---
license: mit
tags:
  - action-segmentation
  - temporal-action-segmentation
  - video-understanding
  - diffact
datasets:
  - gtea
  - 50salads
  - breakfast
---

# TST: Temporal Segment Transformer for Action Segmentation

Pre-trained DiffAct+TST checkpoints for temporal action segmentation on three benchmark datasets.

## Model Description

TST (Temporal Segment Transformer) is a plug-in segment-level refinement module that improves frame-level action segmentation backbones. It uses DETR-style Hungarian matching to refine backbone predictions at the segment level via cross-attention and self-attention.

These checkpoints use **DiffAct** (ICCV'23) as the backbone with TST applied as a Stage 2 refinement head (frozen backbone).

## Results

### GTEA

| Method | F1@10 | F1@25 | F1@50 | Edit | Acc |
|--------|-------|-------|-------|------|-----|
| DiffAct | 92.5 | 91.5 | 84.7 | 89.6 | 80.3 |
| **DiffAct+TST** | **94.2** | **93.0** | **87.1** | **90.9** | **81.4** |

### 50Salads

| Method | F1@10 | F1@25 | F1@50 | Edit | Acc |
|--------|-------|-------|-------|------|-----|
| DiffAct | 90.1 | 89.2 | 83.7 | 85.0 | 88.9 |
| **DiffAct+TST** | **92.3** | **91.8** | **87.4** | **87.4** | **89.7** |

### Breakfast

| Method | F1@10 | F1@25 | F1@50 | Edit | Acc |
|--------|-------|-------|-------|------|-----|
| DiffAct | 80.3 | 75.9 | 64.6 | 78.4 | 76.4 |
| **DiffAct+TST** | **81.2** | **77.1** | **65.9** | **79.0** | **76.9** |

## Checkpoints

```
gtea/
├── split_1_best.pth   (21 MB)
├── split_2_best.pth   (21 MB)
├── split_3_best.pth   (21 MB)
└── split_4_best.pth   (21 MB)

50salads/
├── split_1_best.pth   (21 MB)
├── split_2_best.pth   (21 MB)
├── split_3_best.pth   (21 MB)
├── split_4_best.pth   (21 MB)
└── split_5_best.pth   (21 MB)

breakfast/
├── split_1_best.pth   (65 MB)
├── split_2_best.pth   (65 MB)
├── split_3_best.pth   (65 MB)
└── split_4_best.pth   (65 MB)
```

Total: ~525 MB

## Usage

```python
import torch
from tst.wrapper import BackboneWithTST, DiffActAdapter
from tst.tst_refiner import TSTRefiner

# Load checkpoint
ckpt = torch.load("gtea/split_1_best.pth", map_location="cpu")

# Build model (see main repo for full setup)
refiner = TSTRefiner(n_classes=11, feat_dim=192, inner_dim=64)
model = BackboneWithTST(adapter, refiner, freeze_backbone=True)
model.load_state_dict(ckpt, strict=False)
```

See the [GitHub repository](https://github.com/yangbai123/TST-action-segmentation) for full training and evaluation code.

## Training Details

| Dataset | lr | lr_transformer | inner_dim | Epochs | Backbone |
|---------|-----|---------------|-----------|--------|----------|
| GTEA | 5e-4 | 5e-5 | 64 | 60 | DiffAct |
| 50Salads | 5e-4 | 5e-5 | 64 | 60 | DiffAct |
| Breakfast | 5e-5 | 5e-6 | 128 | 60 | DiffAct |

All trained with Adam optimizer, cosine annealing LR schedule, batch size 1.

## Citation

```bibtex
@article{tst2024,
  title={Temporal Segment Transformer for Action Segmentation},
  author={},
  year={2024}
}
```