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Found 10 papers, 4 papers with code
UltraEdit: Instruction-based Fine-Grained Image Editing at Scale
This paper presents UltraEdit, a large-scale (approximately 4 million editing samples), automatically generated dataset for instruction-based image editing.
Flow Priors for Linear Inverse Problems via Iterative Corrupted Trajectory Matching
Generative models based on flow matching have attracted significant attention for their simplicity and superior performance in high-resolution image synthesis.
Latent Energy-Based Odyssey: Black-Box Optimization via Expanded Exploration in the Energy-Based Latent Space
To this end, we formulate an learnable energy-based latent space, and propose Noise-intensified Telescoping density-Ratio Estimation (NTRE) scheme for variational learning of an accurate latent space model without costly Markov Chain Monte Carlo.
Object-Conditioned Energy-Based Attention Map Alignment in Text-to-Image Diffusion Models
Text-to-image diffusion models have shown great success in generating high-quality text-guided images.
Learning Energy-Based Prior Model with Diffusion-Amortized MCMC
To remedy this sampling issue, in this paper we introduce a simple but effective diffusion-based amortization method for long-run MCMC sampling and develop a novel learning algorithm for the latent space EBM based on it.
Learning Concept-Based Causal Transition and Symbolic Reasoning for Visual Planning
In this paper, we propose an interpretable and generalizable visual planning framework consisting of i) a novel Substitution-based Concept Learner (SCL) that abstracts visual inputs into disentangled concept representations, ii) symbol abstraction and reasoning that performs task planning via the self-learned symbols, and iii) a Visual Causal Transition model (ViCT) that grounds visual causal transitions to semantically similar real-world actions.
Latent Diffusion Energy-Based Model for Interpretable Text Modeling
Latent space Energy-Based Models (EBMs), also known as energy-based priors, have drawn growing interests in generative modeling.
Unsupervised Foreground Extraction via Deep Region Competition
Foreground extraction can be viewed as a special case of generic image segmentation that focuses on identifying and disentangling objects from the background.
HALMA: Humanlike Abstraction Learning Meets Affordance in Rapid Problem Solving
Leveraging these concepts, they could understand the internal structure of this task, without seeing all of the problem instances.
P$^2$GNet: Pose-Guided Point Cloud Generating Networks for 6-DoF Object Pose Estimation
Humans are able to perform fast and accurate object pose estimation even under severe occlusion by exploiting learned object model priors from everyday life.
