Li Fei-Fei

TL;DR: The findings show that a single CNN model can be effectively utilized for the identification of diverse abnormalities in highly variable radiographs of multiple body parts, a result that holds potential for improving patient triage and assisting with diagnostics in resource-limited settings.

TL;DR: 6-PACK learns to compactly represent an object by a handful of 3D keypoints, based on which the interframe motion of an object instance can be estimated through keypoint matching, and substantially outperforms existing methods on the NOCS category-level 6D pose estimation benchmark.

TL;DR: This work introduces a novel online game called “Bubbles” that reveals discriminative features humans use and proposes the "BubbleBank” representation that uses the human selected bubbles to improve machine recognition performance.

TL;DR: Task-Oriented Grasping Network (TOG-Net) as mentioned in this paper jointly optimizes both task-oriented grasping of a tool and the manipulation policy for that tool.

TL;DR: This work proposes to endow an artificial agent with the capability of causal reasoning for completing goal-directed tasks and develops learning-based approaches to inducing causal knowledge in the form of directed acyclic graphs, which can be used to contextualize a learned goal-conditional policy to perform tasks in novel environments with latent causal structures.