Semantic Segmentation

Co-Scale Conv-Attentional Image Transformer (CoaT) is a Transformer-based image classifier equipped with co-scale and conv-attentional mechanisms. First, the co-scale mechanism maintains the integrity of Transformers' encoder branches at individual scales, while allowing representations learned at different scales to effectively communicate with each other. Second, the conv-attentional mechanism is designed by realizing a relative position embedding formulation in the factorized attention module with an efficient convolution-like implementation. CoaT empowers image Transformers with enriched multi-scale and contextual modeling capabilities.

DEXTR, or Deep Extreme Cut, obtains an object segmentation from its four extreme points: the left-most, right-most, top, and bottom pixels. The annotated extreme points are given as a guiding signal to the input of the network. To this end, we create a heatmap with activations in the regions of extreme points. We center a 2D Gaussian around each of the points, in order to create a single heatmap. The heatmap is concatenated with the RGB channels of the input image, to form a 4-channel input for the CNN. In order to focus on the object of interest, the input is cropped by the bounding box, formed from the extreme point annotations. To include context on the resulting crop, we relax the tight bounding box by several pixels. After the pre-processing step that comes exclusively from the extreme clicks, the input consists of an RGB crop including an object, plus its extreme points. ResNet-101 is chosen as backbone of the architecture. We remove the fully connected layers as well as the max pooling layers in the last two stages to preserve acceptable output resolution for dense prediction, and we introduce atrous convolutions in the last two stages to maintain the same receptive field. After the last ResNet-101 stage, we introduce a pyramid scene parsing module to aggregate global context to the final feature map. The output of the CNN is a probability map representing whether a pixel belongs to the object that we want to segment or not. The CNN is trained to minimize the standard cross entropy loss, which takes into account that different classes occur with different frequency in a dataset.

DetNet is a backbone convolutional neural network for object detection. Different from traditional pre-trained models for ImageNet classification, DetNet maintains the spatial resolution of the features even though extra stages are included. DetNet attempts to stay efficient by employing a low complexity dilated bottleneck structure.

An ECA block has similar formulation to an SE block including a squeeze module for aggregating global spatial information and an efficient excitation module for modeling cross-channel interaction. Instead of indirect correspondence, an ECA block only considers direct interaction between each channel and its k-nearest neighbors to control model complexity. Overall, the formulation of an ECA block is: \begin{align} s = F_\text{eca}(X, \theta) & = \sigma (\text{Conv1D}(\text{GAP}(X))) \end{align} \begin{align} Y & = s X \end{align} where $\text{Conv1D}(\cdot)$ denotes 1D convolution with a kernel of shape $k$ across the channel domain, to model local cross-channel interaction. The parameter $k$ decides the coverage of interaction, and in ECA the kernel size $k$ is adaptively determined from the channel dimensionality $C$ instead of by manual tuning, using cross-validation: \begin{equation} k = \psi(C) = \left | \frac{\log_2(C)}{\gamma}+\frac{b}{\gamma}\right |\text{odd} \end{equation} where $\gamma$ and $b$ are hyperparameters. $|x|\text{odd}$ indicates the nearest odd function of $x$. Compared to SENet, ECANet has an improved excitation module, and provides an efficient and effective block which can readily be incorporated into various CNNs.

Height-driven Attention Network, or HANet, is a general add-on module for improving semantic segmentation for urban-scene images. It emphasizes informative features or classes selectively according to the vertical position of a pixel. The pixel-wise class distributions are significantly different from each other among horizontally segmented sections in the urban-scene images. Likewise, urban-scene images have their own distinct characteristics, but most semantic segmentation networks do not reflect such unique attributes in the architecture. The proposed network architecture incorporates the capability exploiting the attributes to handle the urban scene dataset effectively.

