Update README.md

README.md

@@ -8,6 +8,16 @@ configs:
   - config_name: frames
     data_files: "frames/*.tar"
 ---
+# Grounding YouTube Dataset #
+What, when, and where? -- Self-Supervised Spatio-Temporal Grounding in Untrimmed Multi-Action Videos from Narrated Instructions
+[arxiv](https://arxiv.org/abs/2303.16990)
+
+## The dataset is present in three styles:
+* Untrimmed videos + annotations within the entire video
+* Action clips extracted from the videos + annotations in each clip
+* Action frames extracted from the videos + annotation of the frame
+
+
 ## Example usage for clips:
 ### Also decoding raw binary video data and json
 ```python
@@ -43,4 +53,20 @@ dataset = (
     .shuffle(100)
     .to_tuple("mp4", "json")
     .map_tuple(load_video, load_json)
-)
+)
+```
+
+## Evaluation - Pointwise accuracy:
+For pointwise accuracy, a prediction is considered correct if the predicted point lies inside the annotated ground truth bounding box. In order to evaluate your predictions, see [evaluation](evaluation/)
+
+If you're using GroundingYouTube in your research or applications, please cite using this BibTeX:
+```
+@InProceedings{Chen_2024_CVPR,
+    author    = {Chen, Brian and Shvetsova, Nina and Rouditchenko, Andrew and Kondermann, Daniel and Thomas, Samuel and Chang, Shih-Fu and Feris, Rogerio and Glass, James and Kuehne, Hilde},
+    title     = {What When and Where? Self-Supervised Spatio-Temporal Grounding in Untrimmed Multi-Action Videos from Narrated Instructions},
+    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
+    month     = {June},
+    year      = {2024},
+    pages     = {18419-18429}
+}
+```