timesformer

We have hosted the application timesformer in order to run this application in our online workstations with Wine or directly.

Quick description about timesformer:

TimeSformer is a vision transformer architecture for Video that extends the standard attention mechanism into spatiotemporal attention. The model alternates attention along spatial and temporal dimensions (or designs variants like divided attention) so that it can capture both appearance and motion cues in video. Because the attention is global across frames, TimeSformer can reason about dependencies across long time spans, not just local neighborhoods. The official implementation in PyTorch provides configurations, pretrained models, and training scripts that make it straightforward to evaluate or fine-tune on Video datasets. TimeSformer was influential in showing that pure transformer architectures�without convolutional backbones�can perform strongly on Video classification tasks. Its flexible attention design allows experimenting with different factoring (spatial-then-temporal, joint, etc.) to trade off compute, memory, and accuracy.

Features:

• Spatiotemporal transformer attention for Video modeling
• Variants: divided spatial/temporal attention and joint attention schemas
• PyTorch reference implementation with pretrained weights and scripts
• Ability to reason about long-range temporal dependencies globally
• Configurable parameters for patch size, frames, embedding dimension, and head count
• Support for fine-tuning across Video classification and recognition benchmarks

Programming Language: Python.
Categories: