Enabling Robots to Draw and Tell: Towards Visually Grounded Multimodal Description Generation

TL;DR

This paper explores enabling robots to generate natural language descriptions along with sketches and gestures to describe visual scenes, integrating multimodal communication for more human-like interaction.

Contribution

It introduces the task of visually-grounded multimodal description generation, combining language, sketches, and gestures for robotic communication.

Findings

Discusses challenges and evaluation metrics for multimodal description generation.

Highlights benefits from recent advances in NLP and computer vision.

Proposes a framework for integrating multimodal outputs in robotic systems.

Abstract

Socially competent robots should be equipped with the ability to perceive the world that surrounds them and communicate about it in a human-like manner. Representative skills that exhibit such ability include generating image descriptions and visually grounded referring expressions. In the NLG community, these generation tasks are largely investigated in non-interactive and language-only settings. However, in face-to-face interaction, humans often deploy multiple modalities to communicate, forming seamless integration of natural language, hand gestures and other modalities like sketches. To enable robots to describe what they perceive with speech and sketches/gestures, we propose to model the task of generating natural language together with free-hand sketches/hand gestures to describe visual scenes and real life objects, namely, visually-grounded multimodal description generation. In this paper, we discuss the challenges and evaluation metrics of the task, and how the task can benefit from progress recently made in the natural language processing and computer vision realms, where related topics such as visually grounded NLG, distributional semantics, and photo-based sketch generation have been extensively studied.