**Object detection** is the task of drawing one bounding box around each instance of the type of object one wants to detect. Typically, image classification is done before object detection. With neural networks, the usual procedure for object detection is to train a classification network, replace the last layer with a regression layer which essentially predicts pixel-wise if the object is there or not. An bounding box inference algorithm is added at last to make a consistent prediction (see [Deep Neural Networks for Object Detection](http://papers.nips.cc/paper/5207-deep-neural-networks-for-object-detection.pdf)).

The paper introduces RPNs (Region Proposal Networks). They are end-to-end trained to generate region proposals.They simoultaneously regress region bounds and bjectness scores at each location on a regular grid.

RPNs are one type of fully convolutional networks. They take an image of any size as input and output a set of rectangular object proposals, each with an objectness score.

## See also
* [R-CNN](http://www.shortscience.org/paper?bibtexKey=conf/iccv/Girshick15#joecohen)
* [Fast R-CNN](http://www.shortscience.org/paper?bibtexKey=conf/iccv/Girshick15#joecohen)
* [Faster R-CNN](http://www.shortscience.org/paper?bibtexKey=conf/nips/RenHGS15#martinthoma)
* [Mask R-CNN](http://www.shortscience.org/paper?bibtexKey=journals/corr/HeGDG17)

This paper models object detection as a regression problem for bounding
boxes and object class probabilities with a single pass through the CNN. The
main contribution is the idea of dividing the image into a 7x7 grid, and having
each cell predict a distribution over class labels as well as a bounding box
for the object whose center falls into it. It's much faster than R-CNN and
Fast R-CNN, as the additional step of extracting region proposals has been
removed.

## Strengths

- Works real-time. Base model runs at 45fps and a faster version goes up to
150fps, and they claim that it's more than twice as fast as other works on
real-time detection.

- End-to-end model; Localization and classification errors can be jointly
optimized.

- YOLO makes more localization errors and fewer background mistakes than
Fast R-CNN, so using YOLO to eliminate false background detections from
Fast R-CNN results in ~3% mAP gain (without much computational time as R-CNN
is much slower).

## Weaknesses / Notes

- Results fall short of state-of-the-art: 57.9% v/s 70.4% mAP (Faster R-CNN).

- Performs worse at detecting small objects, as at most one object per grid
cell can be detected.

This work expands on prior techniques for designing models that can both be stored using fewer parameters, and also execute using fewer operations and less memory, both of which are key desiderata for having trained machine learning models be usable on phones and other personal devices. 

The main contribution of the original MobileNets paper was to introduce the idea of using "factored" decompositions of Depthwise and Pointwise convolutions, which separate the procedures of "pull information from a spatial range" and "mix information across channels" into two distinct steps. In this paper, they continue to use this basic Depthwise infrastructure, but also add a new design element: the inverted-residual linear bottleneck. 

The reasoning behind this new layer type comes from the observation that, often, the set of relevant points in a high-dimensional space (such as the 'per-pixel' activations inside a conv net) actually lives on a lower-dimensional manifold. So, theoretically, and naively, one could just try to use lower dimensional internal representations to map the dimensionality of that assumed manifold. However, the authors argue that ReLU non-linearities kill information (because of the region where all inputs are mapped to zero), and so having layers contain only the number of dimensions needed for the manifold would mean that you end up with too-few dimensions after the ReLU information loss. However, you need to have non-linearities somewhere in the network in order to be able to learn complex, non-linear functions. 

So, the authors suggest a method to mostly use smaller-dimensional representations internally, but still maintain ReLus and the network's needed complexity. 

https://i.imgur.com/pN4d9Wi.png

- A lower-dimensional output is "projected up" into a higher dimensional output
- A ReLu is applied on this higher-dimensional layer
- That layer is then projected down into a smaller-dimensional layer, which uses a linear activation to avoid information loss
- A residual connection between the lower-dimensional output at the beginning and end of the expansion

This way, we still maintain the network's non-linearity, but also replace some of the network's higher-dimensional layers with lower-dimensional linear ones

When machine learning models need to run on personal devices, that implies a very particular set of constraints: models need to be fairly small and low-latency when run on a limited-compute device, without much loss in accuracy. A number of human-designed architectures have been engineered to try to solve for these constraints (depthwise convolutions, inverted residual bottlenecks), but this paper's goal is to use Neural Architecture Search (NAS) to explicitly optimize the architecture against latency and accuracy, to hopefully find a good trade-off curve between the two. 

This paper isn't the first time NAS has been applied on the problem of mobile-optimized networks, but a few choices are specific to this paper. 

