📚 https://arxiv.org/abs/2403.15377
🏆 Published in CVPR 2024 (Best Paper Honorable Mention)

📄 InternVideo2 – Scaling Video Foundation Models for Multimodal Understanding

✨ Key Contributions

• Introduces a unified multimodal video-language foundation model integrating visual, textual, and auditory modalities.
• Proposes a progressive training framework combining masked video modeling, contrastive learning, and next-token prediction.
• Achieves state-of-the-art performance on over 60 benchmarks, demonstrating strong generalization and scalability.
• Scales up to 6B parameters, trained on 400M video-text pairs, showing consistent performance gains with model size.

🎯 Problem Definition

• Prior vision-language models (e.g., CLIP, BLIP-2) handle static images well but struggle with temporal coherence in videos.
• Existing methods use fragmented objectives—contrastive, reconstruction, or captioning—without a unified structure.
• The challenge: Build a scalable and cohesive video foundation model capable of understanding motion, sound, and language together.

🧠 Method / Architecture

1. Progressive Multimodal Training
   • Stage 1: Masked video modeling (MVM) for spatial-temporal representation learning.
   • Stage 2: Multimodal contrastive learning aligning video–text–audio–speech features.
   • Stage 3: Next-token prediction for generative reasoning and contextual understanding.
2. Model Design
   • Hierarchical temporal encoder for long-term motion modeling.
   • Unified transformer backbone handling multimodal fusion.
   • Large-scale distributed training using DeepSpeed and FlashAttention.

🧪 Experiments & Results

• Trained on 400M multimodal pairs from diverse video datasets.
• Evaluated on retrieval, captioning, recognition, and dialogue benchmarks.
• Outperformed previous models across all 60+ tasks, confirming that multimodal supervision (especially audio and speech) enhances semantic comprehension.

🚫 Limitations

• Extremely resource-intensive; pretraining demands massive compute and data resources.
• Current framework optimized for large-scale tasks; small dataset adaptation remains limited.
• Multimodal alignment still relies on extensive labeled data.

🔭 Future Ideas

• Develop efficient pretraining techniques for smaller datasets.
• Explore adapter-based finetuning to extend flexibility to domain-specific tasks.
• Integrate reinforcement or human feedback for richer temporal and contextual understanding.

🔁 Personal Reflections

• InternVideo2 shows that video foundation models are following the same scaling laws as LLMs and CLIP.
• Its unified framework elegantly bridges reconstruction, alignment, and generation in a single pipeline.
• The study reinforces a clear trend: scaling + multimodality are the future of video understanding.