Search Results for author: Matt Smith

Found 2 papers, 0 papers with code

AnyMAL: An Efficient and Scalable Any-Modality Augmented Language Model

no code implementations27 Sep 2023 Seungwhan Moon, Andrea Madotto, Zhaojiang Lin, Tushar Nagarajan, Matt Smith, Shashank Jain, Chun-Fu Yeh, Prakash Murugesan, Peyman Heidari, Yue Liu, Kavya Srinet, Babak Damavandi, Anuj Kumar

We present Any-Modality Augmented Language Model (AnyMAL), a unified model that reasons over diverse input modality signals (i. e. text, image, video, audio, IMU motion sensor), and generates textual responses.

Language Modelling Video Question Answering

Feature anomaly detection system (FADS) for intelligent manufacturing

no code implementations21 Apr 2022 Anthony Garland, Kevin Potter, Matt Smith

Anomaly detection is important for industrial automation and part quality assurance, and while humans can easily detect anomalies in components given a few examples, designing a generic automated system that can perform at human or above human capabilities remains a challenge.

Anomaly Detection

Cannot find the paper you are looking for? You can Submit a new open access paper.