1. Instead of just optimizing against accuracy, or optimizing against accuracy with a sharp latency requirement, the authors here construct a weighted loss that includes both accuracy and latency, so that NAS can explore the space of different trade-off points, rather than only those below a sharp threshold.  
2. They design a search space where individual sections or "blocks" of the network can be configured separately, with the hope being that this flexibility helps NAS trade off complexity more strongly in the early parts of the network, where, at a higher spatial resolution, it implies greater computation cost and latency, without necessary dropping that complexity later in the network, where it might be lower-cost. Blocks here are specified by the type of convolution op, kernel size, squeeze-and-excitation ratio, use of a skip op, output filter size, and the number of times an identical layer of this construction will be repeated to constitute a block. 

Mechanically, models are specified as discrete strings of tokens (a block is made up of tokens indicating its choices along these design axes, and a model is made up of multiple blocks). These are represented in a RL framework, where a RNN model sequentially selects tokens as "actions" until it gets to a full model specification . This is repeated multiple times to get a batch of models, which here functions analogously to a RL episode. These models are then each trained for only five epochs (it's desirable to use a full-scale model for accurate latency measures, but impractical to run its full course of training). After that point, accuracy is calculated, and latency determined by running the model on an actual Pixel phone CPU. These two measures are weighted together to get a reward, which is used to train the RNN model-selection model using PPO. 

https://i.imgur.com/dccjaqx.png

Across a few benchmarks, the authors show that models found with MNasNet optimization are able to reach parts of the accuracy/latency trade-off curve that prior techniques had not.

When humans classify images, we tend to use high-level information about the shape and position of the object. However, when convolutional neural networks classify images,, they tend to use low-level, or textural, information more than high-level shape information. This paper tries to understand what factors lead to higher shape bias or texture bias. 

To investigate this, the authors look at three datasets with disagreeing shape and texture labels. The first is GST, or Geirhos Style Transfer. In this dataset, style transfer is used to render the content of one class in the style of another (for example, a cat shape in the texture of an elephant). In the Navon dataset, a large-scale letter is rendered by tiling smaller letters. And, in the ImageNet-C dataset, a given class is rendered with a particular kind of distortion; here the distortion is considered to be the "texture label". In the rest of the paper, "shape bias" refers to the extent to which a model trained on normal images will predict the shape label rather than the texture label associated with a GST image. The other datasets are used in experiments where a model explicitly tries to learn either shape or texture. 

https://i.imgur.com/aw1MThL.png

To start off, the authors try to understand whether CNNs are inherently more capable of learning texture information rather than shape information. To do this, they train models on either the shape or the textural label on each of the three aforementioned datasets. On GST and Navon, shape labels can be learned faster and more efficiently than texture ones. On ImageNet-C (i.e. distorted ImageNet), it seems to be easier to learn texture than texture, but recall here that texture corresponds to the type of noise, and I imagine that the cardinality of noise types is far smaller than that of ImageNet images, so I'm not sure how informative this comparison is. Overall, this experiment suggests that CNNs are able to learn from shape alone without low-level texture as a clue, in cases where the two sources of information disagree 

The paper moves on to try to understand what factors about a normal ImageNet model give it higher or lower shape bias - that is, a higher or lower likelihood of classifying a GST image according to its shape rather than texture. Predictably, data augmentations have an effect here. When data is augmented with aggressive random cropping, this increases texture bias relative to shape bias, presumably because when large chunks of an object are cropped away, its overall shape becomes a less useful feature. Center cropping is better for shape bias, probably because objects are likely to be at the center of the image, so center cropping has less of a chance of distorting them. On the other hand, more "naturalistic" augmentations like adding Gaussian noise or distorting colors lead to a higher shape bias in the resulting networks, up to 60% with all the modifications. However, the authors also find that pushing the shape bias up has the result of dropping final test accuracy. 

https://i.imgur.com/Lb6RMJy.png

Interestingly, while the techniques that increase shape bias seem to also harm performance, the authors also find that higher-performing models tend to have higher shape bias (though with texture bias still outweighing shape) suggesting that stronger models learn how to use shape more effectively, but also that handicapping models' ability to use texture in order to incentivize them to use shape tends to hurt performance overall. 

Overall, my take from this paper is that texture-level data is actually statistically informative and useful for classification - even in terms of generalization - even if is too high-resolution to be useful as a visual feature for humans. CNNs don't seem inherently incapable of learning from shape, but removing their ability to rely on texture seems to lead to a notable drop in accuracy, suggesting there was real signal there that we're losing out on.