Skip to content

Latest commit

 

History

History
108 lines (84 loc) · 5.12 KB

README.md

File metadata and controls

108 lines (84 loc) · 5.12 KB

2D-TAN

we are hiring talented interns: [email protected]

In this paper, we study the problem of moment localization with natural language, and propose a novel 2D Temporal Adjacent Networks(2D-TAN) method. The core idea is to retrieve a moment on a two-dimensional temporal map, which considers adjacent moment candidates as the temporal context. 2D-TAN is capable of encoding adjacent temporal relation, while learning discriminative feature for matching video moments with referring expressions. Our model is simple in design and achieves competitive performance in comparison with the state-of-the-art methods on three benchmark datasets.

Arxiv Preprint

Please check the ms-2d-tan branch for our TPAMI extension.

News

Framework

alt text

Main Results

Main results on Charades-STA

Method [email protected] [email protected] [email protected] [email protected]
Pool 40.94 22.85 83.84 50.35
Conv 42.80 23.25 80.54 54.14

I fixed a bug for loading charades visual features, the updated performance is listed above. Please use these results when comparing with our AAAI paper.

Main results on ActivityNet Captions

Method [email protected] [email protected] [email protected] [email protected] [email protected] [email protected]
Pool 59.45 44.51 26.54 85.53 77.13 61.96
Conv 58.75 44.05 27.38 85.65 76.65 62.26

Main results on TACoS

Method [email protected] [email protected] [email protected] [email protected] [email protected] [email protected]
Pool 47.59 37.29 25.32 70.31 57.81 45.04
Conv 46.39 35.17 25.17 74.46 56.99 44.24

Prerequisites

  • pytorch 1.1.0
  • python 3.7
  • torchtext
  • easydict
  • terminaltables

Quick Start

Please download the visual features from box or dropbox and save it to the data/ folder.

Training

Use the following commands for training:

# Evaluate "Pool" in Table 1
python moment_localization/train.py --cfg experiments/charades/2D-TAN-16x16-K5L8-pool.yaml --verbose
# Evaluate "Conv" in Table 1
python moment_localization/train.py --cfg experiments/charades/2D-TAN-16x16-K5L8-conv.yaml --verbose

# Evaluate "Pool" in Table 2
python moment_localization/train.py --cfg experiments/activitynet/2D-TAN-64x64-K9L4-pool.yaml --verbose
# Evaluate "Conv" in Table 2
python moment_localization/train.py --cfg experiments/activitynet/2D-TAN-64x64-K9L4-conv.yaml --verbose

# Evaluate "Pool" in Table 3
python moment_localization/train.py --cfg experiments/tacos/2D-TAN-128x128-K5L8-pool.yaml --verbose
# Evaluate "Conv" in Table 3
python moment_localization/train.py --cfg experiments/tacos/2D-TAN-128x128-K5L8-conv.yaml --verbose

Testing

Our trained model are provided in box or dropbox. Please download them to the checkpoints folder.

Then, run the following commands for evaluation:

# Evaluate "Pool" in Table 1
python moment_localization/test.py --cfg experiments/charades/2D-TAN-16x16-K5L8-pool.yaml --verbose --split test
# Evaluate "Conv" in Table 1
python moment_localization/test.py --cfg experiments/charades/2D-TAN-16x16-K5L8-conv.yaml --verbose --split test

# Evaluate "Pool" in Table 2
python moment_localization/test.py --cfg experiments/activitynet/2D-TAN-64x64-K9L4-pool.yaml --verbose --split test
# Evaluate "Conv" in Table 2
python moment_localization/test.py --cfg experiments/activitynet/2D-TAN-64x64-K9L4-conv.yaml --verbose --split test

# Evaluate "Pool" in Table 3
python moment_localization/test.py --cfg experiments/tacos/2D-TAN-128x128-K5L8-pool.yaml --verbose --split test
# Evaluate "Conv" in Table 3
python moment_localization/test.py --cfg experiments/tacos/2D-TAN-128x128-K5L8-conv.yaml --verbose --split test

Citation

If any part of our paper and code is helpful to your work, please generously cite with:

@InProceedings{2DTAN_2020_AAAI,
author = {Zhang, Songyang and Peng, Houwen and Fu, Jianlong and Luo, Jiebo},
title = {Learning 2D Temporal Adjacent Networks forMoment Localization with Natural Language},
booktitle = {AAAI},
year = {2020}
}