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

Related tags

Deep Learningconnect-caption-and-trace
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).

example results

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.

Owner
Meta Research
Meta Research
GitHub Repository
Code for AutoNL on ImageNet (CVPR2020)

Neural Architecture Search for Lightweight Non-Local Networks This repository contains the code for CVPR 2020 paper Neural Architecture Search for Lig

Yingwei Li 104 Aug 31, 2022
Self-Supervised Monocular DepthEstimation with Internal Feature Fusion(arXiv), BMVC2021

DIFFNet This repo is for Self-Supervised Monocular Depth Estimation with Internal Feature Fusion(arXiv), BMVC2021 A new backbone for self-supervised d

Hang 94 Dec 25, 2022
Bottom-up attention model for image captioning and VQA, based on Faster R-CNN and Visual Genome

bottom-up-attention This code implements a bottom-up attention model, based on multi-gpu training of Faster R-CNN with ResNet-101, using object and at

Peter Anderson 1.3k Jan 09, 2023
CNN Based Meta-Learning for Noisy Image Classification and Template Matching

CNN Based Meta-Learning for Noisy Image Classification and Template Matching Introduction This master thesis used a few-shot meta learning approach to

Kumar Manas 2 Dec 09, 2021
Prototype for Baby Action Detection and Classification

Baby Action Detection Table of Contents About Install Run Predictions Demo About An attempt to harness the power of Deep Learning to come up with a so

Shreyas K 30 Dec 16, 2022
imbalanced-DL: Deep Imbalanced Learning in Python

imbalanced-DL: Deep Imbalanced Learning in Python Overview imbalanced-DL (imported as imbalanceddl) is a Python package designed to make deep imbalanc

NTUCSIE CLLab 19 Dec 28, 2022
Predicting path with preference based on user demonstration using Maximum Entropy Deep Inverse Reinforcement Learning in a continuous environment

Preference-Planning-Deep-IRL Introduction Check my portfolio post Dependencies Gym stable-baselines3 PyTorch Usage Take Demonstration python3 record.

Tianyu Li 9 Oct 26, 2022
Ratatoskr: Worcester Tech's conference scheduling system

Ratatoskr: Worcester Tech's conference scheduling system In Norse mythology, Ratatoskr is a squirrel who runs up and down the world tree Yggdrasil to

4 Dec 22, 2022
In this project, we create and implement a deep learning library from scratch.

ARA In this project, we create and implement a deep learning library from scratch. Table of Contents Deep Leaning Library Table of Contents About The

22 Aug 23, 2022
Setup and customize deep learning environment in seconds.

Deepo is a series of Docker images that allows you to quickly set up your deep learning research environment supports almost all commonly used deep le

Ming 6.3k Jan 06, 2023
I decide to sync up this repo and self-critical.pytorch. (The old master is in old master branch for archive)

An Image Captioning codebase This is a codebase for image captioning research. It supports: Self critical training from Self-critical Sequence Trainin

Ruotian(RT) Luo 1.3k Dec 31, 2022
Equivariant GNN for the prediction of atomic multipoles up to quadrupoles.

Equivariant Graph Neural Network for Atomic Multipoles Description Repository for the Model used in the publication 'Learning Atomic Multipoles: Predi

16 Nov 22, 2022
Dataset VSD4K includes 6 popular categories: game, sport, dance, vlog, interview and city.

CaFM-pytorch ICCV ACCEPT Introduction of dataset VSD4K Our dataset VSD4K includes 6 popular categories: game, sport, dance, vlog, interview and city.

96 Jul 05, 2022
Malware Env for OpenAI Gym

Malware Env for OpenAI Gym Citing If you use this code in a publication please cite the following paper: Hyrum S. Anderson, Anant Kharkar, Bobby Fila

ENDGAME 563 Dec 29, 2022
Implementation of Graph Convolutional Networks in TensorFlow

Graph Convolutional Networks This is a TensorFlow implementation of Graph Convolutional Networks for the task of (semi-supervised) classification of n

Thomas Kipf 6.6k Dec 30, 2022
[ACMMM 2021 Oral] Enhanced Invertible Encoding for Learned Image Compression

InvCompress Official Pytorch Implementation for "Enhanced Invertible Encoding for Learned Image Compression", ACMMM 2021 (Oral) Figure: Our framework

96 Nov 30, 2022
Delta Conformity Sociopatterns Analysis - Delta Conformity Sociopatterns Analysis

Delta_Conformity_Sociopatterns_Analysis ∆-Conformity is a local homophily measur

2 Jan 09, 2022
Coursera - Quiz & Assignment of Coursera

Coursera Assignments This repository is aimed to help Coursera learners who have difficulties in their learning process. The quiz and programming home

浅梦 828 Jan 04, 2023
Shape Matching of Real 3D Object Data to Synthetic 3D CADs (3DV project @ ETHZ)

Real2CAD-3DV Shape Matching of Real 3D Object Data to Synthetic 3D CADs (3DV project @ ETHZ) Group Member: Yue Pan, Yuanwen Yue, Bingxin Ke, Yujie He

24 Jun 22, 2022
Technical experimentations to beat the stock market using deep learning :chart_with_upwards_trend:

DeepStock Technical experimentations to beat the stock market using deep learning. Experimentations Deep Learning Stock Prediction with Daily News Hea

Keon 449 Dec 29, 2022