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Found 12 papers, 2 papers with code
Spectral Motion Alignment for Video Motion Transfer using Diffusion Models
The evolution of diffusion models has greatly impacted video generation and understanding.
DreamMotion: Space-Time Self-Similarity Score Distillation for Zero-Shot Video Editing
Text-driven diffusion-based video editing presents a unique challenge not encountered in image editing literature: establishing real-world motion.
DreamSampler: Unifying Diffusion Sampling and Score Distillation for Image Manipulation
Reverse sampling and score-distillation have emerged as main workhorses in recent years for image manipulation using latent diffusion models (LDMs).
VMC: Video Motion Customization using Temporal Attention Adaption for Text-to-Video Diffusion Models
Text-to-video diffusion models have advanced video generation significantly.
Contrastive Denoising Score for Text-guided Latent Diffusion Image Editing
A promising recent approach in this realm is Delta Denoising Score (DDS) - an image editing technique based on Score Distillation Sampling (SDS) framework that leverages the rich generative prior of text-to-image diffusion models.
Regularization by Texts for Latent Diffusion Inverse Solvers
The recent advent of diffusion models has led to significant progress in solving inverse problems, leveraging these models as effective generative priors.
Energy-Based Cross Attention for Bayesian Context Update in Text-to-Image Diffusion Models
Despite the remarkable performance of text-to-image diffusion models in image generation tasks, recent studies have raised the issue that generated images sometimes cannot capture the intended semantic contents of the text prompts, which phenomenon is often called semantic misalignment.
Self-supervised debiasing using low rank regularization
Specifically, we first pretrain a biased encoder in a self-supervised manner with the rank regularization, serving as a semantic bottleneck to enforce the encoder to learn the spuriously correlated attributes.
Training Debiased Subnetworks with Contrastive Weight Pruning
Neural networks are often biased to spuriously correlated features that provide misleading statistical evidence that does not generalize.
Ranked #2 on
Facial Attribute Classification
on bFFHQ
Information-theoretic regularization for Multi-source Domain Adaptation
Here we adopt an information-theoretic approach to identify and resolve the potential adverse effect of the multiple domain discriminators on MDA: disintegration of domain-discriminative information, limited computational scalability, and a large variance in the gradient of the loss during training.
Reliably fast adversarial training via latent adversarial perturbation
To overcome such limitations, we deviate from the existing input-space-based adversarial training regime and propose a single-step latent adversarial training method (SLAT), which leverages the gradients of latent representation as the latent adversarial perturbation.
A Simple Unified Information Regularization Framework for Multi-Source Domain Adaptation
Our framework shows that the information shared across domains cannot be gleaned with multiple discriminators.
