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20192025
most citedDiversity Helps: Unsupervised Few-shot Learning via Distribution Shift-based Data Augmentation

18 citations · 46 across the 12 of their papers we have counts for

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Showing 2025Show all

5 papers · 1 filter

cs.LG2025

Generalizing to New Dynamical Systems via Frequency Domain Adaptation

Tiexin Qin, Hong Yan, Haoliang Li

Learning the underlying dynamics from data with deep neural networks has shown remarkable potential in modeling various complex physical dynamics. However, current approaches are c…

cs.LG2025

Permutation Equivariant Neural Controlled Differential Equations for Dynamic Graph Representation Learning

Torben Berndt, Benjamin Walker, Tiexin Qin +2

Dynamic graphs exhibit complex temporal dynamics due to the interplay between evolving node features and changing network structures. Recently, Graph Neural Controlled Differential…

q-bio.QM2025

Test-time Adaptation for Foundation Medical Segmentation Model without Parametric Updates

Kecheng Chen, Xinyu Luo, Tiexin Qin +5

Foundation medical segmentation models, with MedSAM being the most popular, have achieved promising performance across organs and lesions. However, MedSAM still suffers from compro…

cs.CV2025

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…

cs.CV2025

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…