update

README.md

@@ -38,7 +38,7 @@ This section helps you download the tracklets data and place them correctly, as
 **NOTE** we use the term `proposal` in our code to represent tracklet proposals in video-level, which is totally different with the concept of "proposal" in "proposal-based methods" in our paper. In our paper, we use "proposals to represent paired subject-object tracklet segments. In contrast, here the term `proposal` in our code represents long-term object tracklets in video-level (i.e., without sliding window or video segments).
 
 ## Tracklet data for VidVRD 
-1. Download the tracklet with features at here:  [train](), [test](https://pan.zju.edu.cn/share/694f908a22fff11c037eb50876)
+1. Download the tracklet with features at here:  [train (to be released)](), [test](https://pan.zju.edu.cn/share/694f908a22fff11c037eb50876)
 
 2. Download the tracklet with features used in ["Beyond Short-Term Snippet: Video Relation Detection with Spatio-Temporal Global Context"](https://pkumyd.github.io/paper/CVPR2020_VideoVRD.pdf) at the author's personal page [here](http://www.muyadong.com/publication.html). (**NOTE** we use the term `pku` (i.e., Peking University) in our code to refer to their tracklets & features)
 
@@ -55,9 +55,9 @@ Put them under the dir of this project (or any other position if you use absolut
             --dataset_class pku_i3d
     ```
 
+## Tracklet data for VidOR 
 
-
-
+- TODO
 
 # Evaluation:
 
@@ -75,6 +75,7 @@ Put them under the dir of this project (or any other position if you use absolut
         --dataset_type vidor \
         --save_path datasets/GT_json_for_eval/VidORval_gts.json
     ```
+
 # Training (TODO)
 
 the code for training is still being organized (an initial version will be completed before March 28, 2022).
@@ -110,4 +111,3 @@ If our work is helpful for your research, please cite our publication:
 - All scores are truncated to 4 decimal places (not rounded)
 
 
-