datasets, such as ImageNet. However, in practice, challenges persist, especially with (I) low-quality image d...
Saved in:
Main Author: | Cheng, Hao |
---|---|
Other Authors: | Wen Bihan |
Format: | Thesis-Doctor of Philosophy |
Language: | English |
Published: |
Nanyang Technological University
2024
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/174167 |
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) -
PERSONALIZED VISUAL INFORMATION CAPTIONING
by: WU SHUANG
Published: (2023) -
Few-shot fine-grained classification with Spatial Attentive Comparison
by: Ruan, Xiaoqian, et al.
Published: (2022) -
Learning to Self-Train for Semi-Supervised Few-Shot Classification
by: Xinzhe Li, et al.
Published: (2020) -
HELA-VFA: a hellinger distance-attention-based feature aggregation network for few-shot classification
by: Lee, Gao Yu, et al.
Published: (2024)