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PREGO: online mistake detection in PRocedural EGOcentric videos (CVPR 2024)

-**[Project Page](https://www.pinlab.org/prego)**

| PREGO paper [CVPR 2024] | TI-PREGO paper [arXiv]

Index

1. Introduction
2. News
3. Preparation
   • Data
   • LLAMA
   • Environment
4. Usage
   • Step Recognition
   • Data Aggregation
   • Step Anticipation
     • Data Preparation
     • Parameters
     • Run
5. Reference

Introduction

This repo hosts the official PyTorch implementations of the IEEE/CVF Computer Vision and Pattern Recognition (CVPR) '24 paper PREGO: online mistake detection in PRocedural EGOcentric videos and of the follow-up paper TI-PREGO: Chain of Thought and In-Context Learning for Online Mistake Detection in PRocedural EGOcentric Videos.

PREGO is the first online one-class classification model for mistake detection in procedural egocentric videos. It uses an online action recognition component to model current actions and a symbolic reasoning module to predict next actions, detecting mistakes by comparing the recognized current action with the expected future one. We evaluate this on two adapted datasets, Assembly101-O and Epic-tent-O, for online benchmarking of procedural mistake detection.

News

[2024-11-12] Uploaded the TSN features for Assembly101-O and Epic-tent-O [GDrive].

[2024-11-04] Published the follow-up paper [TI-PREGO].

[2024-06-20] Presented PREGO at #CVPR2024.

[2024-06-16] Uploaded the anticipation branch.

Preparation

Data

- Work in progress

LLAMA

In order to run our anticipation step with LLAMA, you have to be granted access to the models by Meta here. Place them whenever you like, just recall to update the paths whenever necessary, as in step_anticipation/scripts/anticipation.sh.

Environment

You can choose between creating a conda or virtualenv environment, as you prefer

# conda
conda create -n prego python
conda activate prego

# virtualenv
python -m venv .venv
source .venv/bin/activate

Then, install the requirements

pip install -r requirements.txt

Install unsloth following the instructions here.

Usage

Step Recognition

For the Step Recognition branch, you can refer to the official implementation of MiniROAD here.

Data Aggregation

The data aggregation process is handled by the utils/aggregate.py script. This script is responsible for aggregating predictions and ground truth data, and saving the results to a JSON file.

To run the data aggregation script, use the following command:

python utils/aggregate.py <input_path> <output_path>

• <input_path>: Path to the input JSON file containing the data.
• <output_path>: Path to save the aggregated JSON file.

Example

python utils/aggregate.py data/input.json data/output/aggregated_data.json

Step Anticipation

Data Preparation

Description of the steps needed to prepare the data for the Step Anticipation branch.

Step Recognition predictions:

• place the predictions (after aggregation) of the Step Recognizer in the step_anticipation/data/predictions
• the file should have the following structure:

{
    "nusar-2021_action_both_9044-a08_9044_user_id_2021-02-05_154403": {
        "pred": [
            39,
            37,
            74,
            39,
            37
        ],
        "gt": [
            37,
            80,
            39,
            29,
            85
        ]
    },
...
}

Context prompt:

• step_anticipation/data/context_prompt/assembly_context_prompt_train.json and step_anticipation/data/context_prompt/epictents_context_prompt_train.json contain the context to be used for the In-context learning prompt.
• step_anticipation/data/context_prompt/context_prompt.json contains the strings to fill the context prompt.

Parameters

Description of the parameters that can be added to the step_anticipation/scripts/anticipation.sh script.

• ckpt_dir=/path/to//llama/llama-2-7b
• tokenizer_path=/path/to/tokenizer/llama/tokenizer.model
• max_seq_len=2048 Maximum sequence length for input text
• max_batch_size Maximum batch size for generating sequences
• temperature Temperature value for controlling randomness in generation
• max_gen_len Maximum length of the generated text sequence.
• num_samples How many generations per each input context
• use_gt Select if gt or predictions from Step Recognizer are used as input context
• dataset Select dataset to use. ['assembly', 'epictent']
• type_prompt Select which type of context to be passed. ['num', 'alpha', 'emoji']
• toy_class_context For the assembly dataset only. If True, the input context has all the example from the same class of toys
• recognition_model If not use_gt, select which Step Recognizer predictions to use. ['miniROAD', 'OadTR']
• prompt_context Select how the prompt context is structured. ['default', 'unreferenced','elaborate','no-context']

Run

cd step_anticipation
./scripts/anticipation.sh

Reference

If you find our code or paper to be helpful, please consider citing:

@InProceedings{Flaborea_2024_CVPR,
    author    = {Flaborea, Alessandro and di Melendugno, Guido Maria D'Amely and Plini, Leonardo and Scofano, Luca and De Matteis, Edoardo and Furnari, Antonino and Farinella, Giovanni Maria and Galasso, Fabio},
    title     = {PREGO: Online Mistake Detection in PRocedural EGOcentric Videos},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2024},
    pages     = {18483-18492}
}

@misc{plini2024tipregochainthoughtincontext,
      title={TI-PREGO: Chain of Thought and In-Context Learning for Online Mistake Detection in PRocedural EGOcentric Videos}, 
      author={Leonardo Plini and Luca Scofano and Edoardo De Matteis and Guido Maria D'Amely di Melendugno and Alessandro Flaborea and Andrea Sanchietti and Giovanni Maria Farinella and Fabio Galasso and Antonino Furnari},
      year={2024},
      eprint={2411.02570},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2411.02570}, 
}