All links are available in the blog post.
In this episode of the Talking Papers Podcast, I hosted Jiahao Zhang to chat about our CVPR 2023 paper "Aligning Step-by-Step Instructional Diagrams to Video Demonstrations".
furniture assembly diagram. To do that, we collected and annotated a brand new dataset: "IKEA Assembly in the Wild" where we aligned YouTube videos with IKEA's instruction manuals. Our approach to addressing this task proposes several supervised contrastive losses that contrast between video and diagram, video and manual, and internal manual images.
Jiahao is currently a PhD student at the Australian National University. His research focus is on human action recognition and multi-modal representation alignment. We first met (virtually) when Jiahao did his Honours project, where he developed an amazing (and super useful) video annotation tool ViDaT. His strong software engineering and web development background gives him a strong advantage when working on his research projects. Even though we never met in person (yet), we are actively collaborating and I already know what he is cooking up next. I hope to share it with the world soon.
AUTHORS
Jiahao Zhang, Anoop Cherian, Yanbin Liu, Yizhak Ben-Shabat, Cristian Rodriguez, Stephen Gould
RELATED PAPERS
📚IKEA ASM Dataset
📚CLIP
📚SlowFast
LINKS AND RESOURCES
📚 Paper
💻Project page
💻Dataset page
💻Code
SPONSOR
This episode was sponsored by YOOM. YOOM is an Israeli startup dedicated to volumetric video creation. They were voted as the 2022 best start-up to work for by Dun’s 100.
Join their team that works on geometric deep learning research, implicit representations of 3D humans, NeRFs, and 3D/4D generative models.
Visit YOOM
For job opportunities with YOOM visit https://www.yoom.com/careers/
CONTACT
If you would like to be a guest, sponsor or just share your thoughts, feel free to reach out via email: talking.papers.podcast@gmail.com
This episode was recorded on May 1st, 2023.
#talkingpapers #CVPR2023 #IAWDataset #ComputerVision #AI #ActionRecognition #DeepLearning #MachineLearning #research #artificialintelligence #podcasts
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