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Nội dung được cung cấp bởi Itzik Ben-Shabat. Tất cả nội dung podcast bao gồm các tập, đồ họa và mô tả podcast đều được Itzik Ben-Shabat hoặc đối tác nền tảng podcast của họ tải lên và cung cấp trực tiếp. Nếu bạn cho rằng ai đó đang sử dụng tác phẩm có bản quyền của bạn mà không có sự cho phép của bạn, bạn có thể làm theo quy trình được nêu ở đây https://vi.player.fm/legal.
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3DInAction - Yizhak Ben-Shabat

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Manage episode 421837998 series 3300270
Nội dung được cung cấp bởi Itzik Ben-Shabat. Tất cả nội dung podcast bao gồm các tập, đồ họa và mô tả podcast đều được Itzik Ben-Shabat hoặc đối tác nền tảng podcast của họ tải lên và cung cấp trực tiếp. Nếu bạn cho rằng ai đó đang sử dụng tác phẩm có bản quyền của bạn mà không có sự cho phép của bạn, bạn có thể làm theo quy trình được nêu ở đây https://vi.player.fm/legal.

🎙️ **Unveiling 3DInAction with Yizhak Ben-Shabat | Talking Papers Podcast** 🎙️
📚 *Title:* 3DInAction: Understanding Human Actions in 3D Point Clouds
📅 *Published In:* CVPR 2024
👤 *Guest:* Yizhak (Itzik) Ben-Shabat
Welcome back to another exciting episode of the Talking Papers Podcast, where we bring you the latest breakthroughs in academic research directly from early career academics and PhD students! This week, we have the pleasure of hosting Itzik Ben-Shabat to discuss his groundbreaking paper *3DInAction: Understanding Human Actions in 3D Point Clouds*, published in CVPR 2024 as a highlight.
In this episode, we delve into a novel method for 3D point cloud action recognition. Itzik explains how this innovative pipeline addresses the major limitations of point cloud data, such as lack of structure, permutation invariance, and varying number of points. With patches moving in time (t-patches) and a hierarchical architecture, 3DInAction significantly enhances spatio-temporal representation learning, achieving superior performance on datasets like DFAUST and IKEA ASM.
**Main Contributions:**
1. Introduction of the 3DInAction pipeline for 3D point cloud action recognition.
2. Detailed explanation of t-patches as a key building block.
3. Presentation of a hierarchical architecture for improved spatio-temporal representations.
4. Demonstration of enhanced performance on existing benchmarks.
**Host Insights:** Given my involvement in the project, I can share that when I embarked on this journey, there were only a handful of studies tackling the intricate task of 3D action recognition from point cloud data. Today, this has burgeoned into an active and evolving field of research, showing just how pivotal and timely this work is.
**Anecdotes and Behind the Scenes:** The title "3DInAction" signifies the culmination of three years of passionate research coinciding with my fellowship's theme. This episode is unique as it's hosted by an AI avatar created by Synthesia—Itzik was looking for an exciting way to share this story using the latest technology. While there is no sponsorship, the use of AI avatars adds an innovative twist to our discussion.
Don't miss this intellectually stimulating conversation with Itzik Ben-Shabat. Be sure to leave your thoughts and questions in the comments section below—we’d love to hear from you! And if you haven't already, hit that subscribe button to stay updated with our latest episodes.
🔗 **Links and References:**
- Watch the full episode: [Podcast Link]
- Read the full paper: [Paper Link]
📢 **Engage with Us:**
- What are your thoughts on 3D point cloud action recognition? Drop a comment below!
- Don’t forget to like, subscribe, and hit the notification bell for more insightful episodes!
Join us in pushing the boundaries of what's possible in research and technology!
---
Ready to be part of this journey? Click play and let’s dive deep into the world of 3D action recognition! 🚀
All links and resources are available in the blogpost: https://www.itzikbs.com/3dinaction
Note that the host of this episode is not a real person. It is an AI generated avatar and everything she said in the episode was fully scripted.

