2 citations · 3 across the 10 of their papers we have counts for
11 papers · 1 filter
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…
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…
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…