recurrent video models

**Recurrent video models** are the **sequence architectures that process frames one step at a time while carrying a hidden state as temporal memory** - they are designed for streaming scenarios where future frames are unavailable and long videos must be handled incrementally. **What Are Recurrent Video Models?** - **Definition**: Video networks based on RNN, LSTM, or GRU style recurrence over frame or clip features. - **State Mechanism**: Hidden state summarizes prior observations and updates with each new timestep. - **Typical Inputs**: Raw frames, CNN features, or token embeddings from lightweight backbones. - **Output Modes**: Per-frame labels, clip summaries, sequence forecasts, and online detections. **Why Recurrent Video Models Matter** - **Streaming Readiness**: Natural fit for online inference where data arrives continuously. - **Memory Efficiency**: Stores compact state instead of full frame history. - **Low Latency**: Produces predictions at each timestep without full-clip buffering. - **Long-Horizon Potential**: Can, in principle, process arbitrarily long sequences. - **System Simplicity**: Easy to integrate with sensor pipelines and edge devices. **Common Recurrent Designs** **Feature-RNN Pipelines**: - CNN extracts frame features and recurrent core models temporal dynamics. - Works well for lightweight action recognition. **Conv-Recurrent Blocks**: - Recurrence applied to spatial feature maps for better structure retention. - Useful for prediction and segmentation over time. **Bidirectional Recurrence**: - Uses forward and backward passes when offline full video is available. - Improves context at cost of streaming compatibility. **How It Works** **Step 1**: - Encode incoming frame to features and combine with previous hidden state in recurrent unit. **Step 2**: - Update hidden state and emit prediction for current timestep, then iterate across sequence. **Tools & Platforms** - **PyTorch sequence modules**: LSTM, GRU, and custom recurrent cells. - **Streaming inference runtimes**: Causal deployment with persistent state buffers. - **Monitoring utilities**: Track hidden-state drift and long-sequence stability. Recurrent video models are **the classic one-step-at-a-time backbone for temporal perception in streaming systems** - they remain valuable when low latency and bounded memory are primary requirements.