Recently, dense connections have attracted substantial attention in computer vision because they facilitate gradient flow and implicit deep supervision during training. Particularly, DenseNet that connects each layer to every other layer in a feed-forward fashion and has shown impressive performances in natural image classification tasks. We propose HyperDenseNet, a 3-D fully convolutional neural network that extends the definition of dense connectivity to multi-modal segmentation problems. Each imaging modality has a path, and dense connections occur not only between the pairs of layers within the same path but also between those across different paths. This contrasts with the existing multi-modal CNN approaches, in which modeling several modalities relies entirely on a single joint layer (or level of abstraction) for fusion, typically either at the input or at the output of the network. Therefore, the proposed network has total freedom to learn more complex combinations between the modalities, within and in-between all the levels of abstraction, which increases significantly the learning representation. We report extensive evaluations over two different and highly competitive multi-modal brain tissue segmentation challenges, iSEG 2017 and MRBrainS 2013, with the former focusing on six month infant data and the latter on adult images. HyperDenseNet yielded significant improvements over many state-of-the-art segmentation networks, ranking at the top on both benchmarks. We further provide a comprehensive experimental analysis of features re-use, which confirms the importance of hyper-dense connections in multi-modal representation learning.

Matrix NMS, or Matrix Non-Maximum Suppression, performs non-maximum suppression with parallel matrix operations in one shot. It is motivated by Soft-NMS. Soft-NMS decays the other detection scores as a monotonic decreasing function $f(iou)$ of their overlaps. By decaying the scores according to IoUs recursively, higher IoU detections will be eliminated with a minimum score threshold. However, such process is sequential like traditional Greedy NMS and can not be implemented in parallel. Matrix NMS views this process from another perspective by considering how a predicted mask $m\_{j}$ being suppressed. For $m\_{j}$, its decay factor is affected by: (a) The penalty of each prediction $m\_{i}$ on $m\_{j}$ $\left(s\_{i}>s\_{j}\right)$, where $s\_{i}$ and $s\_{j}$ are the confidence scores; and (b) the probability of $m\_{i}$ being suppressed. For (a), the penalty of each prediction $m\_{i}$ on $m\_{j}$ could be easily computed by $f\left(\right.$ iou $\left.\_{i, j}\right)$. For (b), the probability of $m\_{i}$ being suppressed is not so elegant to be computed. However, the probability usually has positive correlation with the IoUs. So here we directly approximate the probability by the most overlapped prediction on $m\_{i}$ as $$ f\left(\text { iou. }\_{, i}\right)=\min\_{\forall s\_{k}>s\_{i}} f\left(\text { iou }\_{k, i}\right) $$ To this end, the final decay factor becomes $$ \operatorname{decay}\_{j}=\min\_{\forall s\_{i}>s\_{j}} \frac{f\left(\text { iou }\_{i, j}\right)}{f\left(\text { iou }\_{\cdot, i}\right)} $$ and the updated score is computed by $s\_{j}=s\_{j} \cdot$ decay $\_{j} .$ The authors consider the two most simple decremented functions, denoted as linear $f\left(\right.$ iou $\left.\_{i, j}\right)=1-$ iou $\_{i, j}$, and Gaussian $f\left(\right.$ iou $\left.\_{i, j}\right)=\exp \left(-\frac{i o u\_{i, j}^{2}}{\sigma}\right)$.

Spatial pooling usually operates on a small region which limits its capability to capture long-range dependencies and focus on distant regions. To overcome this, Hou et al. proposed strip pooling, a novel pooling method capable of encoding long-range context in either horizontal or vertical spatial domains. Strip pooling has two branches for horizontal and vertical strip pooling. The horizontal strip pooling part first pools the input feature $F \in \mathcal{R}^{C \times H \times W}$ in the horizontal direction: \begin{align} y^1 = \text{GAP}^w (X) \end{align} Then a 1D convolution with kernel size 3 is applied in $y$ to capture the relationship between different rows and channels. This is repeated $W$ times to make the output $y_v$ consistent with the input shape: \begin{align} y_h = \text{Expand}(\text{Conv1D}(y^1)) \end{align} Vertical strip pooling is performed in a similar way. Finally, the outputs of the two branches are fused using element-wise summation to produce the attention map: \begin{align} s &= \sigma(Conv^{1\times 1}(y_{v} + y_{h})) \end{align} \begin{align} Y &= s X \end{align} The strip pooling module (SPM) is further developed in the mixed pooling module (MPM). Both consider spatial and channel relationships to overcome the locality of convolutional neural networks. SPNet achieves state-of-the-art results for several complex semantic segmentation benchmarks.

