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

Supplemental Code for "ImpressionNet :A Multi view Approach to Predict Socio Facial Impressions"

(Preprint) Official PyTorch implementation of "How Do Vision Transformers Work?"

PyMatting: A Python Library for Alpha Matting

Keras code and weights files for popular deep learning models.

Add gui for YoloV5 using PyQt5

WHENet: Real-time Fine-Grained Estimation for Wide Range Head Pose

TensorFlow CNN for fast style transfer

Implementation of SwinTransformerV2 in TensorFlow.

VACA: Designing Variational Graph Autoencoders for Interventional and Counterfactual Queries

Caffe: a fast open framework for deep learning.

Code for "LoRA: Low-Rank Adaptation of Large Language Models"

Deep Learning Emotion decoding using EEG data from Autism individuals

PyTorch code for our paper "Attention in Attention Network for Image Super-Resolution"

for a paper about leveraging discourse markers for training new models

Code for the CVPR2021 paper "Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition"

A face dataset generator with out-of-focus blur detection and dynamic interval adjustment.

This repository is the official implementation of Unleashing the Power of Contrastive Self-Supervised Visual Models via Contrast-Regularized Fine-Tuning (NeurIPS21).

Pytorch implementation for "Adversarial Robustness under Long-Tailed Distribution" (CVPR 2021 Oral)

Offical implementation of Shunted Self-Attention via Multi-Scale Token Aggregation

Use AI to generate a optimized stock portfolio