Paper:
The abstract paragraph should be indented 1/2 inch (3 picas) on both left and righthand margins. Use 10 point type, with a vertical spacing of 11 points. The word ABSTRACT must be centered, in small caps, and in point size 12. Two line spaces precede the abstract. The abstract must be limited to one paragraph.
Authors' names are set in boldface, and each name is placed above its corresponding address. The lead author's name is to be listed first, and the co-authors' names are set to follow. Authors sharing the same address can be on the same line.
Please pay special attention to the instructions in section 4 regarding figures, tables, acknowledgments, and references.

Review:
The authors propose a variant to convLSTMs with convolutional peephole units (as opposed to the original Hadamard product) and tied gates. The description of the model is confusing, the authors don't offer a strong justification for the proposed approach, some of the technical choices seem flawed.
It is also obviously false that removing an LSTM gate does not incur in a reduction of trainable parameters: "There were other attempts to design smaller recurrent gated models [...] based on either removing one of the gates or the activation functions [...] these models had no significant reduction in trainable parameters".
The model description is difficult to follow, but from what I can gather the model depiction is flawed. In particular, the proposed model bases on the idea to use tied gates to reduce the number of parameters. f^{(t)}, i.e., the value that governs the input, forger and output gates, is derived via a sigmoid. When the sigmoid is zero, the network will hence set all the gates to zero, ignoring the input, forgetting the memory and suppressing the output, which is hardly a behaviour one could ever want. Unfortunately, almost half of the sigmoid spectrum will lead to such behavior. This seems to be a poor modeling decision.
The nodes in Figure 1 seem to suggest that two consecutive non-linearities are applied in a row, if W_fc is clamped to zero, as reported by the authors, there is no reason to specify it, the corresponding part of the equation can be removed and the narrative simplified. In sec3, the meaning of “net input image” and “network gate image value” is unclear. Also the description of the equation is hard to follow, e.g., the square bracket notation is eventually explained only after 8 lines of text.
At the beginning of Sec4 err_1 and err_2 are defined as the difference between the predicted frame and the target frame, and vice versa. This error is then fed to the “rgcLSTM input arranger unit and to the next higher layer”. By my understanding the error of one layer is fed in the next as an input. I wonder if such error is provided also at inference time, giving a clear guidance to the network to produce the correct output exploiting privileged information. This could also explain why the training process was completed in only one epoch.
- Typos:
*Intro: More important → more importantly
* page5: ReL -> ReLu