For video understanding in multimodal search, models that effectively combine visual, temporal, and textual information tend to perform best. Three key approaches include transformer-based architectures for spatiotemporal reasoning, multimodal embedding models trained on paired data, and hybrid models that merge convolutional and attention mechanisms. These models excel because they handle the complexity of video data—such as object interactions, temporal dynamics, and cross-modal relationships—while enabling efficient search and retrieval.
One effective approach involves transformer-based models adapted for video. For example, TimeSformer applies self-attention mechanisms across both space (individual frames) and time (sequence of frames). This allows the model to track objects and actions over time, which is critical for tasks like action recognition or event detection. Another example is ViViT, which splits video into spatial and temporal tokens, processing them with separate transformer layers. These models are often pre-trained on large video datasets like Kinetics-400, learning general features for motion and context. Developers can fine-tune them on custom datasets using frameworks like PyTorch or TensorFlow, modifying attention heads to prioritize specific temporal patterns relevant to their search use case.
Multimodal embedding models like CLIP (originally designed for images and text) can also be adapted for video. By processing video frames as a sequence of images and aggregating their embeddings, CLIP enables cross-modal search—for example, finding videos that match a text query like “a dog chasing a ball.” Extensions like VideoCLIP or Flamingo add temporal pooling or attention layers to better capture video-specific context. For instance, VideoCLIP uses contrastive learning on video-text pairs to align actions (e.g., “opening a door”) with their visual representations. These models are useful for retrieval tasks, as they map videos and text into a shared embedding space where similarity can be measured using cosine distance or approximate nearest-neighbor search libraries like FAISS.
Hybrid architectures combine the strengths of convolutional neural networks (CNNs) and transformers. SlowFast Networks, for example, use two pathways: a “slow” branch to capture spatial details and a “fast” branch to detect motion. This is particularly effective for action recognition in videos with subtle movements, like sports analysis. Another example is X3D, a family of efficient 3D CNNs that scale across depth, width, and temporal resolution, making them practical for real-time applications. Developers can pair these models with audio-processing networks (e.g., VGGish for sound) or optical flow estimators to enrich multimodal features. For deployment, tools like NVIDIA’s Triton Inference Server can optimize these pipelines, balancing latency and accuracy for search applications.
In summary, the best models for video understanding in multimodal search balance spatial and temporal analysis, leverage cross-modal training, and prioritize efficiency. Transformers like TimeSformer, adapted multimodal embeddings like VideoCLIP, and hybrid models like SlowFast provide flexible starting points, which developers can customize using open-source frameworks and domain-specific datasets.