weakness/questions: 1. Description of the framework: It's not very clear what Bs is in the formulation. It's not introduced in the formulation, but later on the paper talks about how to form Bs along with Os and Zs for different supervision signals. And it;s very confusing what is Bs's role in the formulation. 2. computational cost: it would be great to see an analysis about the computation cost. 3. Experiment section: it seems that for the comparison with other methods, the tracklets are also generated using different process. So it's hard to draw conclusions based on the results. Is it possible to apply different algorithms to same set of tracklets? For example, for the comparison of temporal vs temp+BB, the conclusion is not clear as there are three ways of generating tracklets. It seems that the conclusion is -- when using same tracklet set, the temp + BB achieves similar performance as using temporal signal only. However, this is not explicitly stated in the paper. 4. The observation and conclusions are hidden in the experimental section. It would be great if the paper can highlight those observations and conclusions, which is very useful for understanding the trade-offs of annotation effort and corresponding training performance. 5. comparison with fully supervised methods: It would be great if the paper can show comparison with other fully supervised methods. 6. What is the metric used for the video level supervision experiment? It seems it's not using the tracklet based metrics here, but the paper didn't give details on that.
4. The observation and conclusions are hidden in the experimental section. It would be great if the paper can highlight those observations and conclusions, which is very useful for understanding the trade-offs of annotation effort and corresponding training performance.