ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2204.04567
  4. Cited By
Joint Distribution Matters: Deep Brownian Distance Covariance for
  Few-Shot Classification

Joint Distribution Matters: Deep Brownian Distance Covariance for Few-Shot Classification

9 April 2022
Jiangtao Xie
Fei Long
Jiaming Lv
Qilong Wang
P. Li
ArXivPDFHTML

Papers citing "Joint Distribution Matters: Deep Brownian Distance Covariance for Few-Shot Classification"

16 / 16 papers shown
Title
Tiny models from tiny data: Textual and null-text inversion for few-shot distillation
Tiny models from tiny data: Textual and null-text inversion for few-shot distillation
Erik Landolsi
Fredrik Kahl
DiffM
55
1
0
05 Jun 2024
HQG-Net: Unpaired Medical Image Enhancement with High-Quality Guidance
HQG-Net: Unpaired Medical Image Enhancement with High-Quality Guidance
Chunming He
Kai Li
Guoxia Xu
Jiangpeng Yan
Longxiang Tang
Yulun Zhang
Xiu Li
Yao Wang
MedIm
24
37
0
15 Jul 2023
FILM: How can Few-Shot Image Classification Benefit from Pre-Trained
  Language Models?
FILM: How can Few-Shot Image Classification Benefit from Pre-Trained Language Models?
Zihao Jiang
Yunkai Dang
Dong Pang
Huishuai Zhang
Weiran Huang
VLM
26
2
0
09 Jul 2023
An Adaptive Plug-and-Play Network for Few-Shot Learning
An Adaptive Plug-and-Play Network for Few-Shot Learning
Hao Li
Li Li
Yun-Ya Huang
Ning Li
Yongtao Zhang
21
3
0
18 Feb 2023
Reference Twice: A Simple and Unified Baseline for Few-Shot Instance
  Segmentation
Reference Twice: A Simple and Unified Baseline for Few-Shot Instance Segmentation
Yue Han
Jiangning Zhang
Zhucun Xue
Chao Xu
Xintian Shen
Yabiao Wang
Chengjie Wang
Yong Liu
Xiangtai Li
34
17
0
03 Jan 2023
Few-shot Classification with Hypersphere Modeling of Prototypes
Few-shot Classification with Hypersphere Modeling of Prototypes
Ning Ding
Yulin Chen
Ganqu Cui
Xiaobin Wang
Haitao Zheng
Zhiyuan Liu
Pengjun Xie
23
8
0
10 Nov 2022
Few-shot Fine-grained Image Classification via Multi-Frequency
  Neighborhood and Double-cross Modulation
Few-shot Fine-grained Image Classification via Multi-Frequency Neighborhood and Double-cross Modulation
Hegui Zhu
Zhan Gao
Jiayi Wang
Yangqiaoyu Zhou
Chengqing Li
11
6
0
18 Jul 2022
Rethinking Generalization in Few-Shot Classification
Rethinking Generalization in Few-Shot Classification
Markus Hiller
Rongkai Ma
Mehrtash Harandi
Tom Drummond
OCL
VLM
17
55
0
15 Jun 2022
A Comprehensive Survey of Few-shot Learning: Evolution, Applications,
  Challenges, and Opportunities
A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities
Yisheng Song
Ting-Yuan Wang
S. Mondal
J. P. Sahoo
SLR
41
342
0
13 May 2022
LibFewShot: A Comprehensive Library for Few-shot Learning
LibFewShot: A Comprehensive Library for Few-shot Learning
Wenbin Li
Ziyi
Ziyi Wang
Xuesong Yang
C. Dong
...
Jing Huo
Yinghuan Shi
Lei Wang
Yang Gao
Jiebo Luo
VLM
108
66
0
10 Sep 2021
CrossTransformers: spatially-aware few-shot transfer
CrossTransformers: spatially-aware few-shot transfer
Carl Doersch
Ankush Gupta
Andrew Zisserman
ViT
201
330
0
22 Jul 2020
Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning
Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning
Yinbo Chen
Zhuang Liu
Huijuan Xu
Trevor Darrell
Xiaolong Wang
160
340
0
09 Mar 2020
Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness
  of MAML
Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML
Aniruddh Raghu
M. Raghu
Samy Bengio
Oriol Vinyals
177
639
0
19 Sep 2019
Delta-encoder: an effective sample synthesis method for few-shot object
  recognition
Delta-encoder: an effective sample synthesis method for few-shot object recognition
Eli Schwartz
Leonid Karlinsky
J. Shtok
Sivan Harary
Mattias Marder
Rogerio Feris
Abhishek Kumar
Raja Giryes
A. Bronstein
186
351
0
12 Jun 2018
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
311
11,681
0
09 Mar 2017
Measuring and testing dependence by correlation of distances
Measuring and testing dependence by correlation of distances
G. Székely
Maria L. Rizzo
N. K. Bakirov
175
2,577
0
28 Mar 2008
1