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TGDM: Target Guided Dynamic Mixup for Cross-Domain Few-Shot Learning
v1v2 (latest)

TGDM: Target Guided Dynamic Mixup for Cross-Domain Few-Shot Learning

11 October 2022
Linhai Zhuo
Yu Fu
Jingjing Chen
Yixin Cao
Yu-Gang Jiang
ArXiv (abs)PDFHTML

Papers citing "TGDM: Target Guided Dynamic Mixup for Cross-Domain Few-Shot Learning"

34 / 34 papers shown
Title
TAVP: Task-Adaptive Visual Prompt for Cross-domain Few-shot Segmentation
TAVP: Task-Adaptive Visual Prompt for Cross-domain Few-shot Segmentation
Jiaqi Yang
Ye Huang
Jingxi Hu
Xiangjian He
Linlin Shen
Guoping Qiu
VLM
123
1
0
31 Dec 2024
Deep Learning for Cross-Domain Few-Shot Visual Recognition: A Survey
Deep Learning for Cross-Domain Few-Shot Visual Recognition: A Survey
Huali Xu
Shuaifeng Zhi
Shuzhou Sun
Vishal M. Patel
Li Liu
128
14
0
15 Mar 2023
Wave-SAN: Wavelet based Style Augmentation Network for Cross-Domain
  Few-Shot Learning
Wave-SAN: Wavelet based Style Augmentation Network for Cross-Domain Few-Shot Learning
Yuqian Fu
Yu Xie
Yanwei Fu
Jingjing Chen
Yu-Gang Jiang
68
20
0
15 Mar 2022
On the Importance of Distractors for Few-Shot Classification
On the Importance of Distractors for Few-Shot Classification
Rajshekhar Das
Yu-Xiong Wang
José M. F. Moura
76
29
0
20 Sep 2021
Self-Taught Cross-Domain Few-Shot Learning with Weakly Supervised Object
  Localization and Task-Decomposition
Self-Taught Cross-Domain Few-Shot Learning with Weakly Supervised Object Localization and Task-Decomposition
Xiyao Liu
Zhong Ji
Yanwei Pang
Zhongfei Zhang
69
17
0
03 Sep 2021
Information Symmetry Matters: A Modal-Alternating Propagation Network
  for Few-Shot Learning
Information Symmetry Matters: A Modal-Alternating Propagation Network for Few-Shot Learning
Zhong Ji
Zhishen Hou
Xiyao Liu
Yanwei Pang
Jungong Han
87
21
0
03 Sep 2021
Boosting the Generalization Capability in Cross-Domain Few-shot Learning
  via Noise-enhanced Supervised Autoencoder
Boosting the Generalization Capability in Cross-Domain Few-shot Learning via Noise-enhanced Supervised Autoencoder
Hanwen Liang
Qiong Zhang
Peng Dai
Juwei Lu
86
63
0
11 Aug 2021
Meta-FDMixup: Cross-Domain Few-Shot Learning Guided by Labeled Target
  Data
Meta-FDMixup: Cross-Domain Few-Shot Learning Guided by Labeled Target Data
Yu Fu
Yanwei Fu
Yu-Gang Jiang
67
66
0
26 Jul 2021
Cross-Domain Few-Shot Classification via Adversarial Task Augmentation
Cross-Domain Few-Shot Classification via Adversarial Task Augmentation
Haoqing Wang
Zhihong Deng
99
123
0
29 Apr 2021
Modular Adaptation for Cross-Domain Few-Shot Learning
Modular Adaptation for Cross-Domain Few-Shot Learning
Xiaoyu Lin
Meng Ye
Yunye Gong
G. Buracas
Nikoletta Basiou
Ajay Divakaran
Yi Yao
113
4
0
01 Apr 2021
Adversarially Optimized Mixup for Robust Classification
Adversarially Optimized Mixup for Robust Classification
Jason Bunk
Srinjoy Chattopadhyay
B. S. Manjunath
S. Chandrasekaran
AAML
124
8
0
22 Mar 2021
Dual-Awareness Attention for Few-Shot Object Detection
Dual-Awareness Attention for Few-Shot Object Detection
Tung-I Chen
Yueh-Cheng Liu
Hung-Ting Su
Yu-Cheng Chang
Yu-Hsiang Lin
Jia-Fong Yeh
Wen-Chin Chen
Winston H. Hsu
ObjD
100
98
0
24 Feb 2021
Cross-domain few-shot learning with unlabelled data
Cross-domain few-shot learning with unlabelled data
Fupin Yao
58
10
0
19 Jan 2021
Depth Guided Adaptive Meta-Fusion Network for Few-shot Video Recognition
Depth Guided Adaptive Meta-Fusion Network for Few-shot Video Recognition
Yuqian Fu
Li Zhang
Junke Wang
Yanwei Fu
Yu-Gang Jiang
72
97
0
20 Oct 2020
Self-training for Few-shot Transfer Across Extreme Task Differences
Self-training for Few-shot Transfer Across Extreme Task Differences
Cheng Perng Phoo
B. Hariharan
SSL
126
108
0
15 Oct 2020
