Context reasoning is critical in a wide variety of applications where current inputs need to be interpreted in the light of previous experience and knowledge. Both spatial and temporal contextual information play a critical role in the domain of visual recognition. Here we investigate spatial constraints (what image features provide contextual information and where they are located), and temporal constraints (when different contextual cues matter) for visual recognition. The task is to reason about the scene context and infer what a target object hidden behind a flap is in a natural image. To tackle this problem, we first describe an online human psychophysics experiment recording active sampling via mouse clicks in lift-the-flap games and identify clicking patterns and features which are diagnostic for high contextual reasoning accuracy. As a proof of the usefulness of these clicking patterns and visual features, we extend a state-of-the-art recurrent model capable of attending to salient context regions, dynamically integrating useful information, making inferences, and predicting class label for the target object over multiple clicks. The proposed model achieves human-level contextual reasoning accuracy, shares human-like sampling behavior and learns interpretable features for contextual reasoning.
The paper is about context reasoning in the visual recognition. They designed a task called 'lift-the-flap', where the human or the model are given an image with one of the object blacked out. The image is provided in a relative low resolution, and the subjects could choose to click on area to reveal a local window of high-res image. The subject then need to answer what is the object in the blacked out region after a few clicks (ranging from 1 to 8 clicks). The authors has collected human data using mTurk, and also trained a model (named ClickNet) to perform the task. ClickNet has a LSTM memory which can carry the information of previous clicks and use spatial attention mechanism to decide the next click location (just use the argmax of the attention weight.) I find the paper is clearly-written and quite easy to follow. I think this is an interesting paper comparing human attentional performance to a trained computation model (without any human-imposed prior), showing that they behave similarly. However, I am not sure it would be of general interest to the ICLR audience. This may be more suitable for cognitive science conference/journal in my opinion. I have a few suggestions below:
1. In formula 2, the attention weight (before softmax) is  " e_ti = A_h * h_{t−1} + A_a * a_ti " I am surprised there is no term that is h_{t-1} * a_ti, which do the content-based reading that is common in memory model like (DNC) or in language processing (transformer.) Can the author comment on this decision?
2. In result 5.1, the authors titled: "WHAT: IMPORTANCE SAMPLING VIA ATTENTION AND PRIOR INFORMATION". I am not sure I get where is the importance sampling appear in this paragraph. Importance sampling is a specific term, which refers to estimating properties of a particular distribution, while only having samples generated from a different distribution than the distribution of interest. I did not see how that is related to this paragraph. Or, I may have missed it, and would like the authors to elaborate.
3. I could not follow well the discussion in section 5.5 WHEN: ROLE OF RECURRENT CONNECTIONS. Based on the title of the section, I would expect to discuss why the recurrence of LSTM is important. Though, the paragraph is mainly talking about other control models like SVM and HMM, so I don't quite follow it. Also, it is not clear to me the word 'When' is a good choice. I am convinced that the LSTM helps the model carrying information across click, but it is not clearly shown the sequential order is critical (say click location 1, then location 2, then location 3 compared to location 2, location 3, then location 1 matters) Maybe it does? But, it is now shown in the results.
4. The comparison of where the human versus the model click (in Figure 6) is impressive. I wonder if the authors could go even one step further that is to see if the temporal order of the clicks are similar in human vs. model. It may not be the case. However, if it is true, that could make an even stronger case that the model has a similar way to decide attentional location compared to human subjects.
Minor:
1. It is impressive that the authors have considered a wide range of control models. Though, for a conference paper with limited page limit, I don't think it is necessary to describe all these models in the main text ( for the SVM, HMM, and CRF models.)