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Found 6 papers, 2 papers with code
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 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.
