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Found 12 papers, 6 papers with code
Text-Based Reasoning About Vector Graphics
By casting an image to a text-based representation, we can leverage the power of language models to learn alignment from SVG to visual primitives and generalize to unseen question-answering tasks.
What's Left? Concept Grounding with Logic-Enhanced Foundation Models
We propose the Logic-Enhanced Foundation Model (LEFT), a unified framework that learns to ground and reason with concepts across domains with a differentiable, domain-independent, first-order logic-based program executor.
Motion Question Answering via Modular Motion Programs
In order to build artificial intelligence systems that can perceive and reason with human behavior in the real world, we must first design models that conduct complex spatio-temporal reasoning over motion sequences.
Programmatically Grounded, Compositionally Generalizable Robotic Manipulation
Robots operating in the real world require both rich manipulation skills as well as the ability to semantically reason about when to apply those skills.
NS3D: Neuro-Symbolic Grounding of 3D Objects and Relations
Different functional modules in the programs are implemented as neural networks.
Geoclidean: Few-Shot Generalization in Euclidean Geometry
In contrast, low-level and high-level visual features from standard computer vision models pretrained on natural images do not support correct generalization.
Monte Carlo Tree Search for Interpreting Stress in Natural Language
Using a dataset of Reddit posts that exhibit stress, we demonstrate the ability of our MCTS algorithm to identify interpretable explanations for a person's feeling of stress in both a context-dependent and context-independent manner.
DARCNN: Domain Adaptive Region-based Convolutional Neural Network for Unsupervised Instance Segmentation in Biomedical Images
In the biomedical domain, there is an abundance of dense, complex data where objects of interest may be challenging to detect or constrained by limits of human knowledge.
DARCNN: Domain Adaptive Region-based Convolutional Neural Network forUnsupervised Instance Segmentation in Biomedical Images
We showcase DARCNN’sperformance for unsupervised instance segmentation on nu-merous biomedical datasets.
Capturing implicit hierarchical structure in 3D biomedical images with self-supervised hyperbolic representations
To that end, we consider encoder-decoder architectures with a hyperbolic latent space, to explicitly capture hierarchical relationships present in subvolumes of the data.
Learning Hyperbolic Representations for Unsupervised 3D Segmentation
There exists a need for unsupervised 3D segmentation on complex volumetric data, particularly when annotation ability is limited or discovery of new categories is desired.
Improving Medical Annotation Quality to Decrease Labeling Burden Using Stratified Noisy Cross-Validation
For instance, Noisy Cross-Validation splits the training data into halves, and has been shown to identify low-quality labels in computer vision tasks; but it has not been applied to medical imaging tasks specifically.
