ira's Introduction

Interclass-Relativity-Adaptive Metric Learning for Cross-Modal Matching and Beyond

Pytorch implementation for the paper: Interclass-Relativity-Adaptive Metric Learning for Cross-Modal Matching and Beyond, IEEE Transactions on Multimedia.

We provide the source codes built on SCAN. IRA loss can likewise be evaluated on other benchmarks.

Environment Requirement

The code has been tested running under Python 3.5.2. The required packages are as follows:

Pytorch 1.4.0
numpy 1.15.4

Data

Download data following the instructions here.

We also provide our pretrained checkpoints and log files here.

Training

MS-COCO dataset

python train.py --data_path ./data --data_name coco_precomp --vocab_path ./vocab --logger_name "$LOG_PATH" --model_name "$SAVE_PATH" --max_violation --bi_gru --margin 0.1 --alpha 4 --beta 1

Flickr30K dataset

python train.py --data_path ./data --data_name f30k_precomp --vocab_path ./vocab --logger_name "$LOG_PATH" --model_name "$SAVE_PATH" --max_violation --bi_gru --margin 0.1 --alpha 3 --beta 1

Evaluation

MS-COCO dataset

from vocab import Vocabulary
import evaluation
evaluation.evalrank("$SAVE_PATH/model_best.pth.tar", data_path="$DATA_PATH", split="testall", fold5=True)

Flickr30K dataset

from vocab import Vocabulary
import evaluation
evaluation.evalrank("$SAVE_PATH/model_best.pth.tar", data_path="$DATA_PATH", split="test")

Reference

If you found this code useful, please cite the following paper:

@article{DBLP:journals/tmm/ChenSZXS21,
  author    = {Feiyu Chen and Jie Shao and Yonghui Zhang and Xing Xu and Heng Tao Shen},
  title     = {Interclass-Relativity-Adaptive Metric Learning for Cross-Modal Matching and Beyond},
  journal   = {{IEEE} Trans. Multim.},
  volume    = {23},
  pages     = {3073--3084},
  year      = {2021},
}

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