11 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…
Latent Bias Alignment for High-Fidelity Diffusion Inversion in Real-World Image Reconstruction and Manipulation
Weiming Chen, Qifan Liu, Siyi Liu +4
Recent research has shown that text-to-image diffusion models are capable of generating high-quality images guided by text prompts. But can they be used to generate or approximate…
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…
Training-Free Dual Hyperbolic Adapters for Better Cross-Modal Reasoning
Yi Zhang, Chun-Wun Cheng, Junyi He +5
Recent research in Vision-Language Models (VLMs) has significantly advanced our capabilities in cross-modal reasoning. However, existing methods suffer from performance degradation…
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…
Continuous Q-Score Matching: Diffusion Guided Reinforcement Learning for Continuous-Time Control
Chengxiu Hua, Jiawen Gu, Yushun Tang
Reinforcement learning (RL) has achieved significant success across a wide range of domains, however, most existing methods are formulated in discrete time. In this work, we introd…