HAT

CVPR 2024 "Unifying Top-down and Bottom-up Scanpath Prediction Using Transformers"

Installation

• Install Detectron2
• Install MSDeformableAttn:

  cd ./hat/pixel_decoder/ops
  sh make.sh
  

• Download pretrained model weights (ResNet-50 and Deformable Transformer) with the following python code

   if not os.path.exists("./pretrained_models/"):
       os.mkdir('./pretrained_models')
  
   print('downloading pretrained model weights...')
   url = f"http://vision.cs.stonybrook.edu/~cvlab_download/HAT/pretrained_models/M2F_R50_MSDeformAttnPixelDecoder.pkl"
   wget.download(url, 'pretrained_models/')
   url = f"http://vision.cs.stonybrook.edu/~cvlab_download/HAT/pretrained_models/M2F_R50.pkl"
   wget.download(url, 'pretrained_models/')
  

• Dataset: https://sites.google.com/view/cocosearch/coco-freeview
• Prepare the data following https://github.com/cvlab-stonybrook/Scanpath_Prediction.

Try out the demo code to generate a scanpath for your test image!

Commands

• Train a model on COCO-Search18 with

  python train.py --hparams ./configs/coco_search18_dense_SSL.json --dataset-root <dataset_root> 
  

• Steps to train HAT on your custom dataset:

1. Modify the configuration file: Update the values for Data.name, Data.TAP, and Data.max_traj_length in the config file to match your dataset's specifications.
2. Create a fixation file: Generate a fixation.json file in the same format as coco_freeview_fixations_all.json. If your dataset doesn't have specific image categories, you can set the "task" to "none". One important note: HAT assumes that the subject values are continuous integers starting from 1. Please ensure this if you plan to conduct experiments related to subjects.
3. Load your dataset: Refer to the implementations for loading OSIE and MIT1003 in hat/builder.py, common/dataset.py, and common/data.py as a guide to integrate your own dataset.
4. Sequence Score calculation: We will soon release the code for computing cluster.npy, which is required for calculating the Sequence Score. Currently, Sequence Score and Semantic Sequence Score are only supported for COCO-Search18 and COCO-Freeview datasets. For now, skip this calculation during evaluation.

Reference

This repository contains code for scanpath prediction models for the following papers. Please cite if you use this code base.

@InProceedings{yang2024unify,
  author = {Yang, Zhibo and Mondal, Sounak and Ahn, Seoyoung and Xue, Ruoyu and Zelinsky, Gregory and Hoai, Minh and Samaras, Dimitris},
  title = {Unifying Top-down and Bottom-up Scanpath Prediction Using Transformers},
  booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  month = {June},
  year = {2024}
}