BASNet, or Boundary-Aware Segmentation Network, is an image segmentation architecture that consists of a predict-refine architecture and a hybrid loss. The proposed BASNet comprises a predict-refine architecture and a hybrid loss, for highly accurate image segmentation. The predict-refine architecture consists of a densely supervised encoder-decoder network and a residual refinement module, which are respectively used to predict and refine a segmentation probability map. The hybrid loss is a combination of the binary cross entropy, structural similarity and intersection-over-union losses, which guide the network to learn three-level (i.e., pixel-, patch- and map- level) hierarchy representations.

CRF-RNN is a formulation of a CRF as a Recurrent Neural Network. Specifically it formulates mean-field approximate inference for the Conditional Random Fields with Gaussian pairwise potentials as Recurrent Neural Networks.

DeepLabv2 is an architecture for semantic segmentation that build on DeepLab with an atrous spatial pyramid pooling scheme. Here we have parallel dilated convolutions with different rates applied in the input feature map, which are then fused together. As objects of the same class can have different sizes in the image, ASPP helps to account for different object sizes.

Deformable RoI Pooling adds an offset to each bin position in the regular bin partition of the RoI Pooling. Similarly, the offsets are learned from the preceding feature maps and the RoIs, enabling adaptive part localization for objects with different shapes.

The focal self-attention is built to make Transformer layers scalable to high-resolution inputs. Instead of attending all tokens at fine-grain, the approach attends the fine-grain tokens only locally, but the summarized ones globally. As such, it can cover as many regions as standard self-attention but with much less cost. An image is first partitioned into patches, resulting in visual tokens. Then a patch embedding layer, consisting of a convolutional layer with filter and stride of same size, to project the patches into hidden features. This spatial feature map in then passed to four stages of focal Transformer blocks. Each focal Transformer block consists of $N_i$ focal Transformer layers. Patch embedding layers are used in between to reduce spatial size of feature map by factor 2, while feature dimension increased by 2.

Local Patch Interaction, or LPI, is a module used for the XCiT layer to enable explicit communication across patches. LPI consists of two depth-wise 3×3 convolutional layers with Batch Normalization and GELU non-linearity in between. Due to its depth-wise structure, the LPI block has a negligible overhead in terms of parameters, as well as a limited overhead in terms of throughput and memory usage during inference.

Pyramid Vision Transformer v2 (PVTv2) is a type of Vision Transformer for detection and segmentation tasks. It improves on PVTv1 through several design improvements: (1) overlapping patch embedding, (2) convolutional feed-forward networks, and (3) linear complexity attention layers that are orthogonal to the PVTv1 framework.

STDC, or Short-Term Dense Concatenate, is a module for semantic segmentation to extract deep features with scalable receptive field and multi-scale information. It aims to remove structure redundancy in the BiSeNet architecture, specifically BiSeNet adds an extra path to encode spatial information which can be time-consuming,. Instead, STDC gradually reduces the dimension of feature maps and use the aggregation of them for image representation. We concatenate response maps from multiple continuous layers, each of which encodes input image/feature in different scales and respective fields, leading to multi-scale feature representation. To speed up, the filter size of layers is gradually reduced with negligible loss in segmentation performance.

Cross-Covariance Image Transformers, or XCiT, is a type of vision transformer that aims to combine the accuracy of conventional transformers with the scalability of convolutional architectures. The self-attention operation underlying transformers yields global interactions between all tokens, i.e. words or image patches, and enables flexible modelling of image data beyond the local interactions of convolutions. This flexibility, however, comes with a quadratic complexity in time and memory, hindering application to long sequences and high-resolution images. The authors propose a “transposed” version of self-attention called cross-covariance attention that operates across feature channels rather than tokens, where the interactions are based on the cross-covariances matrix between keys and queries.

An XCiT Layer is the main building block of the XCiT architecture which uses a cross-covariance attention operator as its principal operation. The XCiT layer consists of three main blocks, each preceded by LayerNorm and followed by a residual connection: (i) the core cross-covariance attention (XCA) operation, (ii) the local patch interaction (LPI) module, and (iii) a feed-forward network (FFN). By transposing the query-key interaction, the computational complexity of XCA is linear in the number of data elements N, rather than quadratic as in conventional self-attention.

