diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..e6250b3d94876076b325b17f9b90ae110a0188d6 --- /dev/null +++ b/README.md @@ -0,0 +1,48 @@ +--- +pretty_name: VisAnomBench +tags: +- anomaly-detection +- time-series +- vision-language +--- + +# VisAnomBench + +VisAnomBench is a benchmark for vision-language reasoning over time-series anomaly plots. It is designed to evaluate whether a model can inspect a plotted time series, decide if an anomaly is present, localize the anomalous interval, and explain the visual evidence behind the decision. + +Unlike many time-series anomaly benchmarks that mainly provide raw numeric sequences and anomaly labels, VisAnomBench pairs rendered plots with language prompts. The training split also includes reference answers that localize anomalies and explain the visual evidence, making it suitable for supervised fine-tuning (SFT) of vision-language models. + +## Contents + +| Path | What it contains | +| --- | --- | +| `VisAnomBench_train.json` | Training prompts, image paths, anomaly intervals, and reference answers with visual explanations for SFT. | +| `VisAnomBench_test.json` | Test prompts, image paths, and anomaly intervals for evaluation. Reference explanation responses are not included. | +| `VisAnomBench_plot_train/` | Rendered plot images used by the training JSON. | +| `VisAnomBench_plot_test/` | Rendered plot images used by the test JSON. | +| `VisAnomBench_train_ts/` | Raw time-series CSV files for the training split. | +| `VisAnomBench_test_ts/` | Raw time-series CSV files for the test split. | + +## Split Sizes + +| Split | Time series | +| --- | ---: | +| Train | 2,576 | +| Test | 740 | + +Each JSON record contains an `image` path and a `query` prompt. Training records also include a `resp` field with the reference answer used for SFT. The `anomalies` field stores the ground-truth interval for scoring. CSV files contain `timestamp`, `value`, and `label` columns. + +## Citation + +```bibtex +@article{zhou2026tiny, + title={Tiny but Trusted: Efficient Vision-Language Reasoning for Time-Series Anomaly Detection}, + author={Zhou, Xiaona and Wahed, Muntasir and Yu, Tianjiao and Brif, Constantin and Lourentzou, Ismini}, + journal={arXiv preprint arXiv:2605.30344}, + year={2026} +} +``` + +## Contact + +For questions or suggestions, contact Xiaona Zhou at xiaonaz2@illinois.edu. diff --git a/VisAnomBench_plot_test/007_UCR_Anomaly_DISTORTEDCIMIS44AirTemperature3_4000_6520_6544_seg01_6374-6628.png b/VisAnomBench_plot_test/007_UCR_Anomaly_DISTORTEDCIMIS44AirTemperature3_4000_6520_6544_seg01_6374-6628.png new file mode 100644 index 0000000000000000000000000000000000000000..da4b24661dfcc88b63f5cd73208e13cc2887dbfc --- /dev/null +++ b/VisAnomBench_plot_test/007_UCR_Anomaly_DISTORTEDCIMIS44AirTemperature3_4000_6520_6544_seg01_6374-6628.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 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