We present Segment Anything Model 2 (SAM 2), a foundation model towards solving
promptable visual segmentation in images and videos. We build a data engine, which …

Scaling up the size of vision models has been the de facto standard to obtain more powerful
visual representations. In this work, we discuss the point beyond which larger vision models …

In this work we study the benefits of using tracking and 3D poses for action recognition. To
achieve this, we take the Lagrangian view on analysing actions over a trajectory of human …

Recent advances in segmentation foundation models have enabled accurate and efficient
segmentation across a wide range of natural images and videos, but their utility to medical …

As the deep learning revolution marches on, self-supervised learning has garnered
increasing attention in recent years thanks to its remarkable representation learning ability …

An ideal model for dense video captioning--predicting captions localized temporally in a
video--should be able to handle long input videos predict rich detailed textual descriptions …

Image segmentation plays an important role in vision understanding. Recently, the emerging
vision foundation models continuously achieved superior performance on various tasks …

Most neural networks assume that input images have a fixed number of channels (three for
RGB images). However, there are many settings where the number of channels may vary …

Action Localization is a challenging problem that combines detection and recognition tasks
which are often addressed separately. State-of-the-art methods rely on off-the-shelf …

We interact with the world with our hands and see it through our own (egocentric)
perspective. A holistic 3D understanding of such interactions from egocentric views is …