To aggregate global spatial information, an SE block applies global pooling to the feature map. However, it ignores pixel-wise spatial information, which is important in dense prediction tasks. Therefore, Roy et al. proposed spatial and channel SE blocks (scSE). Like BAM, spatial SE blocks are used, complementing SE blocks, to provide spatial attention weights to focus on important regions. Given the input feature map $X$, two parallel modules, spatial SE and channel SE, are applied to feature maps to encode spatial and channel information respectively. The channel SE module is an ordinary SE block, while the spatial SE module adopts $1\times 1$ convolution for spatial squeezing. The outputs from the two modules are fused. The overall process can be written as \begin{align} s_c & = \sigma (W_{2} \delta (W_{1}\text{GAP}(X))) \end{align} \begin{align} X_\text{chn} & = s_c X \end{align} \begin{align} s_s &= \sigma(\text{Conv}^{1\times 1}(X)) \end{align} \begin{align} X_\text{spa} & = s_s X \end{align} \begin{align} Y &= f(X_\text{spa},X_\text{chn}) \end{align} where $f$ denotes the fusion function, which can be maximum, addition, multiplication or concatenation. The proposed scSE block combines channel and spatial attention to enhance features as well as capturing pixel-wise spatial information. Segmentation tasks are greatly benefited as a result. The integration of an scSE block in F-CNNs makes a consistent improvement in semantic segmentation at negligible extra cost.

CBNet is a backbone architecture that consists of multiple identical backbones (specially called Assistant Backbones and Lead Backbone) and composite connections between neighbor backbones. From left to right, the output of each stage in an Assistant Backbone, namely higher-level features, flows to the parallel stage of the succeeding backbone as part of inputs through composite connections. Finally, the feature maps of the last backbone named Lead Backbone are used for object detection. The features extracted by CBNet for object detection fuse the high-level and low-level features of multiple backbones, hence improve the detection performance.

Inspired on the widely known spatial squeeze and channel excitation (SE) block, the sSE block performs channel squeeze and spatial excitation, to recalibrate the feature maps spatially and achieve more fine-grained image segmentation.

An EESP Unit, or Extremely Efficient Spatial Pyramid of Depth-wise Dilated Separable Convolutions, is an image model block designed for edge devices. It was proposed as part of the ESPNetv2 CNN architecture. This building block is based on a reduce-split-transform-merge strategy. The EESP unit first projects the high-dimensional input feature maps into low-dimensional space using groupwise pointwise convolutions and then learns the representations in parallel using depthwise dilated separable convolutions with different dilation rates. Different dilation rates in each branch allow the EESP unit to learn the representations from a large effective receptive field. To remove the gridding artifacts caused by dilated convolutions, the EESP fuses the feature maps using hierarchical feature fusion (HFF).

Flow Alignment Module, or FAM, is a flow-based align module for scene parsing to learn Semantic Flow between feature maps of adjacent levels and broadcast high-level features to high resolution features effectively and efficiently. The concept of Semantic Flow is inspired from optical flow, which is widely used in video processing task to represent the pattern of apparent motion of objects, surfaces, and edges in a visual scene caused by relative motion. The authors postulate that the relatinship between two feature maps of arbitrary resolutions from the same image can also be represented with the “motion” of every pixel from one feature map to the other one. Once precise Semantic Flow is obtained, the network is able to propagate semantic features with minimal information loss. In the FAM module, the transformed high-resolution feature map are combined with the low-resolution feature map to generate the semantic flow field, which is utilized to warp the low-resolution feature map to high-resolution feature map.

