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Found 7 papers, 2 papers with code
CAUS: A Dataset for Question Generation based on Human Cognition Leveraging Large Language Models
We introduce the CAUS (Curious About Uncertain Scene) dataset, designed to enable Large Language Models, specifically GPT-4, to emulate human cognitive processes for resolving uncertainties.
Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models
We present a novel method, the Chain of Empathy (CoE) prompting, that utilizes insights from psychotherapy to induce Large Language Models (LLMs) to reason about human emotional states.
BallGAN: 3D-aware Image Synthesis with a Spherical Background
To solve this problem, we propose to approximate the background as a spherical surface and represent a scene as a union of the foreground placed in the sphere and the thin spherical background.
Source-free Subject Adaptation for EEG-based Visual Recognition
In this paper, we introduce a novel and practical problem setup, namely source-free subject adaptation, where the source subject data are unavailable and only the pre-trained model parameters are provided for subject adaptation.
Inter-subject Contrastive Learning for Subject Adaptive EEG-based Visual Recognition
This paper tackles the problem of subject adaptive EEG-based visual recognition.
Toward a Human-Level Video Understanding Intelligence
In this paper, we propose the Video Turing Test to provide effective and practical assessments of video understanding intelligence as well as human-likeness evaluation of AI agents.
CogME: A Cognition-Inspired Multi-Dimensional Evaluation Metric for Story Understanding
We introduce CogME, a cognition-inspired, multi-dimensional evaluation metric designed for AI models focusing on story understanding.
