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 …

Abstract Object State Changes (OSCs) are pivotal for video understanding. While humans
can effortlessly generalize OSC understanding from familiar to unknown objects current …

What will the future be? We wonder! In this survey, we explore the gap between current
research in egocentric vision and the ever-anticipated future, where wearable computing …

Daily objects commonly experience state changes. For example, slicing a cucumber
changes its state from whole to sliced. Learning about object state changes in Video Object …

This paper studies the problem of concept-based interpretability of transformer
representations for videos. Concretely we seek to explain the decision-making process of …

Current state-of-the-art Video Object Segmentation (VOS) methods rely on dense per-object
mask annotations both during training and testing. This requires time-consuming and costly …

The Segment Anything Model 2 (SAM 2) has emerged as a powerful foundation model for
object segmentation in both images and videos, paving the way for various downstream …

Egocentric videos provide a first-person perspective of the wearer's activities involving
simultaneous interactions with multiple objects. In this work we propose the task of weakly …

With recent video object segmentation (VOS) benchmarks evolving to challenging scenarios
we revisit a simple but overlooked strategy: restricting the size of memory banks. This …

Delving into the realm of egocentric vision, the advancement of referring video object
segmentation (RVOS) stands as pivotal in understanding human activities. However …