Causal Framework for Egocentric Video Object Segmentation

This paper addresses the challenging problem of segmenting objects in egocentric videos based on language queries. It's significant because it tackles the inherent ambiguities and biases in egocentric video data, which are crucial for understanding human behavior from a first-person perspective. The proposed causal framework, CERES, is a novel approach that leverages causal intervention to mitigate these issues, potentially leading to more robust and reliable models for egocentric video understanding.

• •Proposes CERES, a causal framework for Egocentric Referring Video Object Segmentation (Ego-RVOS).
• •Addresses ambiguities and biases in egocentric videos using dual-modal causal intervention.
• •Achieves state-of-the-art performance on Ego-RVOS benchmarks.