TreezzZ/SMILE-data

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. Code: 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:

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 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

@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},
}