Summary of Sampling Bag Of Views For Open-vocabulary Object Detection, by Hojun Choi et al.
Sampling Bag of Views for Open-Vocabulary Object Detection
by Hojun Choi, Junsuk Choe, Hyunjung Shim
First submitted to arxiv on: 24 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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 The paper presents a novel approach to open-vocabulary object detection (OVD) by leveraging compositional structures learned by visual language models (VLMs). The existing methods use individual region embeddings, but this can lead to noisy representations. To address this, the authors propose a concept-based alignment method that groups contextually related concepts into a bag and adjusts their scales for more effective embedding alignment. This approach is combined with Faster R-CNN and achieves improvements in box AP50 and mask AP on novel categories in COCO and LVIS benchmarks. The method also reduces computation by 80.3% compared to previous research, making it more efficient. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper improves object detection by using a new way to understand images. It takes the ideas from language models and uses them to help detect objects in pictures. Right now, methods use individual parts of an image to detect objects, but this can be noisy. The authors came up with a new method that groups related parts together and adjusts their sizes to make it work better. This helps detect objects more accurately on unseen categories. It also makes the process faster by reducing computation. |
Keywords
» Artificial intelligence » Alignment » Cnn » Embedding » Mask » Object detection
