Summary of Yolo-rd: Introducing Relevant and Compact Explicit Knowledge to Yolo by Retriever-dictionary, By Hao-tang Tsui et al.
YOLO-RD: Introducing Relevant and Compact Explicit Knowledge to YOLO by Retriever-Dictionary
by Hao-Tang Tsui, Chien-Yao Wang, Hong-Yuan Mark Liao
First submitted to arxiv on: 20 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 The paper introduces a novel module, called Retriever-Dictionary (RD), which enhances the performance of YOLO-based object detection models by incorporating explicit knowledge from the entire dataset. The RD module allows these models to efficiently retrieve features from a Dictionary built using insights from Visual Models (VM), Large Language Models (LLM), or Visual Language Models (VLM). This architecture enables the model to benefit multiple tasks, including segmentation, detection, and classification, at pixel and image levels. Experimental results show that the RD module significantly improves model performance, achieving over 3% increase in mean Average Precision for object detection with less than 1% increase in model parameters. The RD module also improves the effectiveness of 2-stage models and DETR-based architectures like Faster R-CNN and Deformable DETR. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to find a specific object in a messy room filled with lots of similar things. Traditional computer vision models are like a person looking only at one spot, missing the bigger picture. This new research introduces an innovative way to make these models better by giving them information from all around the room. The “Retriever-Dictionary” (RD) module helps YOLO-based object detection models learn more effectively by incorporating knowledge from different sources. This improvement leads to better performance in tasks like object segmentation, detection, and classification. By using this new approach, researchers were able to achieve a significant increase in accuracy without adding many more details. |
Keywords
» Artificial intelligence » Classification » Cnn » Mean average precision » Object detection » Yolo