GridMask is a data augmentation method that randomly removes some pixels of an input image. Unlike other methods, the region that the algorithm removes is neither a continuous region nor random pixels in dropout. Instead, the algorithm removes a region with disconnected pixel sets, as shown in the Figure. We express the setting as $$ \tilde{\mathbf{x}}=\mathbf{x} \times M $$ where $\mathbf{x} \in R^{H \times W \times C}$ represents the input image, $M \in$ $\{0,1\}^{H \times W}$ is the binary mask that stores pixels to be removed, and $\tilde{\mathbf{x}} \in R^{H \times W \times C}$ is the result produced by the algorithm. For the binary mask $M$, if $M_{i, j}=1$ we keep pixel $(i, j)$ in the input image; otherwise we remove it. GridMask is applied after the image normalization operation. The shape of $M$ looks like a grid, as shown in the Figure . Four numbers $\left(r, d, \delta_{x}, \delta_{y}\right)$ are used to represent a unique $M$. Every mask is formed by tiling the units. $r$ is the ratio of the shorter gray edge in a unit. $d$ is the length of one unit. $\delta\_{x}$ and $\delta\_{y}$ are the distances between the first intact unit and boundary of the image.

Online Normalization is a normalization technique for training deep neural networks. To define Online Normalization. we replace arithmetic averages over the full dataset in with exponentially decaying averages of online samples. The decay factors $\alpha\_{f}$ and $\alpha\_{b}$ for forward and backward passes respectively are hyperparameters for the technique. We allow incoming samples $x\_{t}$, such as images, to have multiple scalar components and denote feature-wide mean and variance by $\mu\left(x\_{t}\right)$ and $\sigma^{2}\left(x\_{t}\right)$. The algorithm also applies to outputs of fully connected layers with only one scalar output per feature. In fact, this case simplifies to $\mu\left(x\_{t}\right) = x\_{t}$ and $\sigma\left(x\_{t}\right) = 0$. Denote scalars $\mu\_{t}$ and $\sigma\_{t}$ to denote running estimates of mean and variance across all samples. The subscript $t$ denotes time steps corresponding to processing new incoming samples. Online Normalization uses an ongoing process during the forward pass to estimate activation means and variances. It implements the standard online computation of mean and variance generalized to processing multi-value samples and exponential averaging of sample statistics. The resulting estimates directly lead to an affine normalization transform. $$ y\_{t} = \frac{x\_{t} - \mu\_{t-1}}{\sigma\_{t-1}} $$ $$ \mu\_{t} = \alpha\_{f}\mu\_{t-1} + \left(1-\alpha\_{f}\right)\mu\left(x\_{t}\right) $$ $$ \sigma^{2}\_{t} = \alpha\_{f}\sigma^{2}\_{t-1} + \left(1-\alpha\_{f}\right)\sigma^{2}\left(x\_{t}\right) + \alpha\_{f}\left(1-\alpha\_{f}\right)\left(\mu\left(x\_{t}\right) - \mu\_{t-1}\right)^{2} $$

A Panoptic FPN is an extension of an FPN that can generate both instance and semantic segmentations via FPN. The approach starts with an FPN backbone and adds a branch for performing semantic segmentation in parallel with the existing region-based branch for instance segmentation. No changes are made to the FPN backbone when adding the dense-prediction branch, making it compatible with existing instance segmentation methods. The new semantic segmentation branch achieves its goal as follows. Starting from the deepest FPN level (at 1/32 scale), we perform three upsampling stages to yield a feature map at 1/4 scale, where each upsampling stage consists of 3×3 convolution, group norm, ReLU, and 2× bilinear upsampling. This strategy is repeated for FPN scales 1/16, 1/8, and 1/4 (with progressively fewer upsampling stages). The result is a set of feature maps at the same 1/4 scale, which are then element-wise summed. A final 1×1 convolution, 4× bilinear upsampling, and softmax are used to generate the per-pixel class labels at the original image resolution. In addition to stuff classes, this branch also outputs a special ‘other’ class for all pixels belonging to objects (to avoid predicting stuff classes for such pixels).

PolarMask is an anchor-box free and single-shot instance segmentation method. Specifically, PolarMask takes an image as input and predicts the distance from a sampled positive location (ie a candidate object's center) with respect to the object's contour at each angle, and then assembles the predicted points to produce the final mask. There are several benefits to the system: (1) The polar representation unifies instance segmentation (masks) and object detection (bounding boxes) into a single framework (2) Two modules are designed (i.e. soft polar centerness and polar IoU loss) to sample high-quality center examples and optimize polar contour regression, making the performance of PolarMask does not depend on the bounding box prediction results and more efficient in training. (3) PolarMask is fully convolutional and can be embedded into most off-the-shelf detection methods.

