Sample Efficient Learning of Novel Visual Concepts

This is a PyTorch implementation of the paper "Sample Efficient Learning of Novel Visual Concepts" published in the Conference on Lifelong Learning Agents (CoLLAs), 2023.

Abstract

Despite the advances made in visual object recognition, state-of-the-art deep learning models struggle to effectively recognize novel objects in a few-shot setting where only a limited number of examples are provided. Unlike humans who excel at such tasks, these models often fail to leverage known relationships between entities in order to draw conclusions about such objects. In this work, we show that incorporating a symbolic knowledge graph into a state-of-the-art recognition model enables a new approach for effective few-shot classification. In our proposed neuro-symbolic architecture and training methodology, the knowledge graph is augmented with additional relationships extracted from a small set of examples, improving its ability to recognize novel objects by considering the presence of interconnected entities. Unlike existing few-shot classifiers, we show that this enables our model to incorporate not only objects but also abstract concepts and affordances. The existence of the knowledge graph also makes this approach amenable to interpretability through analysis of the relationships contained within it. We empirically show that our approach outperforms current state-of-the-art few-shot multi-label classification methods on the COCO dataset and evaluate the addition of abstract concepts and affordances on the Visual Genome dataset.

Approach

Citation

In case you find our work useful, consider citing:

@article{Bhagat2023SampleEfficientLO,
  title={Sample-Efficient Learning of Novel Visual Concepts},
  author={Sarthak Bhagat and Simon Stepputtis and Joseph Campbell and Katia P. Sycara},
  journal={ArXiv},
  year={2023},
  volume={abs/2306.09482}
}

Index

1. Environment Setup
2. Dataset Preparation
3. Training our Model
4. Evaluating our Model
5. License

Setup

In order to build a conda environment for running our model, run the following command:

conda env create -f environment.yml

Activate environment using:

conda activate concept_learning

Dataset Preparation

• Download the images for Visual Genome dataset.
  
• Download the knowledge graph generated from Visual Genome and the augmented with WordNet dataset from this link.
  
• Download the GloVe pretrained embeddings from this link.
• We also need to prepare the .pth files for training our model. For this we need to create a directory called filtered_data_train and filtered_data_test in the source directory.
  Make sure you add only samples corresponding to the known concepts in the filtered_data_train and the images corresponding to the novel concepts can be placed in filtered_data_test.
  In each folder, we need to place all the .pth files where each sample is a dictionary with the following keys:
  
  • name: Name of the image corresponding to the sample.
    
  • present: Ground truth labels for the sample.
  • detections: Detections from Faster R-CNN. Each detection is represented as a dictionary which consists of the following keys:
    • class_ind: Detection indices (0-80) for classes detected by Faster R-CNN
    • conf: Confidence of predictions (0-1) from Faster R-CNN

Training

Make sure you add your Visual Genome dataset location in the arguments file.
To train our modified GSNN model, simply run the following command.

python train.py --ARGS argument_value

In order to train after removing certain classes from the dataset, you could remove it from the data folder and pass the argument --node_addition along with the right set of test_concepts.

To train the edge prediction model, run the following script:

python train_edge_prediction.py --ARGS argument_value

Make sure you use a lower learning rate compared to the modified GSNN training.

In order to add novel nodes to the model, create a directory called novel_class_images_1 in the source directory.
Add all the images, provided by the SME corresponding to the novel class, in the directory novel_class_images_1/novel_class_name in this folder.
Make sure the image names denote the same ID as the corresponding .pth file. That is, the image in 100.pth should be named 100.jpg.
Also, you should keep the name of the directory where the weights are saved, i.e. exp_name, the same as that of the trained GSNN model.
One alternative is that you use as both the arguments exp_name and load_exp_name the exp_name that you used when running train.py. Following that, run this command to finetune the GSNN models to include the provided novel classes:

python finetune.py --ARGS argument_value

Evaluation

To evaluate the finetuned model, simply run the previous command with the argument --evaluate_fine.

You could also evaluate the trained model to obtain the predicted concepts in each scene, in the following manner:

python evaluate_single_example.py

License

Copyright (c) 2023 Sarthak Bhagat, Simon Stepputtis, Joseph Campbell, Katia Sycara

For license information, see the license.