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. 2110.09446
  4. Cited By
Squeezing Backbone Feature Distributions to the Max for Efficient
  Few-Shot Learning

Squeezing Backbone Feature Distributions to the Max for Efficient Few-Shot Learning

18 October 2021
Yuqing Hu
Vincent Gripon
S. Pateux
ArXivPDFHTML

Papers citing "Squeezing Backbone Feature Distributions to the Max for Efficient Few-Shot Learning"

13 / 13 papers shown
Title
SgVA-CLIP: Semantic-guided Visual Adapting of Vision-Language Models for
  Few-shot Image Classification
SgVA-CLIP: Semantic-guided Visual Adapting of Vision-Language Models for Few-shot Image Classification
Fang Peng
Xiaoshan Yang
Linhui Xiao
Yaowei Wang
Changsheng Xu
VLM
35
43
0
28 Nov 2022
Reconciliation of Pre-trained Models and Prototypical Neural Networks in
  Few-shot Named Entity Recognition
Reconciliation of Pre-trained Models and Prototypical Neural Networks in Few-shot Named Entity Recognition
Youcheng Huang
Wenqiang Lei
Jie Fu
Jiancheng Lv
6
3
0
07 Nov 2022
Adaptive Dimension Reduction and Variational Inference for Transductive
  Few-Shot Classification
Adaptive Dimension Reduction and Variational Inference for Transductive Few-Shot Classification
Yuqing Hu
S. Pateux
Vincent Gripon
32
15
0
18 Sep 2022
Class-Specific Channel Attention for Few-Shot Learning
Ying Chen
J. Hsieh
Ming-Ching Chang
20
0
0
03 Sep 2022
Transductive Decoupled Variational Inference for Few-Shot Classification
Transductive Decoupled Variational Inference for Few-Shot Classification
Ashutosh Kumar Singh
Hadi Jamali Rad
BDL
VLM
39
17
0
22 Aug 2022
Dynamic Sub-Cluster-Aware Network for Few-Shot Skin Disease
  Classification
Dynamic Sub-Cluster-Aware Network for Few-Shot Skin Disease Classification
Li Shuhan
Xiaomeng Li
Xiaowei Xu
Kwang-Ting Cheng
27
6
0
03 Jul 2022
Do Vision-Language Pretrained Models Learn Composable Primitive
  Concepts?
Do Vision-Language Pretrained Models Learn Composable Primitive Concepts?
Tian Yun
Usha Bhalla
Ellie Pavlick
Chen Sun
ReLM
CoGe
VLM
LRM
31
23
0
31 Mar 2022
Delving Deep into One-Shot Skeleton-based Action Recognition with
  Diverse Occlusions
Delving Deep into One-Shot Skeleton-based Action Recognition with Diverse Occlusions
Kunyu Peng
Alina Roitberg
Kailun Yang
Jiaming Zhang
Rainer Stiefelhagen
ViT
21
28
0
23 Feb 2022
EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art
  Few-Shot Classification with Simple Ingredients
EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients
Yassir Bendou
Yuqing Hu
Raphael Lafargue
G. Lioi
Bastien Pasdeloup
S. Pateux
Vincent Gripon
VLM
38
37
0
24 Jan 2022
Few-shot Open-set Recognition by Transformation Consistency
Few-shot Open-set Recognition by Transformation Consistency
Minki Jeong
Seokeon Choi
Changick Kim
46
49
0
02 Mar 2021
Free Lunch for Few-shot Learning: Distribution Calibration
Free Lunch for Few-shot Learning: Distribution Calibration
Shuo Yang
Lu Liu
Min Xu
OODD
213
322
0
16 Jan 2021
Frustratingly Simple Few-Shot Object Detection
Frustratingly Simple Few-Shot Object Detection
Xin Wang
Thomas E. Huang
Trevor Darrell
Joseph E. Gonzalez
F. I. F. Richard Yu
ObjD
95
544
0
16 Mar 2020
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
338
11,684
0
09 Mar 2017
1