Summary of Dynamic Object Queries For Transformer-based Incremental Object Detection, by Jichuan Zhang et al.
Dynamic Object Queries for Transformer-based Incremental Object Detection
by Jichuan Zhang, Wei Li, Shuang Cheng, Ya-Li Li, Shengjin Wang
First submitted to arxiv on: 31 Jul 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 Incremental object detection (IOD) seeks to adaptively learn new classes while retaining existing ones. The paper tackles catastrophic forgetting in IOD by proposing dynamic object queries for Transformer-based architecture. The DyQ-DETR model incrementally expands its representation capabilities, achieving a stability-plasticity tradeoff. It does this by introducing new learnable object queries that are aggregated with previous ones to adapt both old and new knowledge. Additionally, isolated bipartite matching reduces inter-class confusion by eliminating interactions among object queries across phases. The paper also presents risk-balanced partial calibration for effective exemplar replay. Extensive experiments demonstrate significant improvements over state-of-the-art methods with limited parameter overhead. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research is about improving computers’ ability to detect and identify objects as they learn new things. One problem is that when a computer learns something new, it often forgets what it already knew. The scientists developed a way to use “dynamic object queries” to help the computer remember both old and new knowledge. They tested this method with impressive results, showing that their approach was better than other methods at detecting objects. |
Keywords
» Artificial intelligence » Object detection » Transformer
