8 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…
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…
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…