Multimodal Video Analysis and Its Applications: Intelligent Asset Management, Automatic Cover Generation, Knowledge Graph, and Search

1. Video Digital Asset Intelligence

The talk begins with an overview of Youku's video business layout, describing long, short, and micro‑video categories, and emphasizes the need to dissect video content into hierarchical elements for better asset utilization.

1.2 Multimodal Video Analysis Techniques

Expression learning: converting audio, visual, and textual signals into unified embeddings for richer retrieval and recommendation.

Modal mapping: aligning entities across modalities such as speech and images.

Modal alignment and collaborative learning: jointly training across modalities to mitigate high annotation costs.

1.3 Video Content Analysis

Examples illustrate frame‑level detection of actors, emotions, actions, and scenes, enabling fine‑grained labeling and personalized playback experiences.

1.4 Intelligent Video Production

By decomposing videos into elemental components, the system can recombine them for tasks like generating rap‑style clips from dramas or creating near‑real‑time highlights for sports events.

2. Automatic Cover Generation and Personalized Distribution

The need for scalable cover creation is discussed, contrasting Netflix’s high‑budget approach with the challenges of Chinese platforms that have massive legacy libraries.

2.1 What Is a Video Cover

Covers must attract attention and convey content, differing from book covers.

2.2 How Covers Are Obtained

Manual processes involve selecting clear, representative frames and extensive artistic editing.

2.3 Characteristics of a Good Cover

Good covers combine visual appeal, artistic effect, and emotional resonance, often produced by beautification and artistic rendering pipelines.

2.4 Production Workflow

Key steps include extracting key frames, aesthetic labeling, automated editing, and optional designer‑driven template rendering.

2.5 Quality Assessment

Quality control employs pre‑ and post‑processing, low‑quality frame filtering, blur detection via CNN classifiers, and object/action recognition to ensure high‑quality outputs.

2.6 Personalized Distribution

Generated covers are served using bandit‑style algorithms, with plans to explore reinforcement learning for further optimization.

3. Video Knowledge Graph

The knowledge graph organizes entities (people, IP, topics) and their relationships, supporting content discovery, copyright analysis, and production planning.

4. Multimodal Video Search

Challenges such as missing modality information, diverse query intents, and B‑to‑B demand are addressed by multimodal retrieval, dimensionality reduction, and ensemble labeling, yielding significant improvements in click‑through and bounce rates.

5. Future Outlook

Multimodal search and recommendation will become a core trend.

Natural multimodal interaction on shared screens will boost cross‑modal retrieval research.

Intelligent media asset libraries will become standard for platforms and enterprises.

AI‑driven generation will replace much of the manual PGC workflow.

Images illustrating system architecture, examples, and performance metrics are embedded throughout the original presentation.