Street View Image, Pose & 3D Cities Dataset. 25 million images, 3D models of 8 cities, camera pose & correspondences
Stanford Vision and Learning Lab
372 posts
Stanford Vision and Learning Lab
@StanfordSVL
SVL is led by @drfeifei @silviocinguetta @jcniebles @jiajunwu_cs and works on machine learning, computer vision, robotics and language
- #TransferLearning is crucial for general #AI, and understanding what transfers to what is crucial for #TransferLearning. Taskonomy (#CVPR18 oral) is one step towards understanding transferability among #perception tasks. Live demo and more: taskonomy.stanford.edu
GIF - Introducing the RoboTurk Real Robot Dataset - one of the largest, richest, and most diverse robot manipulation datasets ever collected using human creativity and dexterity! 111 hours 54 non-expert demonstrators 2144 demonstrations Download: roboturk.stanford.edu/realrobotdatas… [1/2]
GIF - Stanford Vision and Learning Lab is presenting 7 papers at #CORL2023, including 3 oral presentations, and 3 award nominations, see below:
- [1/2] Our lab has 3 papers accepted to NeurIPS 2019: 1. HYPE: Human Eye Perceptual Evaluation of Generative Models. Zhou and Gordon et al. (Oral) 2. SOCIAL-BIGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networks. Kosaraju et al.
- Stanford Vision and Learning Lab: Performing Research at the Forefront of Computer Vision, Machine Learning, and Robotics - HostingAdvice.com @drfeifei @silviocinguetta @jcniebles hostingadvice.com/blog/a-look-at…
- Stanford Vision and Learning Group website is online now! @drfeifei @silviocinguetta @jcniebles svl.stanford.edu
- We are hosting one of the 3 challenges of embodied-ai.org at CVPR20. Train your navigating agent in our simulator Gibson (svl.stanford.edu/gibson2) and we will test it in the real world! The best solutions will showcase live during CVPR. More info: svl.stanford.edu/gibson2/challe…
- Learning from hints (not demonstrations): A new paper on an important direction of RL for control where expert intuition can be used to guide learning without the need to provide optimal or even complete solutions.Our new work at #Corl2019 will present RL with Ensemble of Suboptimal Teachers -aka- specify as much as you can easily, let learning handle the rest. Blog: buff.ly/2O99xWr Paper: buff.ly/2NY9KeU w\ @andrey_kurenkov, A. Mandlekar, @RobobertoMM, @silviocinguetta
GIF - Work from our group in Robot Learning for Manipulation is finalist for best paper award at @icra2019 and is being presented tomorrow in Montreal. @drfeifei @silviocinguetta @animesh_garg @yukez @michellearning @leto__jeanExcited to be at #ICRA2019 Best Paper Award talk Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks Paper: arxiv.org/abs/1810.10191 Video: youtu.be/TjwDJ_R2204
- Replying to @StanfordSVL
- We are happy to announce our ICCV19 Workshop on Visual Perception for Navigation in Human Environments: The JackRabbot Social Robotics Dataset and Benchmark. Submission deadline August 20. jrdb.stanford.edu/workshops/jrdb… For more info, contact @SHamidRezatofig and Roberto Martin-Martin
- Are you a passionate and experienced researcher in robotics with knowledge in computer vision? Do you want to build impactful robotic systems? Stanford Vision and Learning lab (SVL) is searching for a Postdoctoral Fellow with your skills.
- Our focus on robot learning from single example of a task through a video has resulted in a line of work that combines symbolic systems with neural networksReplying to @animesh_gargThis continues our efforts in neuro-symbolic planning for one-shot imitation in multi-step reasoning domains. 1. Neural Task Programs: arxiv.org/abs/1710.01813 2. Neural Task Graphs: arxiv.org/abs/1807.03480 3. Continuous Relaxation of Symbolic Planner: arxiv.org/abs/1908.06769
