Summary of Robust Visual Representation Learning with Multi-modal Prior Knowledge For Image Classification Under Distribution Shift, by Hongkuan Zhou et al.
Robust Visual Representation Learning with Multi-modal Prior Knowledge for Image Classification Under Distribution Shift
by Hongkuan Zhou, Lavdim Halilaj, Sebastian Monka, Stefan Schmid, Yuqicheng Zhu, Bo Xiong, Steffen Staab
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to improve generalization of deep neural networks (DNNs) under distribution shifts between training and testing data. It introduces Knowledge-Guided Visual representation learning (KGV), which integrates prior knowledge from two modalities: a knowledge graph (KG) and generated synthetic images. The embeddings from these modalities are aligned in a common latent space using a novel translation-based method, enabling more regularized learning of image representations. The proposed approach, KGV, is evaluated on various image classification tasks with distribution shifts, showing improved accuracy and data efficiency across all experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps deep neural networks (DNNs) work better when the data changes between training and testing. It suggests a new way to learn called Knowledge-Guided Visual representation learning (KGV). KGV uses two types of information: a big list of things we know about the world, like relationships between words, and fake images that look like real ones. These different pieces of information are connected in a special space, making it easier for DNNs to learn how to recognize pictures. The paper tests this new approach on several tasks with changed data, showing it works better than usual. |
Keywords
» Artificial intelligence » Generalization » Image classification » Knowledge graph » Latent space » Representation learning » Translation
