Zero-shot Recognition via Semantic Embeddings and Knowledge Graphs (1803.08035v2)

Abstract: We consider the problem of zero-shot recognition: learning a visual classifier for a category with zero training examples, just using the word embedding of the category and its relationship to other categories, which visual data are provided. The key to dealing with the unfamiliar or novel category is to transfer knowledge obtained from familiar classes to describe the unfamiliar class. In this paper, we build upon the recently introduced Graph Convolutional Network (GCN) and propose an approach that uses both semantic embeddings and the categorical relationships to predict the classifiers. Given a learned knowledge graph (KG), our approach takes as input semantic embeddings for each node (representing visual category). After a series of graph convolutions, we predict the visual classifier for each category. During training, the visual classifiers for a few categories are given to learn the GCN parameters. At test time, these filters are used to predict the visual classifiers of unseen categories. We show that our approach is robust to noise in the KG. More importantly, our approach provides significant improvement in performance compared to the current state-of-the-art results (from 2 ~ 3% on some metrics to whopping 20% on a few).

• The paper introduces an integrated framework using GCNs with knowledge graphs to transfer visual classifiers from seen to unseen categories.
• It employs a six-layer GCN to capture semantic relationships, significantly improving top-k accuracy on datasets such as ImageNet.
• Experimental results show up to a 20.9% enhancement in zero-shot tasks, highlighting the method's scalability and robust performance.

Zero-shot Recognition via Semantic Embeddings and Knowledge Graphs

The paper under review addresses the challenge of zero-shot recognition by proposing a novel framework that leverages both semantic embeddings and knowledge graphs (KGs). This approach aims to predict visual classifiers for categories without training examples, an eminent issue in contemporary computer vision and machine learning.

Key Contributions

The primary contribution of this research is the integration of Graph Convolutional Networks (GCNs) with knowledge graphs to facilitate zero-shot learning. The authors effectively use semantic embeddings, such as word vectors, to inform the classifier for unseen categories, while the KG provides explicit relational data between familiar and novel classes.

1. Framework Design: The proposed framework constructs a knowledge graph where nodes represent semantic categories, and edges denote relationships between these categories. GCNs are employed to propagate information through this graph, allowing the transfer of knowledge from seen to unseen categories.
2. Graph Convolutional Network: The GCN is a critical component of this approach, comprising six layers that enable deep convolutional operations over graph-structured data. The network captures complex relationships within the KG, enhancing its ability to accurately infer classifiers for novel categories from the embeddings and relationships of known categories.
3. Experimentation and Results: The paper demonstrates the efficacy of the proposed method across different datasets, including NEIL and ImageNet. Notably, the model yields significant improvements in top-k accuracy metrics over existing state-of-the-art methods, showcasing up to a 20.9% enhancement in certain zero-shot recognition tasks.

Numerical and Empirical Insights

The authors report robust performance with top-1 accuracy improvements of 3.6% to 18.7% over baseline methods. Testing on ImageNet datasets reveals that the GCN-informed approach efficiently handles up to 30,000 classes using only pre-trained word embeddings as input.

Beyond numerical performance, the framework appears resilient to graph noise, indicating practical robustness in real-world scenarios where KGs may not be pristine. The impact of the knowledge graph's size and the architecture's depth is also meticulously analyzed, revealing that deeper GCNs consistently outperform shallower counterparts.

Implications and Future Directions

Practically, this work supports scalable deployment of visual recognition systems that can generalize beyond their training set. Theoretically, it advances the discourse on integrating structured information into machine learning models, highlighting the potential of GCNs as a powerful tool in zero-shot learning.

Future research could explore the refinement of knowledge graphs, potentially reducing noise and enhancing relational data quality. Additionally, the exploration of alternative semantic embeddings and their influence on zero-shot tasks promises further optimization.

In summary, this paper provides a substantial advancement in zero-shot recognition, offering a comprehensive approach that marries semantic understanding with relational inference. The findings encourage ongoing exploration into how structured knowledge can enhance learning models, especially in scenarios with limited direct training data.