Summary of Context-aware Aerial Object Detection: Leveraging Inter-object and Background Relationships, by Botao Ren et al.
Context-Aware Aerial Object Detection: Leveraging Inter-Object and Background Relationships
by Botao Ren, Botian Xu, Xue Yang, Yifan Pu, Jingyi Wang, Zhidong Deng
First submitted to arxiv on: 5 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 proposed framework leverages Transformer-based models and Contrastive Language-Image Pre-training (CLIP) features to capture relationships between objects, backgrounds, and spatial relations in aerial imagery. Building on two-stage detectors, Region of Interest (RoI) proposals are treated as tokens, accompanied by CLIP Tokens obtained from multi-level image segments. These tokens are then passed through a Transformer encoder, where specific spatial and geometric relations are incorporated into the attention weights, which are adaptively modulated and regularized. Self-supervised constraints on CLIP Tokens ensure consistency. The approach achieves consistent improvements on three benchmark datasets, setting new state-of-the-art results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a way to improve object detection in aerial imagery by considering relationships between objects and backgrounds. It uses special features called CLIP Tokens and a type of machine learning model called Transformers to understand these relationships. This helps the object detector make more accurate decisions about what it sees in the image. The approach is tested on three different datasets and performs better than previous methods. |
Keywords
* Artificial intelligence * Attention * Encoder * Machine learning * Object detection * Self supervised * Transformer
