Video-Language Understanding: A Survey from Model Architecture, Model Training, and Data Perspectives

2024-06-09

Annual Meeting of the Association for Computational Linguistics

AI Summary

This work addresses the challenge of building video-language understanding systems with human-like perceptual capabilities, enabling synergistic modeling of linguistic and dynamic visual temporal sequences. We systematically survey model architectures, training paradigms, and data construction methodologies in this domain, and introduce— for the first time—a unified, cross-perspective taxonomy that exposes core challenges including multimodal temporal alignment and dataset bias. Leveraging Transformer-based fusion, contrastive/generative pretraining, synthetic data augmentation, and benchmarks such as How2QA and Ego4D, we conduct a comprehensive, reproducible horizontal evaluation of state-of-the-art models under a standardized assessment protocol. Our key contributions are: (1) the first structured analytical framework for joint video-language modeling; (2) clear identification of critical research directions; and (3) a practical, deployable technology roadmap for embodied intelligence.

Technology Category

• Computer Vision: Language and Vision
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

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.

Problem

• Survey video-language understanding systems' model architectures
• Analyze challenges in model training for video-language tasks
• Compare performance and future research directions

Innovation

• Surveying video-language understanding systems comprehensively
• Analyzing methods from architecture, training, data perspectives
• Comparing performance and suggesting future research directions