Learning Object State Changes in Videos: An Open-World Perspective

Abstract

Object State Changes (OSCs) are pivotal for video understanding. While humans can effortlessly generalize OSC understanding from familiar to unknown objects, current approaches are confined to a closed vocabulary. Addressing this gap, we introduce a novel open-world formulation for the video OSC problem. The goal is to temporally localize the three stages of an OSC-the object's initial state, its transitioning state, and its end state-whether or not the object has been observed during training. Towards this end, we develop Vidosc, a holistic learning approach that: (1) leverages text and vision-language models for super-visory signals to obviate manually labeling OSC training data, and (2) abstracts fine-grained shared state representations from objects to enhance generalization. Furthermore, we present HowToChange, the first open-world benchmark for video OSC localization, which offers an order of mag-nitude increase in the label space and annotation volume compared to the best existing benchmark. Experimental results demonstrate the efficacy of our approach, in both tra-ditional closed-world and open-world scenarios.11Project webpage: https://vision.cs.utexas.edu/projects/VidOSC/.