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ACM Multimedia 2024Technical Demonstrations

MIRACLE: An Online, Explainable Multimodal Interactive Concept Learning System

Ansel Blume, Khanh Duy Nguyen, Zhenhailong Wang, Yangyi Chen, Michal Shlapentokh-Rothman, Xiaomeng Jin, Jeonghwan Kim, Zhen Zhu 0006, Jiateng Liu, Kuan-Hao Huang, Mankeerat Sidhu, Xuanming Zhang, Vivian Liu, Raunak Sinha, Te-Lin Wu, Abhay Zala, Elias Stengel-Eskin, Da Yin, Yao Xiao, Utkarsh Mall, Zhou Yu 0005, Kai-Wei Chang 0001, Camille Cobb, Karrie Karahalios, Lydia B. Chilton, Mohit Bansal, Nanyun Peng 0001, Carl Vondrick, Derek Hoiem, Heng Ji 0001

PDF 由论文原始站点提供,PaperCompass 不保存论文文件。DOI 10.1145/3664647.3684993 ↗

摘要

We present MIRACLE, a system for online, interpretable visual concept and video action recognition. Through a chat interface, users query the recognition system with an uploaded image or video. For images, MIRACLE returns concept predictions from its structured knowledge base, justifying its predictions with heatmaps and natural language-based attribute detections. For videos, MIRACLE predicts an action and justifies its prediction with time varying entity-entity relations. With its ability to learn new concepts in an online, few-shot manner and its support of dynamic changes to its knowledge base, MIRACLE represents a step forward in interpretable multimodal learning systems.