EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
Hacker News·Aug 27·5 min read
Getting video models to learn better, faster
TL;DR
Recent advancements in image and video models stem from improved data strategies rather than changes in model architecture. Key methods include data filtering, enhanced annotations, and synthetic data generation.
✦ Why It Matters
Prioritize data quality by filtering and enhancing annotations to improve your model's learning efficiency today.
Key Takeaways
- Prioritize data quality over quantity in model training.
- Implement advanced filtering techniques to improve learning efficiency.
- Utilize synthetic data to fill gaps in training datasets.
- Transition from traditional CV methods to neural networks for better results.
How It Works
The authors replaced traditional filtering methods with fine-tuned neural networks, allowing for more precise data categorization and improved model training. By leveraging tools like PaddleOCR and custom CUDA kernels, they enhanced processing speed and accuracy, enabling the model to learn complex actions more effectively.