To show readers vivid impression of our dataset, we prepare first-hand materials. 

1. In the `images/` folder, there are selected samples in different months in AirBirds. `1~15.png` contain changing lighting and scenarios where flying birds appear, `airplane.png` contains 
a landing airliner. All images are captured by the camera system deployed alongside the runways in a real-world airport.  

We also support the necessity of AirBirds with extra comparison with related datasets and detail the experiment evaluation with specific configurations and explanations. 

2. The contents in `accv2022suppl.pdf` can be summarized with _five_ keywords: 
    1. **Comparisons**. This section presents extra comparisons among AirBirds and existing relevant datasets, such as the property `Task`, `Sequence` and `Year`. 
    2. **Configurations**. This section completes the training configurations, appending `Learning Rate`, `Epoch`,  `Period` etc, which are absent in the main paper due to space limitation.
    3. **Analysis**. This section analyzes the reasons behind unsatisfactory performances of _sixteen_ strong baselines for evaluation in detail. 
    4. **Visualization**. To further understand what the detectors have learned from AirBirds, we take `YOLOv5` as an example, visualizing its feature maps and comparing it with the original image. 
    5. **Discussion**. We discuss the advantages and limitations of existing datasets and declare the `license` of AirBirds. 