example-2_00

• This paper has been accepted by CVPR 2024!
• This is the official repository for the paper "HalluciDoctor: Mitigating Hallucinatory Toxicity in Visual Instruction Data". 🍇 [Read our arXiv Paper].
• We update the main code of HalluciDoctor and corresponding datasets LLaVA+ and LLaVA++.
• Release LLaVA+ and LLaVA++ model checkpoints for MiniGPT4 with vicuna-7b.

• Dataset preparation: LLaVA-158K; coco_category.json; coco_object_co_occur.json; object_sample for 'counterfactual images' following INSTALL.md .
• HalluciDoctor Framework
  1. PYTHONPATH=./ python models/question_generator.py
  2. PYTHONPATH=./ python models/blip2_candidate_answer_generator.py; PYTHONPATH=./ python models/instructblip_candidate_answer_generator.py; PYTHONPATH=./ python models/minigpt4_candidate_answer_generator.py --cfg-path eval_configs/minigpt4_eval.yaml --gpu-id 0
  3. PYTHONPATH=./ python models/consistency_crosscheck.py; PYTHONPATH=./ python models/consistency_crosscheck_object.py
  4. PYTHONPATH=./ python models/refine_dataset.py -> LLaVA+
  5. PYTHONPATH=./ python models/seesaw_counterfactual_generation.py -> LLaVA++
• MLLM fine-tuning on LLaVA+ and LLaVA++ in the refined_datasets.

If you find this work useful for your research, please cite our paper and star our git repo:

@misc{yu2023hallucidoctor,
 title={HalluciDoctor: Mitigating Hallucinatory Toxicity in Visual Instruction Data}, 
 author={Qifan Yu and Juncheng Li and Longhui Wei and Liang Pang and Wentao Ye and Bosheng Qin and Siliang Tang and Qi Tian and Yueting Zhuang},
 year={2023},
 eprint={2311.13614},
 archivePrefix={arXiv},
 primaryClass={cs.CV}
}