5 citations · 15 across the 10 of their papers we have counts for
6 papers · 1 filter
Enhancing Zero-Shot Image Recognition in Vision-Language Models through Human-like Concept Guidance
Hui Liu, Wenya Wang, Kecheng Chen +6
In zero-shot image recognition tasks, humans demonstrate remarkable flexibility in classifying unseen categories by composing known simpler concepts. However, existing vision-langu…
Q-PART: Quasi-Periodic Adaptive Regression with Test-time Training for Pediatric Left Ventricular Ejection Fraction Regression
Jie Liu, Tiexin Qin, Hui Liu +5
In this work, we address the challenge of adaptive pediatric Left Ventricular Ejection Fraction (LVEF) assessment. While Test-time Training (TTT) approaches show promise for this t…
Test-time adaptation for image compression with distribution regularization
Kecheng Chen, Pingping Zhang, Tiexin Qin +3
Current test- or compression-time adaptation image compression (TTA-IC) approaches, which leverage both latent and decoder refinements as a two-step adaptation scheme, have potenti…
LibFewShot: A Comprehensive Library for Few-shot Learning
Wenbin Li, Ziyi, Wang +9
Few-shot learning, especially few-shot image classification, has received increasing attention and witnessed significant advances in recent years. Some recent studies implicitly sh…
Diversity Helps: Unsupervised Few-shot Learning via Distribution Shift-based Data Augmentation
Tiexin Qin, Wenbin Li, Yinghuan Shi +1
Few-shot learning aims to learn a new concept when only a few training examples are available, which has been extensively explored in recent years. However, most of the current wor…
Automatic Data Augmentation by Learning the Deterministic Policy
Yinghuan Shi, Tiexin Qin, Yong Liu +3
Aiming to produce sufficient and diverse training samples, data augmentation has been demonstrated for its effectiveness in training deep models. Regarding that the criterion of th…