Summary of Ha-fgovd: Highlighting Fine-grained Attributes Via Explicit Linear Composition For Open-vocabulary Object Detection, by Yuqi Ma et al.
HA-FGOVD: Highlighting Fine-grained Attributes via Explicit Linear Composition for Open-Vocabulary Object Detection
by Yuqi Ma, Mengyin Liu, Chao Zhu, Xu-Cheng Yin
First submitted to arxiv on: 24 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Multimedia (cs.MM)
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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 proposes a novel approach to enhance the object detection capabilities of Open-vocabulary Object Detection (OVD) models, specifically focusing on highlighting fine-grained attributes like colors or materials. It leverages Large Multi-modal Models (LMMs) to extract attribute-specific features and composes them in linear space with global text features, allowing for seamless transfer between OVD models. This approach is evaluated on the FG-OVD dataset, achieving state-of-the-art performance and improving fine-grained attribute-level detection across various mainstream OVD models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps large-scale object detection models better recognize objects based on specific attributes like colors or materials. It uses a special kind of model that can understand both images and text to highlight these attributes. By combining the global image features with the highlighted attribute features, the model becomes better at detecting objects with specific characteristics. |
Keywords
» Artificial intelligence » Multi modal » Object detection
