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Found 13 papers, 3 papers with code
Exploring the GLIDE model for Human Action Effect Prediction
GLIDE is a generative neural network that can synthesize (inpaint) masked areas of an image, conditioned on a short piece of text.
Advancing Spatial Reasoning in Large Language Models: An In-Depth Evaluation and Enhancement Using the StepGame Benchmark
We analyze GPT's spatial reasoning performance on the rectified benchmark, identifying proficiency in mapping natural language text to spatial relations but limitations in multi-hop reasoning.
Language Models as a Service: Overview of a New Paradigm and its Challenges
This paper has two goals: on the one hand, we delineate how the aforementioned challenges act as impediments to the accessibility, replicability, reliability, and trustworthiness of LMaaS.
Object-agnostic Affordance Categorization via Unsupervised Learning of Graph Embeddings
Acquiring knowledge about object interactions and affordances can facilitate scene understanding and human-robot collaboration tasks.
A Hierarchical Framework for Collaborative Artificial Intelligence
We propose a hierarchical framework for collaborative intelligent systems.
Exploring the GLIDE model for Human Action-effect Prediction
GLIDE is a generative neural network that can synthesize (inpaint) masked areas of an image, conditioned on a short piece of text.
Scribble-Supervised Semantic Segmentation by Uncertainty Reduction on Neural Representation and Self-Supervision on Neural Eigenspace
Scribble-supervised semantic segmentation has gained much attention recently for its promising performance without high-quality annotations.
Defect segmentation: Mapping tunnel lining internal defects with ground penetrating radar data using a convolutional neural network
This research proposes a Ground Penetrating Radar (GPR) data processing method for non-destructive detection of tunnel lining internal defects, called defect segmentation.
Human-like Planning for Reaching in Cluttered Environments
From this, we devised a qualitative representation of the task space to abstract the decision making, irrespective of the number of obstacles.
GPRInvNet: Deep Learning-Based Ground Penetrating Radar Data Inversion for Tunnel Lining
The results have demonstrated that the GPRInvNet is capable of effectively reconstructing complex tunnel lining defects with clear boundaries.
ViTac: Feature Sharing between Vision and Tactile Sensing for Cloth Texture Recognition
In this paper, addressing for the first time (to the best of our knowledge) texture recognition from tactile images and vision, we propose a new fusion method named Deep Maximum Covariance Analysis (DMCA) to learn a joint latent space for sharing features through vision and tactile sensing.
CLAD: A Complex and Long Activities Dataset with Rich Crowdsourced Annotations
This paper introduces a novel activity dataset which exhibits real-life and diverse scenarios of complex, temporally-extended human activities and actions.
Natural Language Grounding and Grammar Induction for Robotic Manipulation Commands
This is achieved by first learning a set of visual {`}concepts{'} that abstract the visual feature spaces into concepts that have human-level meaning.
