**BiDet** is a binarized neural network learning method for efficient object detection. Conventional network binarization methods directly quantize the weights and activations in one-stage or two-stage detectors with constrained representational capacity, so that the information redundancy in the networks causes numerous false positives and degrades the performance significantly. On the contrary, BiDet fully utilizes the representational capacity of the binary neural networks for object detection by redundancy removal, through which the detection precision is enhanced with alleviated false positives. Specifically, the information bottleneck (IB) principle is generalized to object detection, where the amount of information in the high-level feature maps is constrained and the mutual information between the feature maps and object detection is maximized.

The NA method can be divided into two steps: (i) Training a neural network approximation of f , and (ii) inference of xˆ. Step (i) is conventional and involves training a generic neural network on a dataset
ˆ
of input/output pairs from the simulator, denoted D, resulting in f, an approximation of the forward ˆ
model. This is illustrated in the left inset of Fig 1. In step (ii), our goal is to use ∂f/∂x to help us gradually adjust x so that we achieve a desired output of the forward model, y. This is similar to many classical inverse modeling approaches, such as the popular Adjoint method [8, 9]. For many practical
ˆ
expression for the simulator, from which it is trivial to compute ∂f/∂x, and furthermore, we can use modern deep learning software packages to efficiently estimate gradients, given a loss function L.
More formally, let y be our target output, and let xˆi be our current estimate of the solution, where i indexes each solution we obtain in an iterative gradient-based estimation procedure. Then we compute xˆi+1 with
inverse problems, however, obtaining ∂f/∂x requires significant expertise and/or effort, making these approaches challenging. Crucially, fˆ from step (i) provides us with a closed-form differentiable

Neural adjoint

Benchmarking deep inverse models over time, and the neural-adjoint method

BiDet

BiDet: An Efficient Binarized Object Detector

**Relative Position Encodings** are a type of position embeddings for [Transformer-based models](https://paperswithcode.com/methods/category/transformers) that attempts to exploit pairwise, relative positional information. Relative positional information is supplied to the model on two levels: values and keys. This becomes apparent in the two modified self-attention equations shown below. First, relative positional information is supplied to the model as an additional component to the keys

$$ e\_{ij} = \frac{x\_{i}W^{Q}\left(x\_{j}W^{K} + a^{K}\_{ij}\right)^{T}}{\sqrt{d\_{z}}} $$

Here $a$ is an edge representation for the inputs $x\_{i}$ and $x\_{j}$. The [softmax](https://paperswithcode.com/method/softmax) operation remains unchanged from vanilla self-attention. Then relative positional information is supplied again as a sub-component of the values matrix:

$$ z\_{i} = \sum^{n}\_{j=1}\alpha\_{ij}\left(x\_{j}W^{V} + a\_{ij}^{V}\right)$$

In other words, instead of simply combining semantic embeddings with absolute positional ones, relative positional information is added to keys and values on the fly during attention calculation.

Source: [Jake Tae](https://jaketae.github.io/study/relative-positional-encoding/)

Image Source: [Relative Positional Encoding for Transformers with Linear Complexity](https://www.youtube.com/watch?v=qajudaEHuq8

Source	BiDet: An Efficient Binarized Object Detector
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com