Object Detection

MobileNet is a type of convolutional neural network designed for mobile and embedded vision applications. They are based on a streamlined architecture that uses depthwise separable convolutions to build lightweight deep neural networks that can have low latency for mobile and embedded devices.

Barlow Twins is a self-supervised learning method that applies redundancy-reduction — a principle first proposed in neuroscience — to self supervised learning. The objective function measures the cross-correlation matrix between the embeddings of two identical networks fed with distorted versions of a batch of samples, and tries to make this matrix close to the identity. This causes the embedding vectors of distorted version of a sample to be similar, while minimizing the redundancy between the components of these vectors. Barlow Twins does not require large batches nor asymmetry between the network twins such as a predictor network, gradient stopping, or a moving average on the weight updates. Intriguingly it benefits from very high-dimensional output vectors.

UNet++ is an architecture for semantic segmentation based on the U-Net. Through the use of densely connected nested decoder sub-networks, it enhances extracted feature processing and was reported by its authors to outperform the U-Net in Electron Microscopy (EM), Cell, Nuclei, Brain Tumor, Liver and Lung Nodule medical image segmentation tasks.

Group Normalization is a normalization layer that divides channels into groups and normalizes the features within each group. GN does not exploit the batch dimension, and its computation is independent of batch sizes. In the case where the group size is 1, it is equivalent to Instance Normalization. As motivation for the method, many classical features like SIFT and HOG had group-wise features and involved group-wise normalization. For example, a HOG vector is the outcome of several spatial cells where each cell is represented by a normalized orientation histogram. Formally, Group Normalization is defined as: $$ \mu\_{i} = \frac{1}{m}\sum\_{k\in\mathcal{S}\_{i}}x\_{k} $$ $$ \sigma^{2}\_{i} = \frac{1}{m}\sum\_{k\in\mathcal{S}\_{i}}\left(x\_{k}-\mu\_{i}\right)^{2} $$ $$ \hat{x}\_{i} = \frac{x\_{i} - \mu\_{i}}{\sqrt{\sigma^{2}\_{i}+\epsilon}} $$ Here $x$ is the feature computed by a layer, and $i$ is an index. Formally, a Group Norm layer computes $\mu$ and $\sigma$ in a set $\mathcal{S}\_{i}$ defined as: $\mathcal{S}\_{i} = ${$k \mid k\_{N} = i\_{N} ,\lfloor\frac{k\_{C}}{C/G}\rfloor = \lfloor\frac{I\_{C}}{C/G}\rfloor $}. Here $G$ is the number of groups, which is a pre-defined hyper-parameter ($G = 32$ by default). $C/G$ is the number of channels per group. $\lfloor$ is the floor operation, and the final term means that the indexes $i$ and $k$ are in the same group of channels, assuming each group of channels are stored in a sequential order along the $C$ axis.

YOLOv2, or YOLO9000, is a single-stage real-time object detection model. It improves upon YOLOv1 in several ways, including the use of Darknet-19 as a backbone, batch normalization, use of a high-resolution classifier, and the use of anchor boxes to predict bounding boxes, and more.

ShuffleNet is a convolutional neural network designed specially for mobile devices with very limited computing power. The architecture utilizes two new operations, pointwise group convolution and channel shuffle, to reduce computation cost while maintaining accuracy.

A ShuffleNet Block is an image model block that utilises a channel shuffle operation, along with depthwise convolutions, for an efficient architectural design. It was proposed as part of the ShuffleNet architecture. The starting point is the Residual Block unit from ResNets, which is then modified with a pointwise group convolution and a channel shuffle operation.

A BiFPN, or Weighted Bi-directional Feature Pyramid Network, is a type of feature pyramid network which allows easy and fast multi-scale feature fusion. It incorporates the multi-level feature fusion idea from FPN, PANet and NAS-FPN that enables information to flow in both the top-down and bottom-up directions, while using regular and efficient connections. It also utilizes a fast normalized fusion technique. Traditional approaches usually treat all features input to the FPN equally, even those with different resolutions. However, input features at different resolutions often have unequal contributions to the output features. Thus, the BiFPN adds an additional weight for each input feature allowing the network to learn the importance of each. All regular convolutions are also replaced with less expensive depthwise separable convolutions. Comparing with PANet, PANet added an extra bottom-up path for information flow at the expense of more computational cost. Whereas BiFPN optimizes these cross-scale connections by removing nodes with a single input edge, adding an extra edge from the original input to output node if they are on the same level, and treating each bidirectional path as one feature network layer (repeating it several times for more high-level future fusion).

