Summary of Order-aware Interactive Segmentation, by Bin Wang et al.
Order-aware Interactive Segmentation
by Bin Wang, Anwesa Choudhuri, Meng Zheng, Zhongpai Gao, Benjamin Planche, Andong Deng, Qin Liu, Terrence Chen, Ulas Bagci, Ziyan Wu
First submitted to arxiv on: 16 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 This paper proposes a novel approach called OIS (order-aware interactive segmentation) to improve the accuracy of interactive segmentation, which aims to accurately segment target objects with minimal user interactions. The current methods often fail to separate target objects from the background due to limited understanding of order and relative depth between objects in a scene. To address this issue, OIS explicitly encodes the relative depth into order maps and introduces novel attention mechanisms that guide user interactions and object-level understanding. This approach allows both dense and sparse integration of user clicks, enhancing accuracy and efficiency compared to prior works. The experimental results demonstrate state-of-the-art performance on HQSeg44K and DAVIS datasets, improving mIoU after one click by 7.61 and 1.32 respectively, while also doubling inference speed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to separate objects from the background using computer vision. Currently, this task is difficult because it’s hard to understand how objects are arranged in space. The new method, called OIS, tries to solve this problem by understanding how objects are ordered and their depth relative to each other. This allows for more accurate and efficient segmentation of objects. The results show that OIS is better than previous methods at segmenting objects and can even do it faster. |
Keywords
» Artificial intelligence » Attention » Inference
