Unveiling Apollo: Meta AI's Next-Gen Video-LMMs

Meta AI Releases Apollo: A New Family of Video-LMMs Large ...

While multimodal models (LMMs) have advanced significantly for text and image tasks, video-based models remain underdeveloped. Videos are inherently complex, combining spatial and temporal dimensions that demand more from computational resources. Existing methods often adapt image-based approaches directly or rely on uniform frame sampling, which poorly captures motion and temporal patterns. Moreover, training large-scale video models is computationally expensive, making it difficult to explore design choices efficiently.

To tackle these issues, researchers from Meta AI and Stanford developed Apollo, a family of video-focused LMMs designed to push the boundaries of video understanding. Apollo addresses these challenges through thoughtful design decisions, improving efficiency, and setting a new benchmark for tasks like temporal reasoning and video-based question answering.

Apollo Models and Features

Meta AI’s Apollo models are designed to process videos up to an hour long while achieving strong performance across key video-language tasks. Apollo comes in three sizes – 1.5B, 3B, and 7B parameters – offering flexibility to accommodate various computational constraints and real-world needs.

Key innovations include:

• Apollo's efficient video sampling techniques
• Model scalability for large-scale video understanding
• Strong performance across multiple benchmarks

Results and Impact

The Apollo models are built on a series of well-researched design choices aimed at overcoming the challenges of video-based LMMs. These capabilities are validated through strong results on multiple benchmarks, often outperforming larger models.

Apollo marks a significant step forward in video-LMM development. By addressing key challenges such as efficient video sampling and model scalability, Apollo provides a practical and powerful solution for understanding video content. Its ability to outperform larger models highlights the importance of well-researched design and training strategies.

Real-World Applications

The Apollo family offers practical solutions for real-world applications, from video-based question answering to content analysis and interactive systems. Meta AI’s introduction of ApolloBench provides a more streamlined and effective benchmark for evaluating video-LMMs, paving the way for future research.

For more information, you can check out the Paper, Website, Demo, Code, and Models.