Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects
We present Sequential Attend, Infer, Repeat (SQAIR), an interpretable deep generative model for videos of moving objects. It can reliably discover and track objects throughout the sequence of frames, and can also generate future frames conditioning on the current frame, thereby simulating expected motion of objects. This is achieved by explicitly encoding object presence, locations and appearances in the latent variables of the model. SQAIR retains all strengths of its predecessor, Attend, Infer, Repeat (AIR, Eslami et al., 2016), including learning in an unsupervised manner, and addresses its shortcomings. We use a moving multi-MNIST dataset to show limitations of AIR in detecting overlapping or partially occluded objects, and show how SQAIR overcomes them by leveraging temporal consistency of objects. Finally, we also apply SQAIR to real-world pedestrian CCTV data, where it learns to reliably detect, track and generate walking pedestrians with no supervision.
The authors propose a temporal extension of the structured image generative model AIR.  The model is composed of two parts: the first one, DISC, detects new objects at each time step, and is very similar to AIR. The only difference is that its predictions are conditioned on the updated latent variables of previous frames. The second one PROP, is responsable for updating or forgetting latent variables given a new image. It does so by employing two RNNs : the first one (temporal) updates the previous latent states. The second one (relational) models relations between objects, and is recurrent over the objects in the frame. The authors show extensive experimentation on a stochastic moving MNIST dataset and qualitative results on a dataset collected from static CCTV cameras. The structure of their model allows them to learn to detect and track objects in an unsupervised fashion, perform video prediction, and use the latent variables for solving simple tasks (addition, counting of objects).  The authors propose a compelling and ambitious approach that tackles the extremely important problem of structured generative modeling, where the structure can be used for unsupervised learning of high level recognition tasks. Incorporating structure in the model to reflect the prior that a scene is composed of objects that should be mainly described by their location and their appearance, and showing that the learned latent variables are interpretable and thus useful in down stream tasks, is an extremely appealing direction. At this point, the approach does not model the background, and has not been shown to be applicable in contexts where the camera is non static (as background subtraction is necessary), but it constitues a nice step towards in this important direction. It is nice that the authors show a real-world application of their method.  Unfortunately, the experimental evaluation is rather lacking, and I have several questions in regards to this matter: - the authors should compare to other VAE-flavoured state of the art video generative modelling methods, such as Denton et al. ICML 2018. In particular, the qualitative results of Figure 12 are clearly not state of the art. - how important is the relational RNN ? The authors transparently acknowledge that the method is complex and an ablation study would be very useful. It would also be helpful to show in a more tangible manner how this RNN is "implementing the explaining away phenomenon" ? l.122 -  "All objects are present through the sequence; the digits can move out of the frame, but always come back." l.152, 153 Why impose this constraint on the dataset ? What severely limits the model from dealing where cases when the digit does not come back ? Also, can the digits appear during the sequence and if not, why not ? If this showed failures of the model, it would be interesting to analyse why. - I don't agree with the justifications for not filling in Table 1: a classifier could be learned for VRNN for counting, just like in the addition experiment. And for completion, why not perform these experiments for the MLP-VRNN ; it is better in terms of KL divergence than its CONV counterpart. -  "We will show that the resulting model, which we name Sequential AIR  (Sequential Attend, Infer, Repeat (SQAIR )), retains the strengths of the original AIR formulation while outperforming it in both synthetic and real-world scenarios." I did not find the results for AIR on the CCTV dataset. - can you give a qualitative interpretation of the fact that the reconstruction metric of MLP-AIR is better than MLP-SQAIR ? - Figure 10: why are there two entirely lines input fully black inputs ? - "which signifies that the latent representation can be allocated to regions of the frames that need it, as opposed to describing the whole scene" l.170 This is not clear to me. Cumulatively, this leaves the reader a little bit unsatisfied (to summarize: missing results, no ablation study, missing comparison to an important state of the art paper, main results on a toyish dataset that could be slightly less toy). Also, it would strengthen the paper a lot to show a more thorough and quantitative evaluation on the real-world dataset. This is what leads me to the current decision of "Marginally below the acceptance threshold."    Regarding clarity of the method, the paper is generally well written, but I would like to have the following clarifications:  - l. 118 you specify that the temporal RNN has no access to the image. Then in appendix D, equation (14), as well as Figure 3, imply the opposite. Can you clarify ? - Figure 6 is ambiguous: the lower lines are predictions or reconstructions ?  Finally, a note that for this type of problem, accompanying videos are much appreciated to ease the qualitative evaluation of the samples and reconstructions, especially to appreciate temporal consistency.   Typos / errors :  - then should be than l.53 and l.188 - l.59 in the AIR model, n is at most N (a fixed hyperparameter of the method) - l.65: according to Figure 1, f_theta^dec 's input is z_t^what,i and output is g_t^i. Calling the decoder a "glimpse decoder" seems to imply some form of attention mechanism on the generated image, which is not the case. Also, z^where should be indexed with i. - l.97 vector valued should be -valued vector - l.103 logical flow of text would require that it is also explained that z^what and z^where are sampled during the propagation step -  images l.151 should be digits - l.160 Being similar to a VAE.  -------- Assessment after the rebuttal -------- The authors have addressed most of my concerns. The experimental protocol is still slightly lacking, as they do not experimentally validate the impact of using a relational RNN on top of the temporal RNN; and I still think that the toy dataset should have had certain properties to test the strengths of the proposed model (like appearing and disappearing digits.) However, like the authors and the other reviewers, I am convinced of the importance of the problem addressed, and I acknowledge that the authors have proposed a compelling and ambitious approach to tackle this problem. Therefore, with the clarifications and improvements that the authors have promised to bring, I now think that this paper is good and should be accepted.