- This is a PyTorch implementation of Multimodal Grounded Situation Recognition with Transformers.
- MGSRTR (Multimodal Grounded Situation Recognition Transformer) achieves superior performance in all evaluation metrics on the the multimodal datasets generated from SWiG and Flickr30k entities.
- This repository contains instructions, and code for MGSRTR.
A multimodal grounded situation recognition system receives a pair of inputs consisting of a single image and a caption describing the activity happening on that image and outputs a verb, a set of semantic roles, and groundings of the entities on the image. Semantic roles of the objects in the image describe how they participate in the activity described by the verb. Multimodal GSR will predict the verb first and depending on the verb it will predict the nouns from a fixed set of semantic roles for each verb and finally the groundings on the image against each noun. We presented an MGSRTR model leveraging the attention mechanism that uses the input from this joint representation module to solve the MGSR task that we defined.
MGSRTR mainly consists of two components: Transformer Encoder for verb prediction, and Transformer Decoder for grounded noun prediction and an additional joint representation module to encode features from both modali- ties using BERT decoder layer output to encode features from both modalities into a single vector.
git clone https://github.com/06rajesh/multimodal-gsr
cd multimodal-gsr
# (Create a conda environment, activate the environment and install PyTorch via conda)
conda create --name mgsrtr python=3.9
conda activate mgsrtr
pip install -r requirements.txt
# copy the sample env file and set the desired parameters for training or inference
cp .env.sample .env
We used two different datasets with generated captions / frames to train and test our model.
SWiG captions dataset where the captions are generated from the original SWiG dataset output frames. SWiG images can be downloaded from here. Generated captions can be downloaded from here.
Extract the downloaded images and captions in a directory named SWiG like the following structure.
├── ...
├── SWiG # SWiG Dataset root directory
│ ├── images_512 # all the SWiG images under this folder
│ ├── combined_jsons # all the json files extracted from the generated captions zip
└── ...
Flickr30k Frames datasets where the frames were generated using the flickr30k entities captions and frame entities were grounded using the grounding annotations. Flickr30k images can be obtained from here. Generated frames can be obtained from here.
Extract the downloaded images and json in a directory named flicker30k like the following structure.
├── ...
├── flicker30k # Flickr30k frames Dataset root directory
│ ├── flicker30k-images # all the flickr30k images under this folder
│ ├── flickr30k-jsons # all the json files extracted from zip will be placed here
└── ...
Our training supports setting the training parameters using a .env file and read the parameters
from file. Currently, it supports two DATASET: swig or flicker30k. This code supports training
of four different types of model (MODEL_TYPE). mgsrtr, duel_enc_gsr, t5_mgsrtr, and gsrtr.
Rest of the environment params are self-explanatory.
Sample .env file as follows:
DATASET=flicker30k
DEVICE=cpu
NUM_WORKERS=4
DATASET_PATH=./flicker30k
RESUME=False
START_EPOCH=0
VERSION=v6
MODEL_TYPE=mgsrtr
After setting the environment variables just run the following command.
python main.py
Model checkpoints will be provided in near future.
Our code is modified and adapted from these amazing repositories:
Rajesh Baidya (rajeshbaidya.006@gmail.com)