Venue: NeurIPS 2022 Workshop NeurReps

Reviews:

final_score: rating: 6: Technically solid, moderate impact paper, with no major concerns with respect to evaluation and reproducibility.
review_content: summary: A VSA-based framework, amenable to neuromorphic hardware, is proposed for visual understanding of a scene composed of multiple objects with different configurations of features. A generative model is proposed that can be used to infer components and their features. This model encodes an input image in a factorized fashion to the hyper-vector space where the binding operation is equivalent to the transformations of features. A resonator network is used to perform tractable inference, namely factorize the hyper-vector representation of an image into sources of variations. It is first employed in a simple setting where different letters with arbitrary colors are positioned randomly on an image. Then, a more challenging setting with additional factors of rotation and scale is studied. Thanks to the translational form for these two new factors in log-polar space, the inference is divided into two parts: one part decodes the scale, rotation and identity factors in the log-polar space, while the other decodes the rest in the cartesian space. A communication module (factor) is also introduced which helps unbinding all factors of the current partition when decoding factors in the other partition. In both provided examples, the dynamic process converges to an equilibrium state corresponding to a reasonable disentanglement of rigidly transformed letters in the input image.
strengths_and_weaknesses: * Strengths:
  1. It is well-written. Extending the simple setting to a more challenging one with rotations and scales eases the understanding. The mathematical background on hyper-vector representation has a clear organization.
  2. This work introduces a new useful trick of dividing factors of variations into partitions with each decoding all factors in the same space conditioned on a communication factor from the other partition. One might find this handy for other kinds of transformations, too.

* Weaknesses:
  1. The evaluation is limited as inference is done only for two examples with only few number of letters.
  2. No limitations are provided for the proposed framework

questions:   1. How much is your framework robust to the number of objects in the scene? Suppose an image with dozens of letters. As objects are explained away one at a time, early when several letters are still present in the image, wouldn’t it be difficult for the resonator network to identify an object.
  2. How do you explain sudden shrink in the combinatorial search space and sharp estimates?
  3. In figure 1, part D, what is the y-axis? Also, what does the intensities in part E of the same figure correspond to?
  4. In figure 2, I think that the letter ‘k’ should have a ground-truth rotation around 145 degrees. Then why is 300 written as the estimation?
limitations: There is no discussion over limitations of the proposed framework.
recommended_decision: 3: Accept
review_title: Neuromorphic Visual Scene Understanding

final_score: rating: 6: Technically solid, moderate impact paper, with no major concerns with respect to evaluation and reproducibility.
review_content: summary: Understanding object position and pose is still an open-problem in visual scene understanding. The authors propose a neuromorphic framework that uses resonator networks that attempts to extract object identities and their transformations through modelling it as a factorization problem. This work relies on the fact that scaling and rotation are equivalent to translation in log-polar space. Synthetic scenes of 2D shapes undergoing rigid geometric transformations and color changes were used to train and evaluate these networks. 
strengths_and_weaknesses: Originality: Clever use of log-polar space, resonator networks, and factorization optimization.  

Quality: Technically sound and well-supported. Clearly mentions it uses synthetic, 2D objects. 

Clarity: Excellent clarity. Well formatted, figures are clear and writing is clear and concise. 

Significance: Good significance. Since the dataset is made of synthetic, well-behaved data, the significance is reduced substantially. How does this model perform under more complicated scenes with backgrounds? How about objects with multiple colors? A good starting point.
questions: What happens if your synthetic data is less well-behaved? What happens if the objects are multiple colors? How about scenes with noise?
limitations: Authors mentioned and show that the dataset is synthetic in nature. A direct statement stating the unknown performance on real data would be good. Would be good to answer a few of the questions in the above box.
recommended_decision: 3: Accept
review_title: Great Presentation. Concise. However, synthetic data should be less behaved when training and evaluating 

final_score: rating: 2: A paper with major technical flaws, and/or poor evaluation, limited impact, or poor reproducibility.
review_content: summary: The authors propose an RNN-based architecture for visual scene understanding.
Similar to Capsule networks, their aim is to decouple detecting the object position from detecting its properties such as orientation and color.


strengths_and_weaknesses: ++ Interesting application of RNNs in visual scene understanding.

-- No relevance to the workshop. The authors use the term "geometry" purely in the context geometric transformations in the image space such as translation and rotation. There is no focus on understanding the geometry of neural representations.
The work is a better fit for a CV venue, than to NeurReps.

-- Unclear what the work achieves. The paper ends abruptly without clear results, beyond the activations in Figure 1 and Figure 2.

-- No mention or comparison with Capsule Networks, which aim to achieve more or less the same goal.

questions: Why did not you consider comparing with Capsule Networks?
limitations: No mention of limitations.
recommended_decision: 1: Reject
review_title: Low relevance; better fit in a CV venue.

Decision: Accept (Poster)