1 citations · 1 across the 4 of their papers we have counts for
6 papers
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…
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…
Large Language Models for Lossless Image Compression: Next-Pixel Prediction in Language Space is All You Need
Kecheng Chen, Pingping Zhang, Hui Liu +6
We have recently witnessed that ``Intelligence" and `` Compression" are the two sides of the same coin, where the language large model (LLM) with unprecedented intelligence is a ge…
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…
Deep Signature: Characterization of Large-Scale Molecular Dynamics
Tiexin Qin, Mengxu Zhu, Chunyang Li +3
Understanding protein dynamics are essential for deciphering protein functional mechanisms and developing molecular therapies. However, the complex high-dimensional dynamics and in…
Domain Generalization with Small Data
Kecheng Chen, Elena Gal, Hong Yan +1
In this work, we propose to tackle the problem of domain generalization in the context of \textit{insufficient samples}. Instead of extracting latent feature embeddings based on de…