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README.md

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-license: mit
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+license: mit
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+**PL-Stitch**
+-------------
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+This is the official repository for the paper [A Stitch in Time: Learning Procedural Workflow via Self-Supervised Plackett-Luce Ranking](https://www.arxiv.org/abs/2511.17805).
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+*PL-Stitch* is an image foundation model that captures visual changes over time, enabling procedural activity understanding. It takes an image as input and produces a feature vector as output, leveraging the novel Plackett-Luce temporal ranking objective to build a comprehensive understanding of both the static semantic information and the procedural context within each frame.
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+Star ⭐ us if you like it!
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+<img src="https://cdn-uploads.huggingface.co/production/uploads/67d9504a41d31cc626fcecc8/O0azUcMHjCyKYzM4vox98.png" />
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+If you use our model or code in your research, please cite our paper:
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+```
+@misc{che2025stitchtimelearningprocedural,
+      title={A Stitch in Time: Learning Procedural Workflow via Self-Supervised Plackett-Luce Ranking}, 
+      author={Chengan Che and Chao Wang and Xinyue Chen and Sophia Tsoka and Luis C. Garcia-Peraza-Herrera},
+      year={2025},
+      eprint={2511.17805},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2511.17805}, 
+}
+```
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+Abstract
+--------
+Procedural activities, ranging from routine cooking to complex surgical operations, are highly structured as a set of actions conducted in a specific temporal order. Despite their success on static images and short clips, current self-supervised learning methods often overlook the procedural nature that underpins such activities. We expose the lack of procedural awareness in current SSL methods with a motivating experiment: models pretrained on forward and time-reversed sequences produce highly similar features, confirming that their representations are blind to the underlying procedural order. To address this shortcoming, we propose PL-Stitch, a self-supervised framework that harnesses the inherent temporal order of video frames as a powerful supervisory signal. Our approach integrates two novel probabilistic objectives based on the Plackett-Luce (PL) model. The primary PL objective trains the model to sort sampled frames chronologically, compelling it to learn the global workflow progression. The secondary objective, a spatio-temporal jigsaw loss, complements the learning by capturing fine-grained, cross-frame object correlations. Our approach consistently achieves superior performance across five surgical and cooking benchmarks. Specifically, PL-Stitch yields significant gains in surgical phase recognition (e.g., +11.4 pp k-NN accuracy on Cholec80) and cooking action segmentation (e.g., +5.7 pp linear probing accuracy on Breakfast), demonstrating its effectiveness for procedural video representation learning.
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+🚩 PL-Stitch model
+------------------
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+You can download the checkpoint at [🤗 PL-Stitch](https://huggingface.co/visurg/PL-Stitch) and run the following code to extract features from your video frames.
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+   ```python
+   import torch
+   from PIL import Image
+   from pl_stitch.build_model import build_model
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+   # Load the pre-trained pl_stitch model
+   pl_stitch = build_model(pretrained_weights = 'your path to the model')
+   pl_stitch.eval()
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+   # Load the image and convert it to a PyTorch tensor
+   img_path = 'path/to/your/image.jpg'
+   img = Image.open(img_path)
+   img = img.resize((224, 224))
+   img_tensor = torch.tensor(np.array(img)).unsqueeze(0).to('cuda')
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+   # Extract features from the image
+   outputs = pl_stitch(img_tensor)
+   ```
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