CenterNet is a one-stage object detector that detects each object as a triplet, rather than a pair, of keypoints. It utilizes two customized modules named cascade corner pooling and center pooling, which play the roles of enriching information collected by both top-left and bottom-right corners and providing more recognizable information at the central regions, respectively. The intuition is that, if a predicted bounding box has a high IoU with the ground-truth box, then the probability that the center keypoint in its central region is predicted as the same class is high, and vice versa. Thus, during inference, after a proposal is generated as a pair of corner keypoints, we determine if the proposal is indeed an object by checking if there is a center keypoint of the same class falling within its central region.

PSPNet, or Pyramid Scene Parsing Network, is a semantic segmentation model that utilises a pyramid parsing module that exploits global context information by different-region based context aggregation. The local and global clues together make the final prediction more reliable. We also propose an optimization Given an input image, PSPNet use a pretrained CNN with the dilated network strategy to extract the feature map. The final feature map size is $1/8$ of the input image. On top of the map, we use the pyramid pooling module to gather context information. Using our 4-level pyramid, the pooling kernels cover the whole, half of, and small portions of the image. They are fused as the global prior. Then we concatenate the prior with the original feature map in the final part of. It is followed by a convolution layer to generate the final prediction map.

A SENet is a convolutional neural network architecture that employs squeeze-and-excitation blocks to enable the network to perform dynamic channel-wise feature recalibration.

Cascade Corner Pooling is a pooling layer for object detection that builds upon the corner pooling operation. Corners are often outside the objects, which lacks local appearance features. CornerNet uses corner pooling to address this issue, where we find the maximum values on the boundary directions so as to determine corners. However, it makes corners sensitive to the edges. To address this problem, we need to let corners see the visual patterns of objects. Cascade corner pooling first looks along a boundary to find a boundary maximum value, then looks inside along the location of the boundary maximum value to find an internal maximum value, and finally, add the two maximum values together. By doing this, the corners obtain both the the boundary information and the visual patterns of objects.

Center Pooling is a pooling technique for object detection that aims to capture richer and more recognizable visual patterns. The geometric centers of objects do not necessarily convey very recognizable visual patterns (e.g., the human head contains strong visual patterns, but the center keypoint is often in the middle of the human body). The detailed process of center pooling is as follows: the backbone outputs a feature map, and to determine if a pixel in the feature map is a center keypoint, we need to find the maximum value in its both horizontal and vertical directions and add them together. By doing this, center pooling helps the better detection of center keypoints.

Deformable Attention Module is an attention module used in the Deformable DETR architecture, which seeks to overcome one issue base Transformer attention in that it looks over all possible spatial locations. Inspired by deformable convolution, the deformable attention module only attends to a small set of key sampling points around a reference point, regardless of the spatial size of the feature maps. By assigning only a small fixed number of keys for each query, the issues of convergence and feature spatial resolution can be mitigated. Given an input feature map $x \in \mathbb{R}^{C \times H \times W}$, let $q$ index a query element with content feature $\mathbf{z}\_{q}$ and a 2-d reference point $\mathbf{p}\_{q}$, the deformable attention feature is calculated by: $$ \text{DeformAttn}\left(\mathbf{z}\_{q}, \mathbf{p}\_{q}, \mathbf{x}\right)=\sum\_{m=1}^{M} \mathbf{W}\_{m}\left[\sum\_{k=1}^{K} A\_{m q k} \cdot \mathbf{W}\_{m}^{\prime} \mathbf{x}\left(\mathbf{p}\_{q}+\Delta \mathbf{p}\_{m q k}\right)\right] $$ where $m$ indexes the attention head, $k$ indexes the sampled keys, and $K$ is the total sampled key number $(K \ll H W) . \Delta p_{m q k}$ and $A_{m q k}$ denote the sampling offset and attention weight of the $k^{\text {th }}$ sampling point in the $m^{\text {th }}$ attention head, respectively. The scalar attention weight $A_{m q k}$ lies in the range $[0,1]$, normalized by $\sum_{k=1}^{K} A_{m q k}=1 . \Delta \mathbf{p}_{m q k} \in \mathbb{R}^{2}$ are of 2-d real numbers with unconstrained range. As $p\_{q}+\Delta p\_{m q k}$ is fractional, bilinear interpolation is applied as in Dai et al. (2017) in computing $\mathbf{x}\left(\mathbf{p}\_{q}+\Delta \mathbf{p}\_{m q k}\right)$. Both $\Delta \mathbf{p}\_{m q k}$ and $A\_{m q k}$ are obtained via linear projection over the query feature $z\_{q} .$ In implementation, the query feature $z\_{q}$ is fed to a linear projection operator of $3 M K$ channels, where the first $2 M K$ channels encode the sampling offsets $\Delta p\_{m q k}$, and the remaining $M K$ channels are fed to a softmax operator to obtain the attention weights $A\_{m q k}$.

