Comments (4)
@sgarbanti This is just to be consistent with existing works (e.g., DEM and MT). Besides, in the DEM paper, the authors mention the high overlapping rate between the two sets of annotations (tIoU of 70.2%), so if two segments overlap by a large percentage, the caption outcome should be close to each other. But you're right, we could choose to avoid the unnecessary performance loss by only considering val_1 and test_1, just like what we did for grounding. Reporting both would be ideal.
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@LuoweiZhou Ok, thank you very much for your answer.
Just to know, is there a particular reason why you didn't generate captions for all segments?
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@sgarbanti We could, but since val_2 and test_2 have no bounding box annotations, we could not conduct any grounding evaluation. This is the main obstacle. In my opinion, like what you mentioned, a better way to evaluate given GT segments is to consider only val_1 and test_1 (or whatever file your prediction is based on) and the what-we-assumed convention to evaluate on both files is kind of ill-posed. Therefore, we'd encourage you to report evaluation on both file as a fair comparison and at the same time on val/test_1 only for a more truthful outcome.
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@LuoweiZhou Ok i understand, since the issue is resolved I am closing it.
Thank you very much.
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Related Issues (20)
- Masked Transformer in this repo HOT 3
- In inference-only mode, how to split my own video into several segments to represent each event? HOT 1
- rgb_motion_1d features HOT 4
- Error running main.py HOT 3
- Uniform sampling script? HOT 1
- conda environment HOT 2
- How to extract frame-wise feats? HOT 1
- error when running the Starter code with GPU HOT 2
- main.py error HOT 7
- Viewing results HOT 1
- question about use pre-train model on my own video HOT 11
- i can't fine the grd_reference file HOT 1
- main.py HOT 1
- AssertionError HOT 3
- evaluate
- Does process exist to use pre-trained model on my own video?
- About default loss weights
- Import "evaluate" could not be resolved
- Broken dependencies
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