A unified framework to jointly model images, text, and human attention traces.

Last update: Oct 24, 2022

Related tags

Overview

connect-caption-and-trace

This repository contains the reference code for our paper Connecting What to Say With Where to Look by Modeling Human Attention Traces (CVPR2021).

Requirements

Python 3
PyTorch 1.5+ (along with torchvision)
coco-caption (Remember to follow initialization steps in coco-caption/README.md)

Prepare data

Our experiments cover all four datasets included in Localized Narratives: COCO2017, Flickr30k, Open Images and ADE20k. For each dataset, we need four things: (1) json file containing image info and word tokens. (DATASET_LN.json) (2) h5 file containing caption labels (DATASET_LN_label.h5) (3) The trace labels extracted from Localized Narratives (DATASET_LN_trace_box/) (4) json file for coco-caption evaluation (captions_DATASET_LN_test.json) (5) Image features (with bounding boxes) extracted by a Mask-RCNN pretrained on Visual Genome.

You can download (1--4) from here: (make a folder named data and put (1--3) in it, and put (4) under coco-caption/annotaions/)

To get (5), you can use Detectron2. First, install Detectron2, then follow Prepare COCO-style annotations for Visual Genome (We use the pre-trained Resnet101-C4 model provided there). After that you can utilize tools/extract_feats.py in Detectron2 to extract features. Finally, run scripts/prepare_feats_boxes_from_npz.py in this repo to prepare features and bounding boxes in seperate folders for training.

For COCO dataest you can also directly use the features provided by Peter Anderson here. The performance is almost the same (with around 0.2% difference.)

Training

The dataset can be chosen from the four datasets. The --task can be chosen from trace, caption, c_joint_t and pred_both. The --eval_task can be chosen from trace, caption, and pred_both.

COCO: joint training of controlled caption generation and trace generation (N=2 layers, evaluated on caption generation)

python tools/train.py --language_eval 0 --id transformer_LN_coco  --caption_model transformer --input_json data/coco_LN.json --input_att_dir Dir_to_image_features_vg --input_box_dir Dir_to_bounding_boxes_vg --input_label_h5 data/coco_LN_label.h5 --batch_size 30 --learning_rate 5e-4 --learning_rate_decay_start 0 --scheduled_sampling_start 100 --learning_rate_decay_every 3  --save_checkpoint_every 1000 --max_epochs 30 --max_length 225 --seq_per_img 1 --use_box 1   --use_trace 1  --input_trace_dir data/coco_LN_trace_box --use_trace_feat 0 --beam_size 1 --val_images_use -1 --num_layers 2 --task c_joint_t --eval_task caption --dataset_choice=coco

Open image: training of generating caption and trace at the same time (N=1 layers, evaluated on predicting both)

python tools/train.py --language_eval 0 --id transformer_LN_openimg  --caption_model transformer --input_json data/openimg_LN.json --input_att_dir Dir_to_image_features_vg --input_box_dir Dir_to_bounding_boxes_vg --input_label_h5 data/openimg_LN_label.h5 --batch_size 30 --learning_rate 5e-4 --learning_rate_decay_start 0 --scheduled_sampling_start 100 --learning_rate_decay_every 3  --save_checkpoint_every 1000 --max_epochs 30 --max_length 225 --seq_per_img 1 --use_box 1   --use_trace 1  --input_trace_dir data/openimg_LN_trace_box --use_trace_feat 0 --beam_size 1 --val_images_use -1 --num_layers 1 --task pred_both --eval_task pred_both --dataset_choice=openimg

Flickr30k: training of controlled caption generation alone (N=1 layer)

python tools/train.py --language_eval 0 --id transformer_LN_flk30k  --caption_model transformer --input_json data/flk30k_LN.json --input_att_dir Dir_to_image_features_vg --input_box_dir Dir_to_bounding_boxes_vg --input_label_h5 data/flk30k_LN_label.h5 --batch_size 30 --learning_rate 5e-4 --learning_rate_decay_start 0 --scheduled_sampling_start 100 --learning_rate_decay_every 3  --save_checkpoint_every 1000 --max_epochs 30 --max_length 225 --seq_per_img 1 --use_box 1   --use_trace 1  --input_trace_dir data/flk30k_LN_trace_box --use_trace_feat 0 --beam_size 1 --val_images_use -1 --num_layers 1 --task caption --eval_task caption --dataset_choice=flk30k

ADE20k: training of controlled trace generation alone (N=1 layer)

python tools/train.py --language_eval 0 --id transformer_LN_ade20k  --caption_model transformer --input_json data/ade20k_LN.json --input_att_dir Dir_to_image_features_vg --input_box_dir Dir_to_bounding_boxes_vg --input_label_h5 data/ade20k_LN_label.h5 --batch_size 30 --learning_rate 5e-4 --learning_rate_decay_start 0 --scheduled_sampling_start 100 --learning_rate_decay_every 3  --save_checkpoint_every 1000 --max_epochs 30 --max_length 225 --seq_per_img 1 --use_box 1   --use_trace 1  --input_trace_dir data/ade20k_LN_trace_box --use_trace_feat 0 --beam_size 1 --val_images_use -1 --num_layers 1 --task trace --eval_task trace --dataset_choice=ade20k