Circular Smooth Label (CSL) is a classification-based rotation detection technique for arbitrary-oriented object detection. It is used for circularly distributed angle classification and addresses the periodicity of the angle and increases the error tolerance to adjacent angles.

EfficientDet is a type of object detection model, which utilizes several optimization and backbone tweaks, such as the use of a BiFPN, and a compound scaling method that uniformly scales the resolution,depth and width for all backbones, feature networks and box/class prediction networks at the same time.

Fast R-CNN is an object detection model that improves in its predecessor R-CNN in a number of ways. Instead of extracting CNN features independently for each region of interest, Fast R-CNN aggregates them into a single forward pass over the image; i.e. regions of interest from the same image share computation and memory in the forward and backward passes.

Deformable DETR is an object detection method that aims mitigates the slow convergence and high complexity issues of DETR. It combines the best of the sparse spatial sampling of deformable convolution, and the relation modeling capability of Transformers. Specifically, it introduces a deformable attention module, which attends to a small set of sampling locations as a pre-filter for prominent key elements out of all the feature map pixels. The module can be naturally extended to aggregating multi-scale features, without the help of FPN.

Cascade R-CNN is an object detection architecture that seeks to address problems with degrading performance with increased IoU thresholds (due to overfitting during training and inference-time mismatch between IoUs for which detector is optimal and the inputs). It is a multi-stage extension of the R-CNN, where detector stages deeper into the cascade are sequentially more selective against close false positives. The cascade of R-CNN stages are trained sequentially, using the output of one stage to train the next. This is motivated by the observation that the output IoU of a regressor is almost invariably better than the input IoU. Cascade R-CNN does not aim to mine hard negatives. Instead, by adjusting bounding boxes, each stage aims to find a good set of close false positives for training the next stage. When operating in this manner, a sequence of detectors adapted to increasingly higher IoUs can beat the overfitting problem, and thus be effectively trained. At inference, the same cascade procedure is applied. The progressively improved hypotheses are better matched to the increasing detector quality at each stage.

R-CNN, or Regions with CNN Features, is an object detection model that uses high-capacity CNNs to bottom-up region proposals in order to localize and segment objects. It uses selective search to identify a number of bounding-box object region candidates (“regions of interest”), and then extracts features from each region independently for classification.

Region-based Fully Convolutional Networks, or R-FCNs, are a type of region-based object detector. In contrast to previous region-based object detectors such as Fast/Faster R-CNN that apply a costly per-region subnetwork hundreds of times, R-FCN is fully convolutional with almost all computation shared on the entire image. To achieve this, R-FCN utilises position-sensitive score maps to address a dilemma between translation-invariance in image classification and translation-variance in object detection.

Hou et al. proposed coordinate attention, a novel attention mechanism which embeds positional information into channel attention, so that the network can focus on large important regions at little computational cost. The coordinate attention mechanism has two consecutive steps, coordinate information embedding and coordinate attention generation. First, two spatial extents of pooling kernels encode each channel horizontally and vertically. In the second step, a shared $1\times 1$ convolutional transformation function is applied to the concatenated outputs of the two pooling layers. Then coordinate attention splits the resulting tensor into two separate tensors to yield attention vectors with the same number of channels for horizontal and vertical coordinates of the input $X$ along. This can be written as \begin{align} z^h &= \text{GAP}^h(X) \end{align} \begin{align} z^w &= \text{GAP}^w(X) \end{align} \begin{align} f &= \delta(\text{BN}(\text{Conv}_1^{1\times 1}([z^h;z^w]))) \end{align} \begin{align} f^h, f^w &= \text{Split}(f) \end{align} \begin{align} s^h &= \sigma(\text{Conv}_h^{1\times 1}(f^h)) \end{align} \begin{align} s^w &= \sigma(\text{Conv}_w^{1\times 1}(f^w)) \end{align} \begin{align} Y &= X s^h s^w \end{align} where $\text{GAP}^h$ and $\text{GAP}^w$ denote pooling functions for vertical and horizontal coordinates, and $s^h \in \mathbb{R}^{C\times 1\times W}$ and $s^w \in \mathbb{R}^{C\times H\times 1}$ represent corresponding attention weights. Using coordinate attention, the network can accurately obtain the position of a targeted object. This approach has a larger receptive field than BAM and CBAM. Like an SE block, it also models cross-channel relationships, effectively enhancing the expressive power of the learned features. Due to its lightweight design and flexibility, it can be easily used in classical building blocks of mobile networks.

