Rohrbach*, Anna and Hendricks*, Lisa Anne, et al. "Object Hallucination in Image Captioning." EMNLP (2018).
Find the paper here.
@inproceedings{objectHallucination,
title = {Object Hallucination in Image Captioning.},
author = {Rohrbach, Anna and Hendricks, Lisa Anne and Burns, Kaylee, and Darrell, Trevor, and Saenko, Kate},
booktitle = {Empirical Methods in Natural Language Processing (EMNLP)},
year = {2018}
}
License: BSD 2-Clause license
Getting Started
Run setup.sh to download generated sentences used for our analysis. Additionally you will need MSCOCO annotations (both the instance segmentations and ground truth captions). If you do not already have them, they can be downloaded here. You can see other python requirements in requirements.txt.
Replicating Results
After running setup.sh you should be able to replicate results in our paper by running table1.py, table2.py, table3.py, table4.py and figure6.py (example usage python table1.py --annotation_path PATH_TO_COCO_ANNOTATIONS where coco/annotations is the default for --annotation_path).
Our scripts call on utils/chair.py to compute the CHAIR metric. See below for more details on utils/chair.py.
If you would like to run figure4.py (language and image model consistency) you will need to download some intermediate features. Please see the Language and Image Model Consistency section below.
For reproducing our results on correlation with human scores, run python table5.py. The file with images IDs used in the human evaluation, as well as the average human scores for each of the compared models, will be found in data/human_scores, after running the setup.sh.
Evaluating CHAIR
See utils/chair.py to understand how we compute the CHAIRs and CHAIRi metrics. Evaluate generated sentences by inputting a path to the generated sentences as well as the path which includes coco annotations.
Example usage is:
python utils/chair.py --cap_file generated_sentences/fc_beam5_test.json --annotation_path coco
where cap_file corresponds to a json file with your generated captions and annotation_path points to where MSCOCO annotations are stored.
We expect generated sentences to be stored as a dictionary with the following keys:
- overall: metrics from the COCO evaluation toolkit computed over the entire dataset.
- imgToEval: a dictionary with keys corresponding to image ids and values with a caption, image_id, and sentence metrics for the particular caption.
Note that this is the format of the captions output by the open sourced code here, which we used to replicate most of the models presented in the paper.
Language and Image Model Consistency
To compute language and image consistency, we trained a classifier to predict class labels given an image and a language model to predict the next word in a sentence given all previous words in a sentence.
You can access the labels predicted by our language model in output/image_classifier and the words predicted by our language model here.
To run our code, you ned to first download the zip file into the main directory and unzip.
Once you have these intermediate features you can look at utils/lm_consistency.py and utils/im_consistency.py to understand how these metrics are computed.
Running figure4.py will output the results from our paper (constructing the actual bar plot is left as an exercise to the reader).
Human Eval
Replicate the results from our human evaluation by running python table5.py. Raw human evaluation scores can be found in data/human_scores after running setup.sh.
Captioning Models
We generated sentences for the majority of models by training open source models available here. Within this framework, we wrote code for the LRCN model as well as the topdown deconstructed models (Table 3 in the paper). This code is available upon request. For the top down model with bounding boxes, we used the code here. For the Neural Baby Talk model, we used the code here. For the GAN based model, we used the sentences from the paper here. Sentences were obtained directly from the author (we did not train the GAN model).
