


R-CNN author Ross Girshick resigned, He Kaiming and Xie Saining returned to academia, how many great minds have emerged from Meta CV
Yann LeCun said: "It is our loss that talents leave FAIR, but I am still happy for them."
Another big-name research scientist has left, this time Ross Girshick, the author of R-CNN
Recently, Meta chief scientist Yann LeCun announced on Twitter, Ross Girshick will leave FAIR to join the Allen Institute for Artificial Intelligence (AI2). Previous resignations include ResNeXt author Xie Saining (joined New York University as an assistant professor), Georgia Gkioxari (joined Caltech as an assistant professor), etc.
Source: https://twitter.com/ylecun/status/1730713022195470541
us I checked Ross Girshick's personal homepage and confirmed his resignation from FAIR. He will join AI2 in early 2024.
Ani Kembhavi, senior director of computer vision at AI2, said Ross Girshick will join the PRIOR team. PRIOR stands for Perceptual Reasoning and Interaction Research. It is the computer vision research team of AI2 and is committed to advancing computer vision research to create AI systems that can see, explore, learn and reason about the world
Source: https://twitter.com/anikembhavi/status/1730655170038821085
##Ross Girshick posted an article recalling his career at Meta, saying FAIR was and will remain an amazing place. But staying in one place for too long (8 years) may be a good reason to leave. Reinitialization and randomization are very important in a research career. In addition, he also stated that any talk about publishing indicators is pure nonsense.Please refer to the following link for image source: https://twitter.com/inkynumbers/status/1730735493711810639
In fact, in addition to the fact thatRewritten content is as follows: Image source: https://twitter.com/LearnOpenCV/status/1730736970136158274
In the past year, Meta has successively open sourced large-scale models of the Llama and Llama 2 series, becoming an indispensable force in the open source community. However, Meta also faces many challenges in retaining artificial intelligence talents, and the loss of talent is inevitable. Scientists like Ross Girshick who have extensive experience in industry moving to universities or non-profit institutions will bring a unique perspective to academia and potentially produce more impactful research
RBG Master: Introduction to Ross Girshick
Personal homepage link: https://www.rossgirshick.info/
Previously, Ross Girshick was a research scientist at Meta FAIR, working on computer vision and machine learning from 2015 to 2023. He received his PhD in computer science from the University of Chicago in 2012.
Before joining FAIR, Ross served as a researcher at Microsoft Research and a postdoctoral fellow at the University of California, Berkeley. There, he studied under Professors Jitendra Malik and Trevor Darrell
Ross’s research interests include visual perception algorithms (object recognition, localization, segmentation, pose estimation, etc.), representation learning (using pre-trained networks with strong supervision, weak supervision, or no supervision at all) and vision and language research.
For his contributions to open source software and datasets, Ross was awarded the 2017 PAMI Young Investigator Award, as well as the 2017, 2021, and 2023 PAMI Mark Everingham Awards
Ross Achieving many achievements in the field of artificial intelligence, he first became famous for developing the region-based convolutional neural network (R-CNN) object detection method. This research can be said to have completely changed the research direction in the field of target detection. Subsequent research such as Fast-RCNN and Faster-RCNN are all developed on the basis of R-CNN
His Google Scholar citation is currently Already more than 410,000 times
In the work Ross has participated in in the past, there are many popular research projects, such as Fast R-CNN, Mask R-CNN, YOLO, Faster R-CNN and SAM, etc.
In 2017, Mask R-CNN, which Ross participated in, won the ICCV Marr Award (Best Paper). This paper now has more than 30,000 citations; another paper "Focal Loss for Dense Object Detection" won the ICCV Best Student Paper that year.
In 2021, the paper "Masked Autoencoders Are Scalable Vision Learners" in which Girshick participated became a hot topic in the computer vision circle. This paper demonstrates a new method called masked autoencoders (MAE) that can be used as a scalable self-supervised learner for computer vision.
This year, Meta released the "Segment Anything" (Segment Anything) model (SAM), which has been hailed by many as a subversion of research in the traditional CV field. Ross is one of the authors of this paper. one.
Now I choose to go to AI2, and I look forward to more amazing works from Girshick.
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