Summary of Cp-detr: Concept Prompt Guide Detr Toward Stronger Universal Object Detection, by Qibo Chen et al.
CP-DETR: Concept Prompt Guide DETR Toward Stronger Universal Object Detection
by Qibo Chen, Weizhong Jin, Jianyue Ge, Mengdi Liu, Yuchao Yan, Jian Jiang, Li Yu, Xuanjiang Guo, Shuchang Li, Jianzhong Chen
First submitted to arxiv on: 13 Dec 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 A new universal object detection model called CP-DETR is proposed to address two main challenges in recent research: efficiently using prior information from prompts and reducing alignment bias in downstream tasks. The CP-DETR model combines a prompt visual hybrid encoder with a multi-label loss function and an auxiliary detection head, allowing it to fully utilize prompted information. Two practical concept prompt generation methods are also designed to reduce alignment bias: visual prompts and optimized prompts. Experimental results show that the CP-DETR model achieves superior performance in various scenarios, including zero-shot object detection on LVIS and ODinW35 datasets. The Swin-T backbone model achieves 47.6 zero-shot AP on LVIS, while the Swin-L backbone model achieves 32.2 zero-shot AP on ODinW35. Additionally, the visual prompt generation method achieves 68.4 AP on COCO val by interactive detection, and the optimized prompt achieves 73.1 fully-shot AP on ODinW13. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new universal object detection model called CP-DETR that helps solve two big problems in current research: using prompts to help detect objects and reducing mistakes when trying to understand what the prompts mean. The model uses a special way of combining text and visual information, and it also has two ways to make better prompts for detecting objects. Tests show that this new model does a great job at finding objects without needing extra training data. For example, the Swin-T version of the model can find 47.6% of objects on its own, and the Swin-L version can find 32.2%. The paper also shows that using these special prompts can help with other tasks, like finding objects on pictures. |
Keywords
» Artificial intelligence » Alignment » Encoder » Loss function » Object detection » Prompt » Zero shot
