🎙️ Welcome to the Talking Papers Podcast: Where Research Meets Conversation 🌟
Are you ready to explore the fascinating world of cutting-edge research in computer vision, machine learning, artificial intelligence, graphics, and beyond? Join us on this podcast by researchers, for researchers, as we venture into the heart of groundbreaking academic papers.
At Talking Papers, we've reimagined the way research is shared. In each episode, we engage in insightful discussions with the main authors of academic papers, offering you a unique opportunity to dive deep into the minds behind the innovation.
📚 Structure That Resembles a Paper 📝 Just like a well-structured research paper, each episode takes you on a journey through the academic landscape. We provide a concise TL;DR (abstract) to set the stage, followed by a thorough exploration of related work, approach, results, conclusions, and a peek into future work.
🔍 Peer Review Unveiled: "What Did Reviewer 2 Say?" 📢 But that's not all! We bring you an exclusive bonus section where authors candidly share their experiences in the peer review process. Discover the insights, challenges, and triumphs behind the scenes of academic publishing.
🚀 Join the Conversation 💬 Whether you're a seasoned researcher or an enthusiast eager to explore the frontiers of knowledge, Talking Papers Podcast is your gateway to in-depth, engaging discussions with the experts shaping the future of technology and science.
🎧 Tune In and Stay Informed 🌐 Don't miss out on the latest in research and innovation. Subscribe and stay tuned for our enlightening episodes. Welcome to the future of research dissemination – welcome to Talking Papers Podcast! Enjoy the journey! 🌠 #TalkingPapersPodcast #ResearchDissemination #AcademicInsights
PAPER TITLE: " VLN BERT: A Recurrent Vision-and-Language BERT for Navigation " AUTHORS: Yicong Hong, Qi Wu, Yuankai Qi, Cristian Rodriguez-Opazo, Stephen Gould ABSTRACT: Accuracy of many visiolinguistic tasks has benefited significantly from the application of vision-and-language (V&L) BERT. However, its application for the task of vision and-language navigation (VLN) remains limited. One reason for this is the difficulty adapting the BERT architecture to the partially observable M...
Feb 17, 2022•23 min•Ep 6•Transcript available on Metacast
PAPER TITLE Neural Parts: Learning Expressive 3D Shape Abstractions with Invertible Neural Networks AUTHORS Despoina Paschalidou , Angelos Katharopoulos , Andreas Geiger , Sanja Fidler ABSTRACT Impressive progress in 3D shape extraction led to representations that can capture object geometries with high fidelity. In parallel, primitive-based methods seek to represent objects as semantically consistent part arrangements. However, due to the simplicity of existing primitive representations, these ...
Feb 10, 2022•41 min•Ep 5•Transcript available on Metacast
PAPER TITLE: Dynamic Neural Radiance Fields for Monocular 4D Facial Avatar Reconstruction AUTHORS: Guy Gafni Justus Thies Michael Zollhöfer Matthias Nießner Project page: https://gafniguy.github.io/4D-Facial-Avatars/ CODE: 💻 https://github.com/gafniguy/4D-Facial-Avatars ABSTRACT: We present dynamic neural radiance fields for modeling the appearance and dynamics of a human face. Digitally modeling and reconstructing a talking human is a key building-block for a variety of applications. Especiall...
Feb 03, 2022•33 min•Ep 4•Transcript available on Metacast
PAPER TITLE: " UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders " AUTHORS: Jing Zhang, Deng-Ping Fan, Yuchao Dai, Saeed Anwar, Fatemeh Sadat Saleh, Tong Zhang, Nick Barnes ABSTRACT: In this paper, we propose the first framework (UCNet) to employ uncertainty for RGB-D saliency detection by learning from the data labeling process. Existing RGB-D saliency detection methods treat the saliency detection task as a point estimation problem, and pr...
Jan 20, 2022•30 min•Ep 3•Transcript available on Metacast
PAPER TITLE: " Deep Declarative Networks: a new hope " AUTHORS: Stephen Gould, Richard Hartley, Dylan Campbell ABSTRACT: We explore a new class of end-to-end learnable models wherein data processing nodes (or network layers) are defined in terms of desired behaviour rather than an explicit forward function. Specifically, the forward function is implicitly defined as the solution to a mathematical optimization problem. Consistent with nomenclature in the programming languages community,...
Jan 13, 2022•30 min•Ep 2•Transcript available on Metacast
Paper title: " DORi: Discovering Object Relationships for Moment Localization of a Natural Language Query in a Video " Authors: Cristian Rodriguez-Opazo, Edison Marrese-Taylor, Basura Fernando, Hongdong Li, Stephen Gould Abstract: This paper studies the task of temporal moment localization in a long untrimmed video using natural language query. Given a query sentence, the goal is to determine the start and end of the relevant segment within the video. Our key innovation is to learn a v...
Jan 05, 2022•27 min•Ep 1•Transcript available on Metacast