🎧Subscribe on your favourite podcast app: https://talking.papers.podcast.itzikbs.com

📧Subscribe to our mailing list: http://eepurl.com/hRznqb

🐦Follow us on Twitter: https://twitter.com/talking_papers

🎥YouTube Channel: https://bit.ly/3eQOgwP

  continue reading

35 tập

Artwork

3DInAction - Yizhak Ben-Shabat

Talking Papers Podcast

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published

iconChia sẻ
 
Manage episode 421837998 series 3300270
Nội dung được cung cấp bởi Itzik Ben-Shabat. Tất cả nội dung podcast bao gồm các tập, đồ họa và mô tả podcast đều được Itzik Ben-Shabat hoặc đối tác nền tảng podcast của họ tải lên và cung cấp trực tiếp. Nếu bạn cho rằng ai đó đang sử dụng tác phẩm có bản quyền của bạn mà không có sự cho phép của bạn, bạn có thể làm theo quy trình được nêu ở đây https://vi.player.fm/legal.

🎙️ **Unveiling 3DInAction with Yizhak Ben-Shabat | Talking Papers Podcast** 🎙️
📚 *Title:* 3DInAction: Understanding Human Actions in 3D Point Clouds
📅 *Published In:* CVPR 2024
👤 *Guest:* Yizhak (Itzik) Ben-Shabat
Welcome back to another exciting episode of the Talking Papers Podcast, where we bring you the latest breakthroughs in academic research directly from early career academics and PhD students! This week, we have the pleasure of hosting Itzik Ben-Shabat to discuss his groundbreaking paper *3DInAction: Understanding Human Actions in 3D Point Clouds*, published in CVPR 2024 as a highlight.
In this episode, we delve into a novel method for 3D point cloud action recognition. Itzik explains how this innovative pipeline addresses the major limitations of point cloud data, such as lack of structure, permutation invariance, and varying number of points. With patches moving in time (t-patches) and a hierarchical architecture, 3DInAction significantly enhances spatio-temporal representation learning, achieving superior performance on datasets like DFAUST and IKEA ASM.
**Main Contributions:**
1. Introduction of the 3DInAction pipeline for 3D point cloud action recognition.
2. Detailed explanation of t-patches as a key building block.
3. Presentation of a hierarchical architecture for improved spatio-temporal representations.
4. Demonstration of enhanced performance on existing benchmarks.
**Host Insights:** Given my involvement in the project, I can share that when I embarked on this journey, there were only a handful of studies tackling the intricate task of 3D action recognition from point cloud data. Today, this has burgeoned into an active and evolving field of research, showing just how pivotal and timely this work is.
**Anecdotes and Behind the Scenes:** The title "3DInAction" signifies the culmination of three years of passionate research coinciding with my fellowship's theme. This episode is unique as it's hosted by an AI avatar created by Synthesia—Itzik was looking for an exciting way to share this story using the latest technology. While there is no sponsorship, the use of AI avatars adds an innovative twist to our discussion.
Don't miss this intellectually stimulating conversation with Itzik Ben-Shabat. Be sure to leave your thoughts and questions in the comments section below—we’d love to hear from you! And if you haven't already, hit that subscribe button to stay updated with our latest episodes.
🔗 **Links and References:**
- Watch the full episode: [Podcast Link]
- Read the full paper: [Paper Link]
📢 **Engage with Us:**
- What are your thoughts on 3D point cloud action recognition? Drop a comment below!
- Don’t forget to like, subscribe, and hit the notification bell for more insightful episodes!
Join us in pushing the boundaries of what's possible in research and technology!
---
Ready to be part of this journey? Click play and let’s dive deep into the world of 3D action recognition! 🚀
All links and resources are available in the blogpost: https://www.itzikbs.com/3dinaction
Note that the host of this episode is not a real person. It is an AI generated avatar and everything she said in the episode was fully scripted.

🎧Subscribe on your favourite podcast app: https://talking.papers.podcast.itzikbs.com

📧Subscribe to our mailing list: http://eepurl.com/hRznqb

🐦Follow us on Twitter: https://twitter.com/talking_papers

🎥YouTube Channel: https://bit.ly/3eQOgwP

  continue reading

35 tập

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