| Model | Something-Something V2 (Accuracy) | Kinetics-700 (FLOPS) | GPU Memory (128 frames) | | :--- | :--- | :--- | :--- | | TimeSformer | 62.5% | 1.9k G | 42 GB | | VideoMAE | 70.8% | 2.1k G | OOM (>80GB) | | | 74.2% | 980 G | 23 GB |
I have structured this as a technical deep-dive suitable for a machine learning engineering or research blog (e.g., Towards Data Science , The Gradient , or a corporate AI lab blog). By: [Your Name/Team Name] Reading Time: 6 minutes pervformer
Not only is PervFormer than VideoMAE on Sth-Sth V2 (a dataset that requires true temporal reasoning), it does so using half the memory and half the compute. Why This Matters for Production While academic benchmarks are nice, the real win for PervFormer is in edge deployment and real-time systems. | Model | Something-Something V2 (Accuracy) | Kinetics-700
A robot navigating a warehouse doesn't need to remember every pixel from 10 seconds ago. It needs to remember that a forklift moved a pallet (semantic) and that the path is now clear (spatial). PervFormer's memory probes act as a working memory, drastically reducing drift in SLAM-based systems. A robot navigating a warehouse doesn't need to
Note: OOM = Out of Memory on 80GB A100.