6 citations · 10 across the 15 of their papers we have counts for
15 papers
In-Loop Model Adaptation with Coupled Latent-Noise Guidance for High-Fidelity Subject-Driven Text-to-Image Generation
Yushun Tang, Weiming Chen, Siyi Liu +3
Text-to-image diffusion models have achieved remarkable success in generating high-quality images from a given text prompt. Subject-driven generation aims to synthesize customized…
Progressive Conditioned Scale-Shift Recalibration of Self-Attention for Online Test-time Adaptation
Yushun Tang, Ziqiong Liu, Jiyuan Jia +2
Online test-time adaptation aims to dynamically adjust a network model in real-time based on sequential input samples during the inference stage. In this work, we find that, when a…
Open-World Test-Time Adaptation with Hierarchical Feature Aggregation and Attention Affine
Ziqiong Liu, Yushun Tang, Junyang Ji +1
Test-time adaptation (TTA) refers to adjusting the model during the testing phase to cope with changes in sample distribution and enhance the model's adaptability to new environmen…
LatentEdit: Adaptive Latent Control for Consistent Semantic Editing
Siyi Liu, Weiming Chen, Yushun Tang +1
Diffusion-based Image Editing has achieved significant success in recent years. However, it remains challenging to achieve high-quality image editing while maintaining the backgrou…
Domain-Conditioned Transformer for Fully Test-time Adaptation
Yushun Tang, Shuoshuo Chen, Jiyuan Jia +2
Fully test-time adaptation aims to adapt a network model online based on sequential analysis of input samples during the inference stage. We observe that, when applying a transform…
Window-based Channel Attention for Wavelet-enhanced Learned Image Compression
Heng Xu, Bowen Hai, Yushun Tang +1
Learned Image Compression (LIC) models have achieved superior rate-distortion performance than traditional codecs. Existing LIC models use CNN, Transformer, or Mixed CNN-Transforme…
Dual-Path Adversarial Lifting for Domain Shift Correction in Online Test-time Adaptation
Yushun Tang, Shuoshuo Chen, Zhihe Lu +2
Transformer-based methods have achieved remarkable success in various machine learning tasks. How to design efficient test-time adaptation methods for transformer models becomes an…
Learning Visual Conditioning Tokens to Correct Domain Shift for Fully Test-time Adaptation
Yushun Tang, Shuoshuo Chen, Zhehan Kan +3
Fully test-time adaptation aims to adapt the network model based on sequential analysis of input samples during the inference stage to address the cross-domain performance degradat…
Concept-Guided Prompt Learning for Generalization in Vision-Language Models
Yi Zhang, Ce Zhang, Ke Yu +2
Contrastive Language-Image Pretraining (CLIP) model has exhibited remarkable efficacy in establishing cross-modal connections between texts and images, yielding impressive performa…
Learning to Adapt CLIP for Few-Shot Monocular Depth Estimation
Xueting Hu, Ce Zhang, Yi Zhang +3
Pre-trained Vision-Language Models (VLMs), such as CLIP, have shown enhanced performance across a range of tasks that involve the integration of visual and linguistic modalities. W…
BDC-Adapter: Brownian Distance Covariance for Better Vision-Language Reasoning
Yi Zhang, Ce Zhang, Zihan Liao +2
Large-scale pre-trained Vision-Language Models (VLMs), such as CLIP and ALIGN, have introduced a new paradigm for learning transferable visual representations. Recently, there has…
Critical Sampling for Robust Evolution Operator Learning of Unknown Dynamical Systems
Ce Zhang, Kailiang Wu, Zhihai He
Given an unknown dynamical system, what is the minimum number of samples needed for effective learning of its governing laws and accurate prediction of its future evolution behavio…
Self-Correctable and Adaptable Inference for Generalizable Human Pose Estimation
Zhehan Kan, Shuoshuo Chen, Ce Zhang +2
A central challenge in human pose estimation, as well as in many other machine learning and prediction tasks, is the generalization problem. The learned network does not have the c…
Neuro-Modulated Hebbian Learning for Fully Test-Time Adaptation
Yushun Tang, Ce Zhang, Heng Xu +5
Fully test-time adaptation aims to adapt the network model based on sequential analysis of input samples during the inference stage to address the cross-domain performance degradat…
Self-Constrained Inference Optimization on Structural Groups for Human Pose Estimation
Zhehan Kan, Shuoshuo Chen, Zeng Li +1
We observe that human poses exhibit strong group-wise structural correlation and spatial coupling between keypoints due to the biological constraints of different body parts. This…