Summary of Learnable Expansion Of Graph Operators For Multi-modal Feature Fusion, by Dexuan Ding et al.
Learnable Expansion of Graph Operators for Multi-Modal Feature Fusion
by Dexuan Ding, Lei Wang, Liyun Zhu, Tom Gedeon, Piotr Koniusz
First submitted to arxiv on: 2 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 tackles a crucial problem in computer vision tasks: effectively fusing features from diverse representations, domains, and modalities. Traditional methods like concatenation, element-wise operations, and non-linear techniques often fail to capture structural relationships and deep feature interactions, leading to inefficient or misaligned feature fusion. The authors propose a novel approach that shifts the focus from high-dimensional feature space to a lower-dimensional, interpretable graph space. They construct relationship graphs that encode feature relationships at different levels (e.g., clip, frame, patch, token) and expand these graphs through iterative updates to capture deeper interactions. A learnable graph fusion operator is introduced to integrate these expanded relationships for more effective fusion. The authors demonstrate the effectiveness of their method on video anomaly detection tasks, showcasing strong performance across multi-representational, multi-modal, and multi-domain feature fusion tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to combine different types of information from various sources (e.g., images, videos, text) to get a better understanding. This paper is about finding a way to do this effectively. Current methods have limitations, like not being able to capture relationships between different pieces of information. The authors suggest a new approach that looks at the connections between these pieces of information in a more visual and intuitive way. They show that this method can be used for tasks like detecting unusual events in videos. |
Keywords
» Artificial intelligence » Anomaly detection » Multi modal » Token