Puzzle Mix: Exploiting Saliency and Local Statistics for Optimal Mixup
Puzzle Mix: Exploiting Saliency and Local Statistics for Optimal Mixup
Jang-Hyun Kim
Wonho Choo
Hyun Oh Song
AAML
94
390
0
15 Sep 2020
Explanation-Guided Training for Cross-Domain Few-Shot Classification
Explanation-Guided Training for Cross-Domain Few-Shot Classification
Jiamei Sun
Sebastian Lapuschkin
Wojciech Samek
Yunqing Zhao
Ngai-Man Cheung
Alexander Binder
72
89
0
17 Jul 2020
Learning to Select Base Classes for Few-shot Classification
Learning to Select Base Classes for Few-shot Classification
Linjun Zhou
Peng Cui
Xu Jia
Shiqiang Yang
Q. Tian
86
26
0
01 Apr 2020
Cross-Domain Few-Shot Classification via Learned Feature-Wise
  Transformation
Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformation
Hung-Yu Tseng
Hsin-Ying Lee
Jia-Bin Huang
Ming-Hsuan Yang
77
394
0
23 Jan 2020
AugMix: A Simple Data Processing Method to Improve Robustness and
  Uncertainty
AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty
Dan Hendrycks
Norman Mu
E. D. Cubuk
Barret Zoph
Justin Gilmer
Balaji Lakshminarayanan
OODUQCV
134
1,309
0
05 Dec 2019
MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with
  Meta-Learning
MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning
Zhijun Mai
Guosheng Hu
Dexiong Chen
Fumin Shen
Heng Tao Shen
57
42
0
27 Aug 2019
Few-Shot Video Classification via Temporal Alignment
Few-Shot Video Classification via Temporal Alignment
Kaidi Cao
Jingwei Ji
Zhangjie Cao
C. Chang
Juan Carlos Niebles
AI4TS
86
241
0
27 Jun 2019
MixUp as Directional Adversarial Training
MixUp as Directional Adversarial Training
Guillaume P. Archambault
Yongyi Mao
Hongyu Guo
Richong Zhang
AAML
55
23
0
17 Jun 2019
CutMix: Regularization Strategy to Train Strong Classifiers with
  Localizable Features
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
Sangdoo Yun
Dongyoon Han
Seong Joon Oh
Sanghyuk Chun
Junsuk Choe
Y. Yoo
OOD
624
4,814
0
13 May 2019
MixUp as Locally Linear Out-Of-Manifold Regularization
MixUp as Locally Linear Out-Of-Manifold Regularization
Hongyu Guo
Yongyi Mao
Richong Zhang
73
324
0
07 Sep 2018
Meta-Learning with Latent Embedding Optimization
Meta-Learning with Latent Embedding Optimization
Andrei A. Rusu
Dushyant Rao
Jakub Sygnowski
Oriol Vinyals
Razvan Pascanu
Simon Osindero
R. Hadsell
150
1,374
0
16 Jul 2018
Learning to Reweight Examples for Robust Deep Learning
Learning to Reweight Examples for Robust Deep Learning
Mengye Ren
Wenyuan Zeng
Binh Yang
R. Urtasun
OODNoLa
152
1,431
0
24 Mar 2018
Learning to Compare: Relation Network for Few-Shot Learning
Learning to Compare: Relation Network for Few-Shot Learning
Flood Sung
Yongxin Yang
Li Zhang
Tao Xiang
Philip Torr
Timothy M. Hospedales
314
4,054
0
16 Nov 2017
Few-Shot Learning with Graph Neural Networks
Few-Shot Learning with Graph Neural Networks
Victor Garcia Satorras
Joan Bruna
GNN
178
1,240
0
10 Nov 2017
mixup: Beyond Empirical Risk Minimization
mixup: Beyond Empirical Risk Minimization
Hongyi Zhang
Moustapha Cissé
Yann N. Dauphin
David Lopez-Paz
NoLa
316
9,815
0
25 Oct 2017
Prototypical Networks for Few-shot Learning
Prototypical Networks for Few-shot Learning
Jake C. Snell
Kevin Swersky
R. Zemel
305
8,164
0
15 Mar 2017
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Chelsea Finn
Pieter Abbeel
Sergey Levine
OOD
833
11,961
0
09 Mar 2017
Matching Networks for One Shot Learning
Matching Networks for One Shot Learning
Oriol Vinyals
Charles Blundell
Timothy Lillicrap
Koray Kavukcuoglu
Daan Wierstra
VLM
378
7,343
0
13 Jun 2016
Deep Residual Learning for Image Recognition
Deep Residual Learning for Image Recognition
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
MedIm
2.3K
194,641
0
10 Dec 2015
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