This is the implementation of self-CIDEr and LSA-based diversity metrics (only for python 2.7). If you think this is helpful for your work, please cite the paper: Qingzhong Wang and Antoni Chan. Describing like humans: on diversity in image captioning. CVPR, 2019

To compute the CIDEr score, TF-IDF file is required. In our paper, the TF-IDF is obtained from MSCOCO training dataset. And to compute the diversity, multiple captions for each image should be generated and the format must be the same as the file ./results/merge_results.json.

1. Generating multiple captions for each image, for example 10 for each.
2. Put the json file in ./results and make sure that the format is the same as that of merge_results.json.
3. Download the TF-IDF file from this link and put the file in ./data. Dowonload MSCOCO validation annotation file and put it in ./annotations.
4. Fill the information in the params.json.
5. Run accuracy_evalscript.py or diversity_evalscript.py to obtain the accuracy or diversity.

• Microsoft COCO Captions: Data Collection and Evaluation Server
• PTBTokenizer: We use the Stanford Tokenizer which is included in Stanford CoreNLP 3.4.1.
• BLEU: BLEU: a Method for Automatic Evaluation of Machine Translation
• Meteor: Project page with related publications. We use the latest version (1.5) of the Code. Changes have been made to the source code to properly aggreate the statistics for the entire corpus.
• Rouge-L: ROUGE: A Package for Automatic Evaluation of Summaries
• CIDEr: CIDEr: Consensus-based Image Description Evaluation
• SPICE: SPICE: Semantic Propositional Image Caption Evaluation

• Ramakrishna Vedantam (Virgina Tech)
• MSCOCO Caption Evaluation Team (Xinlei Chen (CMU), Hao Fang (University of Washington), Tsung-Yi Lin (Cornell))

If this is helpful for your work, please cite our paper as:

@InProceedings{Wang_2019_CVPR,
  author = {Wang, Qingzhong and Chan, Antoni B.},
  title = {Describing Like Humans: On Diversity in Image Captioning},
  booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  month = {June},
  year = {2019}
  }