Spatial-Reduction Attention, or SRA, is a multi-head attention module used in the Pyramid Vision Transformer architecture which reduces the spatial scale of the key $K$ and value $V$ before the attention operation. This reduces the computational/memory overhead. Details of the SRA in the stage $i$ can be formulated as follows: $$ \text{SRA}(Q, K, V)=\text { Concat }\left(\operatorname{head}\_{0}, \ldots \text { head }\_{N\_{i}}\right) W^{O} $$ $$\text{ head}\_{j}=\text { Attention }\left(Q W\_{j}^{Q}, \operatorname{SR}(K) W\_{j}^{K}, \operatorname{SR}(V) W\_{j}^{V}\right) $$ where Concat $(\cdot)$ is the concatenation operation. $W\_{j}^{Q} \in \mathbb{R}^{C\_{i} \times d\_{\text {head }}}$, $W\_{j}^{K} \in \mathbb{R}^{C\_{i} \times d\_{\text {head }}}$, $W\_{j}^{V} \in \mathbb{R}^{C\_{i} \times d\_{\text {head }}}$, and $W^{O} \in \mathbb{R}^{C\_{i} \times C\_{i}}$ are linear projection parameters. $N\_{i}$ is the head number of the attention layer in Stage $i$. Therefore, the dimension of each head (i.e. $\left.d\_{\text {head }}\right)$ is equal to $\frac{C\_{i}}{N\_{i}} . \text{SR}(\cdot)$ is the operation for reducing the spatial dimension of the input sequence ($K$ or $V$ ), which is written as: $$ \text{SR}(\mathbf{x})=\text{Norm}\left(\operatorname{Reshape}\left(\mathbf{x}, R\_{i}\right) W^{S}\right) $$ Here, $\mathbf{x} \in \mathbb{R}^{\left(H\_{i} W\_{i}\right) \times C\_{i}}$ represents a input sequence, and $R\_{i}$ denotes the reduction ratio of the attention layers in Stage $i .$ Reshape $\left(\mathbf{x}, R\_{i}\right)$ is an operation of reshaping the input sequence $\mathbf{x}$ to a sequence of size $\frac{H\_{i} W\_{i}}{R\_{i}^{2}} \times\left(R\_{i}^{2} C\_{i}\right)$. $W\_{S} \in \mathbb{R}^{\left(R\_{i}^{2} C\_{i}\right) \times C\_{i}}$ is a linear projection that reduces the dimension of the input sequence to $C\_{i}$. $\text{Norm}(\cdot)$ refers to layer normalization.

Mixture model network (MoNet) is a general framework allowing to design convolutional deep architectures on non-Euclidean domains such as graphs and manifolds. Image and description from: Geometric deep learning on graphs and manifolds using mixture model CNNs

Res2Net is an image model that employs a variation on bottleneck residual blocks. The motivation is to be able to represent features at multiple scales. This is achieved through a novel building block for CNNs that constructs hierarchical residual-like connections within one single residual block. This represents multi-scale features at a granular level and increases the range of receptive fields for each network layer.

A Res2Net Block is an image model block that constructs hierarchical residual-like connections within one single residual block. It was proposed as part of the Res2Net CNN architecture. The block represents multi-scale features at a granular level and increases the range of receptive fields for each network layer. The $3 \times 3$ filters of $n$ channels is replaced with a set of smaller filter groups, each with $w$ channels. These smaller filter groups are connected in a hierarchical residual-like style to increase the number of scales that the output features can represent. Specifically, we divide input feature maps into several groups. A group of filters first extracts features from a group of input feature maps. Output features of the previous group are then sent to the next group of filters along with another group of input feature maps. This process repeats several times until all input feature maps are processed. Finally, feature maps from all groups are concatenated and sent to another group of $1 \times 1$ filters to fuse information altogether. Along with any possible path in which input features are transformed to output features, the equivalent receptive field increases whenever it passes a $3 \times 3$ filter, resulting in many equivalent feature scales due to combination effects. One way of thinking of these blocks is that they expose a new dimension, scale, alongside the existing dimensions of depth, width, and cardinality.

