Nov. 15, 2022, 2:15 a.m. | Kazuki Omi, Jun Kimata, Toru Tamaki

cs.CV updates on arXiv.org arxiv.org

In this paper, we propose a multi-domain learning model for action
recognition. The proposed method inserts domain-specific adapters between
layers of domain-independent layers of a backbone network. Unlike a multi-head
network that switches classification heads only, our model switches not only
the heads, but also the adapters for facilitating to learn feature
representations universal to multiple domains. Unlike prior works, the proposed
method is model-agnostic and doesn't assume model structures unlike prior
works. Experimental results on three popular action recognition …

arxiv model-agnostic

Senior Machine Learning Engineer

@ GPTZero | Toronto, Canada

ML/AI Engineer / NLP Expert - Custom LLM Development (x/f/m)

@ HelloBetter | Remote

Doctoral Researcher (m/f/div) in Automated Processing of Bioimages

@ Leibniz Institute for Natural Product Research and Infection Biology (Leibniz-HKI) | Jena

Seeking Developers and Engineers for AI T-Shirt Generator Project

@ Chevon Hicks | Remote

Senior Applied Data Scientist

@ dunnhumby | London

Principal Data Architect - Azure & Big Data

@ MGM Resorts International | Home Office - US, NV