ON THE EFFECTIVENESS OF TASK GRANULARITY FOR TRANSFER LEARNING
We describe a DNN for video classification and captioning, trained end-to-end, with shared features, to solve tasks at different levels of granularity, exploring the link between granularity in a source task and the quality of learned features for transfer learning. For solving the new task domain in transfer learning, we freeze the trained encoder and fine-tune an MLP on the target domain. We train on the Something-Something dataset with over 220, 000 videos, and multiple levels of target granularity, including 50 action groups, 174 fine-grained action categories and captions. Classification and captioning with Something-Something are challenging because of the subtle differences between actions, applied to thousands of different object classes, and the diversity of captions penned by crowd actors. Our model performs better than existing classification baselines for SomethingSomething, with impressive fine-grained results. And it yields a strong baseline on the new Something-Something captioning task. Experiments reveal that training with more fine-grained tasks tends to produce better features for transfer learning.
 Summary This paper studied video classification and video caption generation problem. Especially, the paper tried few baseline architectures datasets used from pretraining features on recently proposed Something-something-V2 dataset and another newly proposed dataset. This paper argues that fine-grained annotation helps learning good features and enhance transfer learning results Strength There are some interesting observations in terms of transfer learning. Especially, comparison of fine-grained and coarse-grained dataset for transfer learning (Table 2), and effect of using caption and newly collected dataset for transfer learning (Figure 5) is interesting and the result is worth to be shared to the community. In addition, a new dataset that are carefully collected for transfer learning might be useful to make progress on video classification and captioning. Weakness To many tables with different neural network parameter settings look distracting and does not provide much information. Instead, focusing more on effect of dataset for transfer learning and providing more analysis on this aspect would make the main argument of this paper stronger. For example, effect of transfer could be studied on different dataset. If transfer learning with proposed dataset containing find-grained annotation / captions is useful, it might help boosting performance on other video recognition dataset as well. Providing analysis on understanding the effect of fine-grained / captioning dataset for feature learning might help understanding as well. Overall rating This paper suggest interesting observations and useful dataset, but provides relatively less analysis on these observations. I believe providing more analysis on the dataset and effect of transfer would make the main argument of this paper stronger.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
ON THE EFFECTIVENESS OF TASK GRANULARITY FOR TRANSFER LEARNING
We describe a DNN for video classification and captioning, trained end-to-end, with shared features, to solve tasks at different levels of granularity, exploring the link between granularity in a source task and the quality of learned features for transfer learning. For solving the new task domain in transfer learning, we freeze the trained encoder and fine-tune an MLP on the target domain. We train on the Something-Something dataset with over 220, 000 videos, and multiple levels of target granularity, including 50 action groups, 174 fine-grained action categories and captions. Classification and captioning with Something-Something are challenging because of the subtle differences between actions, applied to thousands of different object classes, and the diversity of captions penned by crowd actors. Our model performs better than existing classification baselines for SomethingSomething, with impressive fine-grained results. And it yields a strong baseline on the new Something-Something captioning task. Experiments reveal that training with more fine-grained tasks tends to produce better features for transfer learning.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 Summary This paper studied video classification and video caption generation problem. Especially, the paper tried few baseline architectures datasets used from pretraining features on recently proposed Something-something-V2 dataset and another newly proposed dataset. This paper argues that fine-grained annotation helps learning good features and enhance transfer learning results Strength There are some interesting observations in terms of transfer learning. Especially, comparison of fine-grained and coarse-grained dataset for transfer learning (Table 2), and effect of using caption and newly collected dataset for transfer learning (Figure 5) is interesting and the result is worth to be shared to the community. In addition, a new dataset that are carefully collected for transfer learning might be useful to make progress on video classification and captioning. Weakness To many tables with different neural network parameter settings look distracting and does not provide much information. Instead, focusing more on effect of dataset for transfer learning and providing more analysis on this aspect would make the main argument of this paper stronger. For example, effect of transfer could be studied on different dataset. If transfer learning with proposed dataset containing find-grained annotation / captions is useful, it might help boosting performance on other video recognition dataset as well. Providing analysis on understanding the effect of fine-grained / captioning dataset for feature learning might help understanding as well. Overall rating This paper suggest interesting observations and useful dataset, but provides relatively less analysis on these observations. I believe providing more analysis on the dataset and effect of transfer would make the main argument of this paper stronger.
assistant
 Summary This paper studied video classification and video caption generation problem. Especially, the paper tried few baseline architectures datasets used from pretraining features on recently proposed Something-something-V2 dataset and another newly proposed dataset. This paper argues that fine-grained annotation helps learning good features and enhance transfer learning results Strength There are some interesting observations in terms of transfer learning. Especially, comparison of fine-grained and coarse-grained dataset for transfer learning (Table 2), and effect of using caption and newly collected dataset for transfer learning (Figure 5) is interesting and the result is worth to be shared to the community. In addition, a new dataset that are carefully collected for transfer learning might be useful to make progress on video classification and captioning. Weakness To many tables with different neural network parameter settings look distracting and does not provide much information. Instead, focusing more on effect of dataset for transfer learning and providing more analysis on this aspect would make the main argument of this paper stronger. For example, effect of transfer could be studied on different dataset. If transfer learning with proposed dataset containing find-grained annotation / captions is useful, it might help boosting performance on other video recognition dataset as well. Providing analysis on understanding the effect of fine-grained / captioning dataset for feature learning might help understanding as well. Overall rating This paper suggest interesting observations and useful dataset, but provides relatively less analysis on these observations. I believe providing more analysis on the dataset and effect of transfer would make the main argument of this paper stronger.