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Found 4 papers, 1 papers with code
ZSON: Zero-Shot Object-Goal Navigation using Multimodal Goal Embeddings
We present a scalable approach for learning open-world object-goal navigation (ObjectNav) -- the task of asking a virtual robot (agent) to find any instance of an object in an unexplored environment (e. g., "find a sink").
Neuro-Symbolic Generative Art: A Preliminary Study
There are two classes of generative art approaches: neural, where a deep model is trained to generate samples from a data distribution, and symbolic or algorithmic, where an artist designs the primary parameters and an autonomous system generates samples within these constraints.
On the Benefits of Models with Perceptually-Aligned Gradients
In this paper, we leverage models with interpretable perceptually-aligned features and show that adversarial training with low max-perturbation bound can improve the performance of models for zero-shot and weakly supervised localization tasks.
cFineGAN: Unsupervised multi-conditional fine-grained image generation
We propose an unsupervised multi-conditional image generation pipeline: cFineGAN, that can generate an image conditioned on two input images such that the generated image preserves the texture of one and the shape of the other input.
