Compositional Temporal Visual Grounding of Natural Language Event Descriptions
This work addresses the challenge of accurately linking language descriptions to video events for applications in video understanding and retrieval, representing an incremental improvement with specific gains.
The paper tackles the problem of temporal grounding by establishing correspondences between natural language event descriptions and video segments, using a compositional approach to recognize atomic sub-events and model temporal relationships. The result is that their CTG-Net system outperforms prior state-of-the-art methods on multiple temporal grounding datasets.
Temporal grounding entails establishing a correspondence between natural language event descriptions and their visual depictions. Compositional modeling becomes central: we first ground atomic descriptions "girl eating an apple," "batter hitting the ball" to short video segments, and then establish the temporal relationships between the segments. This compositional structure enables models to recognize a wider variety of events not seen during training through recognizing their atomic sub-events. Explicit temporal modeling accounts for a wide variety of temporal relationships that can be expressed in language: e.g., in the description "girl stands up from the table after eating an apple" the visual ordering of the events is reversed, with first "eating an apple" followed by "standing up from the table." We leverage these observations to develop a unified deep architecture, CTG-Net, to perform temporal grounding of natural language event descriptions to videos. We demonstrate that our system outperforms prior state-of-the-art methods on the DiDeMo, Tempo-TL, and Tempo-HL temporal grounding datasets.