Cascade Mask R-CNN extends Cascade R-CNN to instance segmentation, by adding a mask head to the cascade. In the Mask R-CNN, the segmentation branch is inserted in parallel to the detection branch. However, the Cascade R-CNN has multiple detection branches. This raises the questions of 1) where to add the segmentation branch and 2) how many segmentation branches to add. The authors consider three strategies for mask prediction in the Cascade R-CNN. The first two strategies address the first question, adding a single mask prediction head at either the first or last stage of the Cascade R-CNN. Since the instances used to train the segmentation branch are the positives of the detection branch, their number varies in these two strategies. Placing the segmentation head later on the cascade leads to more examples. However, because segmentation is a pixel-wise operation, a large number of highly overlapping instances is not necessarily as helpful as for object detection, which is a patch-based operation. The third strategy addresses the second question, adding a segmentation branch to each cascade stage. This maximizes the diversity of samples used to learn the mask prediction task. At inference time, all three strategies predict the segmentation masks on the patches produced by the final object detection stage, irrespective of the cascade stage on which the segmentation mask is implemented and how many segmentation branches there are.

MobileViT is a vision transformer that is tuned to mobile phone

Non-maximum suppression is an integral part of the object detection pipeline. First, it sorts all detection boxes on the basis of their scores. The detection box $M$ with the maximum score is selected and all other detection boxes with a significant overlap (using a pre-defined threshold) with $M$ are suppressed. This process is recursively applied on the remaining boxes. As per the design of the algorithm, if an object lies within the predefined overlap threshold, it leads to a miss. Soft-NMS solves this problem by decaying the detection scores of all other objects as a continuous function of their overlap with M. Hence, no object is eliminated in this process.

Neighborhood Attention is a restricted self attention pattern in which each token's receptive field is limited to its nearest neighboring pixels. It was proposed in Neighborhood Attention Transformer as an alternative to other local attention mechanisms used in Hierarchical Vision Transformers. NA is in concept similar to stand alone self attention (SASA), in that both can be implemented with a raster scan sliding window operation over the key value pair. However, NA would require a modification to handle corner pixels, which helps maintain a fixed receptive field size and an increased number of relative positions. The primary challenge in experimenting with both NA and SASA has been computation. Simply extracting key values for each query is slow, takes up a large amount of memory, and is eventually intractable at scale. NA was therefore implemented through a new CUDA extension to PyTorch, NATTEN.

ShuffleNet V2 Block is an image model block used in the ShuffleNet V2 architecture, where speed is the metric optimized for (instead of indirect ones like FLOPs). It utilizes a simple operator called channel split. At the beginning of each unit, the input of $c$ feature channels are split into two branches with $c - c'$ and $c'$ channels, respectively. Following G3, one branch remains as identity. The other branch consists of three convolutions with the same input and output channels to satisfy G1. The two $1\times1$ convolutions are no longer group-wise, unlike the original ShuffleNet. This is partially to follow G2, and partially because the split operation already produces two groups. After convolution, the two branches are concatenated. So, the number of channels keeps the same (G1). The same “channel shuffle” operation as in ShuffleNet is then used to enable information communication between the two branches. The motivation behind channel split is that alternative architectures, where pointwise group convolutions and bottleneck structures are used, lead to increased memory access cost. Additionally more network fragmentation with group convolutions reduces parallelism (less friendly for GPU), and the element-wise addition operation, while they have low FLOPs, have high memory access cost. Channel split is an alternative where we can maintain a large number of equally wide channels (equally wide minimizes memory access cost) without having dense convolutions or too many groups.

Adaptive Feature Pooling pools features from all levels for each proposal in object detection and fuses them for the following prediction. For each proposal, we map them to different feature levels. Following the idea of Mask R-CNN, RoIAlign is used to pool feature grids from each level. Then a fusion operation (element-wise max or sum) is utilized to fuse feature grids from different levels. The motivation for this technique is that in an FPN we assign proposals to different feature levels based on the size of proposals, which could be suboptimal if images with small differences are assigned to different levels, or if the importance of features is not strongly correlated to their level which they belong.

AugMix mixes augmented images through linear interpolations. Consequently it is like Mixup but instead mixes augmented versions of the same image.