From: NIPS.cc/2018/Workshop/Spatiotemporal/Paper6/AnonReviewer2
relevance: 4: high
impact: 2: low
novelty: 2: low

From: NIPS.cc/2018/Workshop/Spatiotemporal/Paper6/AnonReviewer2
evaluation: The paper investigates features learned from different networks by training on different granularity tasks for the problem of transfer learning. The paper trains four networks with different supervision: coarse action classification, fine-grained action classification, coarse video captioning and fine-grained video captioning on the Something-Something video dataset, and fineture these network for action classification on a new dataset, 20bn-kichenware. The paper shows that the networks trained with fine-grained supervision learn better features, so as to perform better on the new dataset for action classification.

Positives:
- The idea of investigating the impact of task granularity of feature learning for transfer learning is interesting.
- The paper shows that fine-grained task supervision achieves better transfer learning performance.

Negatives:
- It is unclear how significant are the results from the paper verifying the assumption that fine-grain tasks learn better features, since the paper only conducts experiments on one dataset, i.e., the 20bn-kichenware dataset. Also, this is a new dataset introduced by the authors in the paper. It is important to conduct experiments on various action recognition datasets and large scale video datasets to investigate the impact of task granularity for transfer learning.

From: NIPS.cc/2018/Workshop/Spatiotemporal/Paper6/AnonReviewer1
relevance: 4: high
impact: 3: borderline
novelty: 3: borderline

From: NIPS.cc/2018/Workshop/Spatiotemporal/Paper6/AnonReviewer1
evaluation: Summary

The paper studies transfer learning using a model trained on data with different label granularities. The authors show that models trained on classification/captioning tasks with more finegrained labels do better on a target classification task. For the target task, authors have collected a new dataset, 20bn-kitchenware.

Overall, the message of the paper is clear, although it's not clear yet what exactly changes about the representations that make them do better on a new task. For follow-up work, I'd like to see some insights on what aspects of task A make for better representations for task B.

Overall eval

1. What did you learn from reading this paper that you did not know before?
Specifically for this video-domain, using captioning as a source task can improve classification task. 

2. What was the most exciting part of the work?
A new dataset. 

3. What in your opinion makes this work stand apart from other papers in this area?
A new dataset. 

4. Does the paper fit into the scope of the workshop?
Yes.

5. Are you aware of relevant literature that you may want to make the authors aware of?
Perhaps recent work on meta-learning etc.