Evaluating

COCO: joint training of controlled caption generation and trace generation (N=2 layers, evaluated on caption generation)

python tools/train.py --language_eval 1 --id transformer_LN_coco  --caption_model transformer --input_json data/coco_LN.json --input_att_dir Dir_to_image_features_vg --input_box_dir Dir_to_bounding_boxes_vg --input_label_h5 data/coco_LN_label.h5 --batch_size 2 --learning_rate 5e-4 --learning_rate_decay_start 0 --scheduled_sampling_start 100 --learning_rate_decay_every 3  --save_checkpoint_every 1000 --max_epochs 30 --max_length 225 --seq_per_img 1 --use_box 1   --use_trace 1  --input_trace_dir data/coco_LN_trace_box --use_trace_feat 0 --beam_size 5 --val_images_use -1 --num_layers 2 --task c_joint_t --eval_task caption --dataset_choice=coco

COCO: joint training of controlled caption generation and trace generation (N=2 layers, evaluated on trace generation)

python tools/train.py --language_eval 1 --id transformer_LN_coco  --caption_model transformer --input_json data/coco_LN.json --input_att_dir Dir_to_image_features_vg --input_box_dir Dir_to_bounding_boxes_vg --input_label_h5 data/coco_LN_label.h5 --batch_size 30 --learning_rate 5e-4 --learning_rate_decay_start 0 --scheduled_sampling_start 100 --learning_rate_decay_every 3  --save_checkpoint_every 1000 --max_epochs 30 --max_length 225 --seq_per_img 1 --use_box 1   --use_trace 1  --input_trace_dir data/coco_LN_trace_box --use_trace_feat 0 --beam_size 1 --val_images_use -1 --num_layers 2 --task c_joint_t --eval_task trace --dataset_choice=coco

Open image: training of generating caption and trace at the same time (N=1 layers, evaluated on predicting both)

python tools/train.py --language_eval 1 --id transformer_LN_openimg  --caption_model transformer --input_json data/openimg_LN.json --input_att_dir Dir_to_image_features_vg --input_box_dir Dir_to_bounding_boxes_vg --input_label_h5 data/openimg_LN_label.h5 --batch_size 2 --learning_rate 5e-4 --learning_rate_decay_start 0 --scheduled_sampling_start 100 --learning_rate_decay_every 3  --save_checkpoint_every 1000 --max_epochs 30 --max_length 225 --seq_per_img 1 --use_box 1   --use_trace 1  --input_trace_dir data/openimg_LN_trace_box --use_trace_feat 0 --beam_size 5 --val_images_use -1 --num_layers 1 --task pred_both --eval_task pred_both --dataset_choice=openimg

Acknowledgements

Some components of this repo were built from Ruotian Luo's ImageCaptioning.pytorch.

A unified framework to jointly model images, text, and human attention traces.

Related tags

Overview

connect-caption-and-trace

Requirements

Prepare data

Training

COCO: joint training of controlled caption generation and trace generation (N=2 layers, evaluated on caption generation)

Open image: training of generating caption and trace at the same time (N=1 layers, evaluated on predicting both)

Flickr30k: training of controlled caption generation alone (N=1 layer)

ADE20k: training of controlled trace generation alone (N=1 layer)

Evaluating

COCO: joint training of controlled caption generation and trace generation (N=2 layers, evaluated on caption generation)

COCO: joint training of controlled caption generation and trace generation (N=2 layers, evaluated on trace generation)

Open image: training of generating caption and trace at the same time (N=1 layers, evaluated on predicting both)

Acknowledgements

Owner

Meta Research

Leveraging OpenAI's Codex to solve cornerstone problems in Music

Lipstick ain't enough: Beyond Color-Matching for In-the-Wild Makeup Transfer (CVPR 2021)

Fewshot-face-translation-GAN - Generative adversarial networks integrating modules from FUNIT and SPADE for face-swapping.

Unofficial pytorch implementation of the paper "Dynamic High-Pass Filtering and Multi-Spectral Attention for Image Super-Resolution"

Open-source codebase for EfficientZero, from "Mastering Atari Games with Limited Data" at NeurIPS 2021.

PyKale is a PyTorch library for multimodal learning and transfer learning as well as deep learning and dimensionality reduction on graphs, images, texts, and videos

Subnet Replacement Attack: Towards Practical Deployment-Stage Backdoor Attack on Deep Neural Networks

Python Tensorflow 2 scripts for detecting objects of any class in an image without knowing their label.

This code provides a PyTorch implementation for OTTER (Optimal Transport distillation for Efficient zero-shot Recognition), as described in the paper.

Official Repo of my work for SREC Nandyal Machine Learning Bootcamp

Human Detection - Pedestrian Detection using OpenCV Python

This repository is an implementation of our NeurIPS 2021 paper (Stylized Dialogue Generation with Multi-Pass Dual Learning) in PyTorch.

EMNLP 2021: Single-dataset Experts for Multi-dataset Question-Answering

The official implementation of Theme Transformer

Anomaly Localization in Model Gradients Under Backdoor Attacks Against Federated Learning

A semismooth Newton method for elliptic PDE-constrained optimization

Malware Env for OpenAI Gym

Code repo for "FASA: Feature Augmentation and Sampling Adaptation for Long-Tailed Instance Segmentation" (ICCV 2021)

Smart edu-autobooking - Johnson @ DMI-UNICT study room self-booking system

This is the codebase for the ICLR 2021 paper Trajectory Prediction using Equivariant Continuous Convolution