| --- |
| pretty_name: SMILE artifacts |
| language: |
| - en |
| tags: |
| - in-context-learning |
| - vision-language-models |
| - hard-negatives |
| task_categories: |
| - visual-question-answering |
| - image-classification |
| viewer: false |
| --- |
| |
| # SMILE: CLIP embeddings, mined hard-negative pools and predictions |
|
|
| Artifacts for **The Value of Being Wrong: Self-Mined Visual In-Context Learning from Errors** (NeurIPS 2026 Spotlight). |
| Paper: [arXiv:2610.16977](https://arxiv.org/abs/2610.16977). |
| Code: [github.com/Tree-Shu-Zhao/SMILE](https://github.com/Tree-Shu-Zhao/SMILE). |
|
|
| ## Contents |
|
|
| ``` |
| clip_embeddings/ |
| <data>_<split>_image.pt # CLIP ViT-g-14 (laion2b_s12b_b42k) image embeddings, L2-normalized |
| <data>_<split>_ids.pt # the sample id of each row |
| hard_negatives/<data>/<org_model>/ |
| hard_negatives.jsonl # the pool: training samples the model gets wrong, with its voted prediction |
| hard_negatives_predictions.jsonl # the voted prediction for every training sample |
| hard_negatives_stats.json |
| hard_negatives_params.json |
| predictions/<data>/<org_model>/ |
| vicl_<k>_<negative_k>.jsonl # test predictions, with retrieved_ids and contrastive_ids |
| vicl_<k>_<negative_k>_params.json |
| ``` |
|
|
| - `<data>`: `cub`, `flowers`, `okvqa`, `vizwiz`. `<split>`: `train`, `test` (for VizWiz, `test` is its val split). |
| - `<org_model>`: `Qwen_Qwen2.5-VL-7B-Instruct`, `Qwen_Qwen2.5-VL-3B-Instruct`, `google_gemma-3-12b-it`, |
| `google_gemma-3-4b-it`. |
| - Pool records: `id`, `question`, `imgpath`, `gt_answer`, `voted_pred`, `vote_count`, `total_votes`, `token_f1`, |
| `is_correct`. |
| - Prediction records: `id`, `question`, `imgpath`, `pred_answer`, `gt_answer`, `k`, `retrieved_ids`, |
| `contrastive_ids`. The VizWiz records hold `id`, `pred_answer`, `query_prediction`, `retrieved_ids` and |
| `contrastive_ids`. |
| - The predictions are one inference run per model and dataset. VizWiz predictions are stored before the |
| answerability vote, which the scorer applies. |
| - `imgpath` is relative to the root of the code repository (`data/<dataset>/...`). |
| - The records contain questions and answers from CUB-200-2011, Oxford 102 Flowers, OK-VQA and VizWiz, which remain |
| under the terms of those datasets. No images are included. |
|
|
| ## Usage |
|
|
| From the root of the code repository: |
|
|
| ```bash |
| uvx --from "huggingface_hub>=1.0" hf download TreezzZ/SMILE-data --repo-type dataset --local-dir . \ |
| --include "clip_embeddings/*" --include "hard_negatives/*" --include "predictions/*" |
| ``` |
|
|
| The files land where the code reads them. To score the released predictions: |
|
|
| ```bash |
| bash scripts/cub/evaluate.sh predictions/cub/Qwen_Qwen2.5-VL-7B-Instruct/vicl_8_4.jsonl |
| bash scripts/vizwiz/evaluate.sh predictions/vizwiz/Qwen_Qwen2.5-VL-7B-Instruct/vicl_12_8.jsonl |
| ``` |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{zhao2026smile, |
| title={The Value of Being Wrong: Self-Mined Visual In-Context Learning from Errors}, |
| author={Shu Zhao and Tan Yu}, |
| year={2026}, |
| eprint={2610.16977}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CV}, |
| url={https://arxiv.org/abs/2610.16977}, |
| } |
| ``` |
|
|
