The best models for generating video embeddings typically fall into three categories: 3D convolutional neural networks (CNNs), transformer-based architectures, and hybrid models combining visual and temporal processing. These models are designed to capture both spatial (visual) and temporal (motion) information in videos, which is critical for tasks like action recognition, video retrieval, or content-based recommendation systems. The choice depends on factors like computational resources, dataset size, and specific use cases.
For 3D CNNs, models like C3D and I3D (Inflated 3D ConvNet) are widely used. C3D applies 3D convolutions directly to video clips, treating time as a third dimension alongside height and width. This allows it to learn spatiotemporal features but requires significant computational power. I3D improves on this by “inflating” 2D convolutional filters (e.g., from pretrained image models like ResNet) into 3D, enabling better initialization and performance. Another option is SlowFast Networks, which use two pathways: a “slow” stream for spatial details and a “fast” stream for motion, balancing accuracy and efficiency. These models are often pretrained on large datasets like Kinetics, making them effective for transfer learning.
Transformer-based models like ViViT (Video Vision Transformer) and TimeSformer have gained traction for their ability to model long-range dependencies. ViViT divides a video into spatiotemporal patches and processes them with self-attention, while TimeSformer optimizes computation by splitting attention across space and time. These models excel at capturing complex interactions but require substantial memory. For lightweight solutions, CLIP-based approaches (e.g., VideoCLIP) extend the image-text alignment idea to videos by training on video-text pairs, producing embeddings useful for cross-modal tasks. Libraries like PyTorch Video or Hugging Face Transformers provide implementations, and pretrained weights are often available for quick experimentation.
When implementing video embeddings, consider using pretrained models from frameworks like TensorFlow Hub or TorchHub to save training time. For example, I3D pretrained on Kinetics can be loaded in a few lines of code and fine-tuned on custom data. If computational resources are limited, feature extraction tools like MediaPipe or OpenCV-based methods can extract keyframes or optical flow for simpler models. Always validate embeddings on downstream tasks—test whether they improve accuracy in retrieval or classification compared to handcrafted features. Balancing model size, inference speed, and task requirements will help you choose the best approach.