Cover Image

Intra-task Mutual Attention based Vision Transformer for Few-Shot Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Jiang, Weihao, Liu, Chang, He, Kun
Format:	Preprint
Published:	2024
Subjects:	Computer Vision and Pattern Recognition
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Boosting Meta-Training with Base Class Information for Few-Shot Learning
by: Jiang, Weihao, et al.
Published: (2024)

Class-relevant Patch Embedding Selection for Few-Shot Image Classification
by: Jiang, Weihao, et al.
Published: (2024)

Siamese Transformer Networks for Few-shot Image Classification
by: Jiang, Weihao, et al.
Published: (2024)

Inter- and Intra-image Refinement for Few Shot Segmentation
by: Fu, Ourui, et al.
Published: (2025)

Slot Attention-based Feature Filtering for Few-Shot Learning
by: Rodenas, Javier, et al.
Published: (2025)

tSF: Transformer-based Semantic Filter for Few-Shot Learning
by: Lai, Jinxiang, et al.
Published: (2022)

Adaptive Additive Parameter Updates of Vision Transformers for Few-Shot Continual Learning
by: Stein, Kyle, et al.
Published: (2025)

Simple Semantic-Aided Few-Shot Learning
by: Zhang, Hai, et al.
Published: (2023)

SpatialFormer: Semantic and Target Aware Attentions for Few-Shot Learning
by: Lai, Jinxiang, et al.
Published: (2023)

You Only Need Less Attention at Each Stage in Vision Transformers
by: Zhang, Shuoxi, et al.
Published: (2024)

Mutually-Aware Feature Learning for Few-Shot Object Counting
by: Jeon, Yerim, et al.
Published: (2024)

FSDAM: Few-Shot Driving Attention Modeling via Vision-Language Coupling
by: Hamid, Kaiser, et al.
Published: (2025)

Calibrated Cache Model for Few-Shot Vision-Language Model Adaptation
by: Ding, Kun, et al.
Published: (2024)

Pre-trained Vision and Language Transformers Are Few-Shot Incremental Learners
by: Park, Keon-Hee, et al.
Published: (2024)

Color as the Impetus: Transforming Few-Shot Learner
by: Qi, Chaofei, et al.
Published: (2025)

Few-Shot Object Detection with Sparse Context Transformers
by: Mei, Jie, et al.
Published: (2024)

SMILe: Leveraging Submodular Mutual Information For Robust Few-Shot Object Detection
by: Majee, Anay, et al.
Published: (2024)

Efficient Masked Attention Transformer for Few-Shot Classification and Segmentation
by: Carrión-Ojeda, Dustin, et al.
Published: (2025)

Calibrating Higher-Order Statistics for Few-Shot Class-Incremental Learning with Pre-trained Vision Transformers
by: Goswami, Dipam, et al.
Published: (2024)

Leveraging Bottom-Up and Top-Down Attention for Few-Shot Object Detection
by: Chen, Xianyu, et al.
Published: (2020)

Attention-Enhanced Prototypical Learning for Few-Shot Infrastructure Defect Segmentation
by: Thrainer, Christina, et al.
Published: (2025)

Sculpting Margin Penalty: Intra-Task Adapter Merging and Classifier Calibration for Few-Shot Class-Incremental Learning
by: Bai, Liang, et al.
Published: (2025)

Text and Image Are Mutually Beneficial: Enhancing Training-Free Few-Shot Classification with CLIP
by: Li, Yayuan, et al.
Published: (2024)

A Comparative Study of Vision Transformers and CNNs for Few-Shot Rigid Transformation and Fundamental Matrix Estimation
by: Kaya, Alon, et al.
Published: (2025)

Exploring Cross-Modal Flows for Few-Shot Learning
by: Jiang, Ziqi, et al.
Published: (2025)

Boosting Few-Shot Learning via Attentive Feature Regularization
by: Zhu, Xingyu, et al.
Published: (2024)

Enhancing Vision-Language Few-Shot Adaptation with Negative Learning
by: Zhang, Ce, et al.
Published: (2024)

Stochastic-based Patch Filtering for Few-Shot Learning
by: Rodenas, Javier, et al.
Published: (2025)

Bayesian Evidential Learning for Few-Shot Classification
by: Linghu, Xiongkun, et al.
Published: (2022)

Causal Disentanglement and Cross-Modal Alignment for Enhanced Few-Shot Learning
by: Jiang, Tianjiao, et al.
Published: (2025)

Vision Transformer Off-the-Shelf: A Surprising Baseline for Few-Shot Class-Agnostic Counting
by: Wang, Zhicheng, et al.
Published: (2023)

TinyViT-Batten: Few-Shot Vision Transformer with Explainable Attention for Early Batten-Disease Detection on Pediatric MRI
by: Uppalapati, Khartik, et al.
Published: (2025)

Vision-Language In-Context Learning Driven Few-Shot Visual Inspection Model
by: Ueno, Shiryu, et al.
Published: (2025)

Video-to-Task Learning via Motion-Guided Attention for Few-Shot Action Recognition
by: Guo, Hanyu, et al.
Published: (2024)

Addressing Exacerbated Attention Sink for Source-Free Cross-Domain Few-Shot Learning
by: Yi, Shuai, et al.
Published: (2026)

CASP: Few-Shot Class-Incremental Learning with CLS Token Attention Steering Prompts
by: Huang, Shuai, et al.
Published: (2026)

Few-Shot Medical Image Segmentation with Large Kernel Attention
by: Wu, Xiaoxiao, et al.
Published: (2024)

VTFusion: A Vision-Text Multimodal Fusion Network for Few-Shot Anomaly Detection
by: Jiang, Yuxin, et al.
Published: (2026)

MPM: Mutual Pair Merging for Efficient Vision Transformers
by: Ravé, Simon, et al.
Published: (2026)

Representative Attention For Vision Transformers
by: Li, Yuntong, et al.
Published: (2026)