---
license: mit
tags:
- video-classification
- event-detection
---
# Team ERIS: IWDD 2027 illegal waste dumping detection

Weights of Team ERIS's entry to the International Contest on Illegal Waste Dumping Detection (IWDD 2027).
A frozen ImageNet-21k ViT-B/16 with bottleneck adapters and a temporal transformer head classifies 3.2 s windows
(16 frames at 5 fps) as normal or dumping; a threshold decoder turns the window probabilities into one onset per video.

`iwdd_s4.pth` holds `{'model': state_dict}` for the `VideoModel` in the submission's `model.py`;
`test.py` downloads it from this repository when no local copy is given.

## Paper and code

This model is the Window Classifier of the paper "Parameter-Efficient Image-to-Video Adaptation for Action Spotting of
Illegal Waste Dumping" (WACV 2027 Workshops, WasteVision). The code is at
https://github.com/arkel23/PEI2V_WasteVision27; its README section "Pretrained weights" shows how to download this file
and score the public-test windows with `tools.train --test_only`.

## Citation

```bibtex
@inproceedings{rios2027pei2v,
  title={Parameter-Efficient Image-to-Video Adaptation for Action Spotting of Illegal Waste Dumping},
  author={Rios, Edwin Arkel and Hu, Min-Chun and Lai, Bo-Cheng},
  booktitle={IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)},
  year={2027}
}

@inproceedings{greco2027evolution,
  title={The Evolution of Illegal Waste Dumping Detection in the Age of Foundation Models},
  author={Greco, Antonio and Ricciardi, Andrea Vincenzo and Sansone, Carlo and Vento, Bruno},
  booktitle={IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)},
  year={2027}
}
```