This code implements Diverse Beam Search (DBS) - a replacement for beam search that generates diverse sequences from sequence models like LSTMs. This repository lets you generate diverse image-captions for models trained using the popular neuraltalk2 repository. A demo of our implementation on captioning is available at dbs.cloudcv.org

You will need to install torch and the packages

• nn
• nngraph
• image
• loadcaffe
• hdf5 (optional, depending on how you want to input data)

You might want to install torch using this repository. It installs a bunch of the requirements. Additionally, if you are using a GPU you will need to install cutorch and cunn. If the image-captioning checkpoint was trained using cudnn, you will need to download cudnn. First, you will need to download it from NVIDIA's website and add it to your LD_LIBRARY_PATH.

Any of the checkpoints distributed by Andrej Karpathy along with the neuraltalk2 repository can be used with this code. Additionally, you could also train your own model using neuraltalk2 and use this code to sample diverse sentences.

After installing the dependencies, you should be able to obtain diverse captions by:

$ th eval.lua -model /path/to/model.t7 -num_images 1 -image_folder eval_images -gpuid -1

To run a beam search of size 10 with 5 diverse groups and a diversity strength of 0.5 on the same image you would do:

$ th eval.lua -model /path/to/model.t7 -B 10 -M 5 -lambda 0.5 -num_images 1 -image_folder eval_images -gpuid -1

The output of the code will be written to a json file that contains all the generated captions and their scores for each image.

The core of our method is in dbs/beam_utils.lua. It contains two functions that you will need to replicate:

• beam_step - Performs one expansion of the beams held at any given time.
• beam_search - Modifies the log-probabilities of the sequences and calls beam_step at every time step. This handles both division of the beam budget into groups and augmenting scores with diversity.

• neuraltalk2