add nuscenes-qa-mini dataset

content/dataset/index.md

@@ -14,9 +14,9 @@ sections:
       spacing:
         padding: ['1.8rem', '0', '0', '0']
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+    id: aware
     content:
       title: Acoustic Waveform Respiratory Evaluation (AWARE)
-      id: aware
       subtitle: January 2024
       text: |
         {{< columns >}}
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       columns: '2'
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         padding: ['20px', '0', '20px', '0']
+  - block: markdown
+    id: nuscenes-qa-mini
+    content:
+      title: NuScenes-QA-mini Dataset
+      subtitle: 'January 2024'
+      text: |
+        This dataset is used for multimodal question-answering tasks in autonomous driving scenarios. We created this dataset based on [nuScenes-QA dataset](https://github.com/qiantianwen/NuScenes-QA) for evaluation in our paper [Modality Plug-and-Play: Elastic Modality Adaptation in Multimodal LLMs for Embodied AI](/publication/2023-mpnp-llm/). The samples are divided into day and night scenes.
+
+        ![NuScenes-QA-mini Data Sample](2024-nuscenes-qa-mini/nuqa_example.png)
+
+        This dataset is built on the [nuScenes](https://www.nuscenes.org/) mini-split, where we obtain the QA pairs from the [original nuScenes-QA dataset](https://github.com/qiantianwen/NuScenes-QA). Each data sample contains **6-view RGB camera captures, a 5D LiDAR point cloud, and a corresponding text QA pair**. The data in the nuScenes-QA dataset is collected from driving scenes in cities of Boston and Singapore with diverse locations, time, and weather conditions.
+        {{< hr-pittisl >}}
+        * You may find more details on our [dataset homepage](https://huggingface.co/datasets/KevinNotSmile/nuscenes-qa-mini).
+        * The source code of generating the dataset can be found [in our GitHub repository](https://github.com/pittisl/mPnP-LLM/tree/main/nuqamini).
+        * Our [Modality Plug-and-Play](/publication/2023-mpnp-llm/) paper utilizes this dataset.
+    design:
+      columns: '2'
+      spacing:
+        padding: ['20px', '0', '20px', '0']
 #   - block: markdown
 #     content:
 #       title:

content/publication/2023-mpnp-llm/index.md

@@ -105,7 +105,8 @@ input modalities, namely RGB camera view and LiDAR point cloud, is shown in the
 
 We use the nuScenes-QA dataset for multimodal visual QA in autonomous driving,
 with results from workstation-level desktop platforms with RTX A6000 and mobile platform
-of Nvidia Jetson AGX Orin.
+of Nvidia Jetson AGX Orin. Our processed dataset is published as the
+[NuScenes-QA-mini dataset](/dataset/#nuscenes-qa-mini).
 
 Compared with existing approaches, mPnP-LLM achieves better accuracy under similar costs.