5 citations · 10 across the 12 of their papers we have counts for
15 papers
LAST: The Last Query Token Guides Visual Token Pruning for Edge-Cloud Collaborative MLLM Inference
Feng Yang, Xinrui Ju, Keyang Zhang +6
Multimodal foundation models are reshaping edge-cloud visual intelligence from task-specific feature pipelines into token-based interfaces, where edge devices encode visual inputs…
LUMI: Tokenizer-Agnostic LLM-Based Lossless Image Compression
Chris Xing Tian, Chengkai Wu, Ziyu Wang +6
Large language model (LLM)-based lossless image compression methods typically represent pixel data through the native text interface of a pretrained model, converting pixel values…
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…
Disentangling Instruction Influence in Diffusion Transformers for Parallel Multi-Instruction-Guided Image Editing
Hui Liu, Bin Zou, Suiyun Zhang +3
Instruction-guided image editing enables users to specify modifications using natural language, offering more flexibility and control. Among existing frameworks, Diffusion Transfor…
SPACE: SPike-Aware Consistency Enhancement for Test-Time Adaptation in Spiking Neural Networks
Xinyu Luo, Kecheng Chen, Pao-Sheng Vincent Sun +3
Spiking Neural Networks (SNNs), as a biologically plausible alternative to Artificial Neural Networks (ANNs), have demonstrated advantages in terms of energy efficiency, temporal p…
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…
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…
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…
Log Neural Controlled Differential Equations: The Lie Brackets Make a Difference
Benjamin Walker, Andrew D. McLeod, Tiexin Qin +3
The vector field of a controlled differential equation (CDE) describes the relationship between a control path and the evolution of a solution path. Neural CDEs (NCDEs) treat time…
Unsupervised Domain Adaptation for Low-dose CT Reconstruction via Bayesian Uncertainty Alignment
Kecheng Chen, Jie Liu, Renjie Wan +4
Low-dose computed tomography (LDCT) image reconstruction techniques can reduce patient radiation exposure while maintaining acceptable imaging quality. Deep learning is widely used…
Generalizing to Evolving Domains with Latent Structure-Aware Sequential Autoencoder
Tiexin Qin, Shiqi Wang, Haoliang Li
Domain generalization aims to improve the generalization capability of machine learning systems to out-of-distribution (OOD) data. Existing domain generalization techniques embark…