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Found 10 papers, 5 papers with code
Collaborative Generative AI: Integrating GPT-k for Efficient Editing in Text-to-Image Generation
We conduct a series of experiments to compare the common edits made by humans and GPT-k, evaluate the performance of GPT-k in prompting T2I, and examine factors that may influence this process.
Visualize Before You Write: Imagination-Guided Open-Ended Text Generation
Recent advances in text-to-image synthesis make it possible to visualize machine imaginations for a given context.
Neuro-Symbolic Procedural Planning with Commonsense Prompting
Procedural planning aims to implement complex high-level goals by decomposition into sequential simpler low-level steps.
Imagination-Augmented Natural Language Understanding
Human brains integrate linguistic and perceptual information simultaneously to understand natural language, and hold the critical ability to render imaginations.
ImaginE: An Imagination-Based Automatic Evaluation Metric for Natural Language Generation
Automatic evaluations for natural language generation (NLG) conventionally rely on token-level or embedding-level comparisons with text references.
Diagnosing Vision-and-Language Navigation: What Really Matters
Results show that indoor navigation agents refer to both object and direction tokens when making decisions.
SSCR: Iterative Language-Based Image Editing via Self-Supervised Counterfactual Reasoning
In this paper, we introduce a Self-Supervised Counterfactual Reasoning (SSCR) framework that incorporates counterfactual thinking to overcome data scarcity.
Counterfactual Vision-and-Language Navigation via Adversarial Path Sampling
In particular, we present a model-agnostic adversarial path sampler (APS) that learns to sample challenging paths that force the navigator to improve based on the navigation performance.
Towards Metamerism via Foveated Style Transfer
The problem of $\textit{visual metamerism}$ is defined as finding a family of perceptually indistinguishable, yet physically different images.
Eye Tracking Assisted Extraction of Attentionally Important Objects From Videos
The proposed algorithm extracts dominant visual tracks using eye tracking data from multiple subjects on a video sequence by a combination of mean-shift clustering and Hungarian algorithm.
