- The paper is incremental and does not have much technical substance. It just adds a new loss to [31].
- "Embedding" is an overloaded word for a scalar value that represents object ID.
- The model of [31] is used in a post-processing stage to refine the detection. Ideally, the proposed model should be end-to-end without any post-processing.
- Keypoint detection results should be included in the experiments section.
- Sometimes the predicted tag value might be in the range of tag values for two or more nearby people, how is it determined to which person the keypoint belongs?
- Line 168: It is mentioned that the anchor point changes if the neck is occluded. This makes training noisy since the distances for most examples are computed with respect to the neck.
Overall assessment: I am on the fence for this paper. The paper achieves state-of-the-art performance, but it is incremental and does not have much technical substance. Furthermore, the main improvement comes from running [31] in a post-processing stage.
- The paper is incremental and does not have much technical substance. It just adds a new loss to [31].