Comments (1)
The activity prediction mAP is computed over each person. There is no bounding box prediction. Given the observations of a person, the model outputs a multi-class probability of all actions, which is used to compare with the ground truth. See problem formulation in Section 3 of the paper. mAP is originally proposed in the Information Retrieval field. See the definition here.
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Related Issues (20)
- regarding the preprocess.py HOT 1
- How did you do action detection on ActEV dataset?and how did you overcome class imbalance in ActEV VIRAT? HOT 2
- What is the backbone network used in the pretrained object detector for the person behavior module? HOT 2
- regarding visualization the ActEV world coordinates HOT 8
- Visualization Questions HOT 5
- try to visualization HOT 1
- request visualization HOT 6
- about the activity prediction HOT 5
- Download ActEV Dataset HOT 5
- How to get prepared data? HOT 1
- solved,thank you!
- exp mask HOT 1
- visualization of the results HOT 1
- train without activity prediction, Colab Running Time HOT 8
- Is there a opencv code to visualize predictions on my own video ? HOT 1
- Is there a config file for your model ? HOT 1
- visualization HOT 5
- Visualization of activity prediction HOT 1
- ETHUCY Dataset test split size HOT 1
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