SSPNG (ACM MM 2023)

The official implementation of the SSPNG paper in PyTorch.

Semi-Supervised Panoptic Narrative Grounding

Semi-Supervised Panoptic Narrative Grounding. Danni Yang, Jiayi Ji*, Xiaoshuai Sun, Haowei Wang, Yinan Li, Yiwei Ma, Rongrong Ji.

News

• [2023-08-3] Code is released.

Abstract

Despite considerable progress, the advancement of Panoptic Narrative Grounding (PNG) remains hindered by costly annotations. In this paper, we introduce a novel Semi-Supervised Panoptic Narrative Grounding (SS-PNG) learning scheme, capitalizing on a smaller set of labeled image-text pairs and a larger set of unlabeled pairs to achieve competitive performance. Unlike visual segmentation tasks, PNG involves one pixel belonging to multiple open-ended nouns. As a result, existing multi-class based semi-supervised segmentation frameworks cannot be directly applied to this task. To address this challenge, we first develop a novel SS-PNG Network (SS-PNG-NW) tailored to the SS-PNG setting. We thoroughly investigate strategies such as Burn-In and data augmentation to determine the optimal generic configuration for the SS-PNG-NW. Additionally, to tackle the issue of imbalanced pseudo-label quality, we propose a Quality-Based Loss Adjustment (QLA) approach to adjust the semi-supervised objective, resulting in an enhanced SS-PNG-NW+. Employing our proposed QLA, we improve BCE Loss and Dice loss at pixel and mask levels, respectively. We conduct extensive experiments on PNG datasets, with our SS-PNG-NW+ demonstrating promising results comparable to fully-supervised models across all data ratios. Remarkably, our SS-PNG-NW+ outperforms fully-supervised models with only 30% and 50% supervision data, exceeding their performance by 0.8% and 1.1% respectively. This highlights the effectiveness of our proposed SS-PNG-NW+ in overcoming the challenges posed by limited annotations and enhancing the applicability of PNG tasks. Source code is available in the supplementary materials.

Installation

Requirements

• Python
• Numpy
• Pytorch 1.10.0
• Tqdm
• Scipy 1.7.3

Dataset Preparation

1. Download the 2017 MSCOCO Dataset from its official webpage. You will need the train and validation splits' images and panoptic segmentations annotations.

2. Download the Panoptic Narrative Grounding Benchmark from the PNG's project webpage. Organize the files as follows:

datasets
|_coco
    |_ train2017
    |_ val2017
    |_ panoptic_stuff_train2017
    |_ panoptic_stuff_val2017
    |_annotations
        |_ png_coco_train2017.json
        |_ png_coco_val2017.json
        |_ panoptic_segmentation
        |  |_ train2017
        |  |_ val2017
        |_ panoptic_train2017.json
        |_ panoptic_val2017.json
        |_ instances_train2017.json

3. Pre-process the Panoptic narrative Grounding Ground-Truth Annotation for the dataloader using utils/pre_process.py.

4. At the end of this step you should have two new files in your annotations folder.

datasets
|_coco
    |_ train2017
    |_ val2017
    |_ panoptic_stuff_train2017
    |_ panoptic_stuff_val2017
    |_annotations
        |_ png_coco_train2017.json
        |_ png_coco_val2017.json
        |_ panoptic_segmentation
        |  |_ train2017
        |  |_ val2017
        |_ panoptic_train2017.json
        |_ panoptic_val2017.json
        |_ instances_train2017.json
        |_ png_coco_train2017_dataloader.json
        |_ png_coco_val2017_dataloader.json

Train and Inference

Pretrained Model

To reproduce all our results as reported in paper, you can use our pretrained model and our source code.

Model	link
FPN	fpn
Bert-base-uncased	bert
SSPNG	sspng

Train

1. Modify the routes in train.sh according to your local paths.
2. Run train.sh

Inference

Run test.sh to test the pretrained model, modify the pretrained model path --ckpt_path.