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Seesaw Loss is a loss function for long-tailed instance segmentation. It dynamically re-balances the gradients of positive and negative samples on a tail class with two complementary factors: mitigation factor and compensation factor. The mitigation factor reduces punishments to tail categories w.r.t the ratio of cumulative training instances between different categories. Meanwhile, the compensation factor increases the penalty of misclassified instances to avoid false positives of tail categories. The synergy of the two factors enables Seesaw Loss to mitigate the overwhelming punishments on tail classes as well as compensate for the risk of misclassification caused by diminished penalties. $$ L\_{seesaw}\left(\mathbf{x}\right) = - \sum^{C}\_{i=1}y\_{i}\log\left(\hat{\sigma}\_{i}\right) $$ $$ \text{with } \hat{\sigma\_{i}} = \frac{e^{z\_{i}}}{- \sum^{C}\_{j\neq{1}}\mathcal{S}\_{ij}e^{z\_{j}}+e^{z\_{i}} } $$ Here $\mathcal{S}\_{ij}$ works as a tunable balancing factor between different classes. By a careful design of $\mathcal{S}\_{ij}$, Seesaw loss adjusts the punishments on class j from positive samples of class $i$. Seesaw loss determines $\mathcal{S}\_{ij}$ by a mitigation factor and a compensation factor, as: $$ \mathcal{S}\_{ij} =\mathcal{M}\_{ij} · \mathcal{C}\_{ij} $$ The mitigation factor $\mathcal{M}\_{ij}$ decreases the penalty on tail class $j$ according to a ratio of instance numbers between tail class $j$ and head class $i$. The compensation factor $\mathcal{C}\_{ij}$ increases the penalty on class $j$ whenever an instance of class $i$ is misclassified to class $j$.

VisTR is a Transformer based video instance segmentation model. It views video instance segmentation as a direct end-to-end parallel sequence decoding/prediction problem. Given a video clip consisting of multiple image frames as input, VisTR outputs the sequence of masks for each instance in the video in order directly. At the core is a new, effective instance sequence matching and segmentation strategy, which supervises and segments instances at the sequence level as a whole. VisTR frames the instance segmentation and tracking in the same perspective of similarity learning, thus considerably simplifying the overall pipeline and is significantly different from existing approaches.

Atrous Convolution Neural Network (ACNN), as a pooling-free network structure, is proposed to achieve full-resolution feature processing using a theoretically optimal dilation setting for a larger receptive field, even with fewer parameters. Compared to other techniques, it can achieve higher segmentation Intersection over Union (IoU) and much less trainable parameters and model sizes, indicating the benefit of full-resolution feature maps in feature processing.

Adaptive Early-Learning Correction for Segmentation from Noisy Annotations

BiSeNet V2 is a two-pathway architecture for real-time semantic segmentation. One pathway is designed to capture the spatial details with wide channels and shallow layers, called Detail Branch. In contrast, the other pathway is introduced to extract the categorical semantics with narrow channels and deep layers, called Semantic Branch. The Semantic Branch simply requires a large receptive field to capture semantic context, while the detail information can be supplied by the Detail Branch. Therefore, the Semantic Branch can be made very lightweight with fewer channels and a fast-downsampling strategy. Both types of feature representation are merged to construct a stronger and more comprehensive feature representation.

Bilateral Guided Aggregation Layer is a feature fusion layer for semantic segmentation that aims to enhance mutual connections and fuse different types of feature representation. It was used in the BiSeNet V2 architecture. Specifically, within the BiSeNet implementation, the layer was used to employ the contextual information of the Semantic Branch to guide the feature response of Detail Branch. With different scale guidance, different scale feature representations can be captured, which inherently encodes the multi-scale information.

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CascadePSP is a general segmentation refinement model that refines any given segmentation from low to high resolution. The model takes as input an initial mask that can be an output of any algorithm to provide a rough object location. Then the CascadePSP will output a refined mask. The model is designed in a cascade fashion that generates refined segmentation in a coarse-to-fine manner. Coarse outputs from the early levels predict object structure which will be used as input to the latter levels to refine boundary details.