Distant Supervision for Scene Graph Generation

Data and code for ICCV 2021 paper Distant Supervision for Scene Graph Generation.

Introduction

The paper applies distant supervision to visual relation detection. The intuition of distant supervision is that possible predicates between entity pairs are highly dependent on the entity types. For example, there might be ride on, feed between human and horse in images, but it is less likely to be covering. Thus, we apply this correlation to take advantage of unlabeled data. Given the knowledge base containing possible combinations between entity types and predicates, our framework enables distantly supervised training without using any human-annotated relation data, and semi-supervised training that incorporates both human-labeled data and distantly labeled data. To build the knowledge base, we parse all possible (subject, predicate, object) triplets from Conceptual Caption dataset, resulting in a knowledge base containing 1.9M distinct relational triples.

Code

Thanks to the elegant code from Scene-Graph-Benchmark.pytorch. This project is built on their framework. There are also some differences from their settings. We show the differences in a later section.

The Illustration of Distant Supervision

Installation

Check INSTALL.md for installation instructions.

Dataset

Check DATASET.md for instructions of dataset preprocessing.

Metrics

Our metrics are directly adapted from Scene-Graph-Benchmark.pytorch.

Object Detector

Download Pre-trained Detector

In generally SGG tasks, the detector is pre-trained on the object bounding box annotations on training set. We directly use the pre-trained Faster R-CNN provided by Scene-Graph-Benchmark.pytorch, because our 20 category setting and their 50 category setting have the same training set.

After you download the Faster R-CNN model, please extract all the files to the directory /home/username/checkpoints/pretrained_faster_rcnn. To train your own Faster R-CNN model, please follow the next section.

The above pre-trained Faster R-CNN model achives 38.52/26.35/28.14 mAp on VG train/val/test set respectively.

Pre-train Your Own Detector

In this work, we do not modify the Faster R-CNN part. The training process can be referred to the origin code.

EM Algorithm based Training

All commands of training are saved in the directory cmds/. The directory of cmds looks like:

cmds/  
├── 20 
│   └── motif
│       ├── predcls
│       │   ├── ds \\ distant supervision which is weakly supervised training
│       │   │   ├── em_M_step1.sh
│       │   │   ├── em_E_step2.sh
│       │   │   ├── em_M_step2.sh
│       │   │   ├── em_M_step1_wclip.sh
│       │   │   ├── em_E_step2_wclip.sh
│       │   │   └── em_M_step2_wclip.sh
│       │   ├── semi \\ semi-supervised training 
│       │   │   ├── em_E_step1.sh
│       │   │   ├── em_M_step1.sh
│       │   │   ├── em_E_step2.sh
│       │   │   └── em_M_step2.sh
│       │   └── sup
│       │       ├── train.sh
│       │       └── val.sh
│       │
│       ├── sgcls
│       │   ...
│       │
│       ├── sgdet
│       │   ...

Generally, we use an EM algorithm based training, which means the model is trained iteratively. In E-step, we estimate the predicate label distribution between entity pairs. In M-step, we optimize the model with estimated predicate label distribution. For example, the em_E_step1 means the initialization of predicate label distribution, and in em_M_step1 the model will be optimized on the label estimation.

All checkpoints can be downloaded from MODEL_ZOO.md.

Preparation

Before running the code, you need to specify the current path as environment variable SG and the experiments' root directory as EXP.

# specify current directory as SG, e.g.:
export SG=~/VisualDS
# specify experiment directory, e.g.:
export EXP=~/exps

Weakly Supervised Training

Weakly supervised training can be done with only knowledge base or can also use external semantic signals to train a better model. As for the external semantic signals, we use currently popular CLIP to initialize the probability of possible predicates between entity pairs.

1. w/o CLIP training for Predcls:

# no need for em_E_step1
sh cmds/20/motif/predcls/ds/em_M_step1.sh
sh cmds/20/motif/predcls/ds/em_E_step2.sh
sh cmds/20/motif/predcls/ds/em_M_step2.sh

2. with CLIP training for Predcls:

Before training, please ensure datasets/vg/20/cc_clip_logits.pk is downloaded.

# the em_E_step1 is conducted by CLIP
sh cmds/20/motif/predcls/ds/em_M_step1_wclip.sh
sh cmds/20/motif/predcls/ds/em_E_step2_wclip.sh
sh cmds/20/motif/predcls/ds/em_M_step2_wclip.sh

3. training for Sgcls and Sgdet:

E_step results of Predcls are directly used for Sgcls and Sgdet. Thus, there is no em_E_step.sh for Sgcls and Sgdet.

Semi-Supervised Training

In semi-supervised training, we use supervised model trained with labeled data to estimate predicate labels for entity pairs. So before conduct semi-supervised training, we should conduct a normal supervised training on Predcls task first:

sh cmds/20/motif/predcls/sup/train.sh

Or just download the trained model here, and put it into $EXP/20/predcls/sup/sup.

Noted that, for three tasks Predcls, Sgcls, Sgdet, we all use supervised model of Predcls task to initialize predicate label distributions. After the preparation, we can run:

sh cmds/20/motif/predcls/semi/em_E_step1.sh
sh cmds/20/motif/predcls/semi/em_M_step1.sh
sh cmds/20/motif/predcls/semi/em_E_step2.sh
sh cmds/20/motif/predcls/semi/em_M_step2.sh

Difference from Scene-Graph-Benchmark.pytorch

1. Fix a bug in evaluation.

   We found that in previous evaluation, there are sometimes duplicated triplets in images, e.g. (1-man, ride, 2-horse)*3. We fix this small bug and use only unique triplets. By fixing the bug, the performance of the model will decrease somewhat. For example, the R@100 of predcls task will decrease about 1~3 points.

2. We conduct experiments on 20 categories predicate setting rather than 50 categories.

3. In evaluation, weakly supervised trained model uses logits rather than softmax normalized scores for relation triplets ranking.