Motion-aware contrastive learning on mask tubes improves temporal panoptic scene graph generation over pooling-based methods on video and 4D datasets.
Video-Language Understanding: A Survey from Model Architecture, Model Training, and Data Perspectives
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
Humans use multiple senses to comprehend the environment. Vision and language are two of the most vital senses since they allow us to easily communicate our thoughts and perceive the world around us. There has been a lot of interest in creating video-language understanding systems with human-like senses since a video-language pair can mimic both our linguistic medium and visual environment with temporal dynamics. In this survey, we review the key tasks of these systems and highlight the associated challenges. Based on the challenges, we summarize their methods from model architecture, model training, and data perspectives. We also conduct performance comparison among the methods, and discuss promising directions for future research.
fields
cs.CV 1years
2024 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Motion-aware Contrastive Learning for Temporal Panoptic Scene Graph Generation
Motion-aware contrastive learning on mask tubes improves temporal panoptic scene graph generation over pooling-based methods on video and 4D datasets.