1 citations · 1 across the 9 of their papers we have counts for
10 papers
An Iteration-Free Fixed-Point Estimator for Diffusion Inversion
Yifei Chen, Kaiyu Song, Yan Pan +3
Diffusion inversion aims to recover the initial noise corresponding to a given image such that this noise can reconstruct the original image through the denoising diffusion process…
Topology Sculptor, Shape Refiner: Discrete Diffusion Model for High-Fidelity 3D Meshes Generation
Kaiyu Song, Hanjiang Lai, Yaqing Zhang +3
In this paper, we introduce Topology Sculptor, Shape Refiner (TSSR), a novel method for generating high-quality, artist-style 3D meshes based on Discrete Diffusion Models (DDMs). O…
Rethinking Oversaturation in Classifier-Free Guidance via Low Frequency
Kaiyu Song, Hanjiang Lai
Classifier-free guidance (CFG) succeeds in condition diffusion models that use a guidance scale to balance the influence of conditional and unconditional terms. A high guidance sca…
Enhancing Few-Shot Out-of-Distribution Detection with Gradient Aligned Context Optimization
Baoshun Tong, Kaiyu Song, Hanjiang Lai
Few-shot out-of-distribution (OOD) detection aims to detect OOD images from unseen classes with only a few labeled in-distribution (ID) images. To detect OOD images and classify ID…
Test-time Alignment-Enhanced Adapter for Vision-Language Models
Baoshun Tong, Kaiyu Song, Hanjiang Lai
Test-time adaptation with pre-trained vision-language models (VLMs) has attracted increasing attention for tackling the issue of distribution shift during the test phase. While pri…
Leveraging Previous Steps: A Training-free Fast Solver for Flow Diffusion
Kaiyu Song, Hanjiang Lai
Flow diffusion models (FDMs) have recently shown potential in generation tasks due to the high generation quality. However, the current ordinary differential equation (ODE) solver…
Flow Matching Posterior Sampling: A Training-free Conditional Generation for Flow Matching
Kaiyu Song, Hanjiang Lai, Yan Pan +2
Training-free conditional generation based on flow matching aims to leverage pre-trained unconditional flow matching models to perform conditional generation without retraining. Re…
Improving Training-free Conditional Diffusion Model via Fisher Information
Kaiyu Song, Hanjiang Lai
Training-free conditional diffusion models have received great attention in conditional image generation tasks. However, they require a computationally expensive conditional score…
MimicDiffusion: Purifying Adversarial Perturbation via Mimicking Clean Diffusion Model
Kaiyu Song, Hanjiang Lai
Deep neural networks (DNNs) are vulnerable to adversarial perturbation, where an imperceptible perturbation is added to the image that can fool the DNNs. Diffusion-based adversaria…
Two Simple Principles for Diffusion-Based Test-Time Adaptation
Kaiyu Song, Hanjiang Lai, Yan Pan +2
Recently, diffusion-based test-time adaptations (TTA) have shown great advances, which leverage a diffusion model to map the images in the unknown test domain to the training domai…