Panoptic Segmentation

Region of Interest Align, or RoIAlign, is an operation for extracting a small feature map from each RoI in detection and segmentation based tasks. It removes the harsh quantization of RoI Pool, properly aligning the extracted features with the input. To avoid any quantization of the RoI boundaries or bins (using $x/16$ instead of $[x/16]$), RoIAlign uses bilinear interpolation to compute the exact values of the input features at four regularly sampled locations in each RoI bin, and the result is then aggregated (using max or average).

Mask R-CNN extends Faster R-CNN to solve instance segmentation tasks. It achieves this by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. In principle, Mask R-CNN is an intuitive extension of Faster R-CNN, but constructing the mask branch properly is critical for good results. Most importantly, Faster R-CNN was not designed for pixel-to-pixel alignment between network inputs and outputs. This is evident in how RoIPool, the de facto core operation for attending to instances, performs coarse spatial quantization for feature extraction. To fix the misalignment, Mask R-CNN utilises a simple, quantization-free layer, called RoIAlign, that faithfully preserves exact spatial locations. Secondly, Mask R-CNN decouples mask and class prediction: it predicts a binary mask for each class independently, without competition among classes, and relies on the network's RoI classification branch to predict the category. In contrast, an FCN usually perform per-pixel multi-class categorization, which couples segmentation and classification.

PnP, or Poll and Pool, is sampling module extension for DETR-type architectures that adaptively allocates its computation spatially to be more efficient. Concretely, the PnP module abstracts the image feature map into fine foreground object feature vectors and a small number of coarse background contextual feature vectors. The transformer models information interaction within the fine-coarse feature space and translates the features into the detection result.

Hierarchical Feature Fusion (HFF) is a feature fusion method employed in ESP and EESP image model blocks for degridding. In the ESP module, concatenating the outputs of dilated convolutions gives the ESP module a large effective receptive field, but it introduces unwanted checkerboard or gridding artifacts. To address the gridding artifact in ESP, the feature maps obtained using kernels of different dilation rates are hierarchically added before concatenating them (HFF). This solution is simple and effective and does not increase the complexity of the ESP module.

ESPNet is a convolutional neural network for semantic segmentation of high resolution images under resource constraints. ESPNet is based on a convolutional module, efficient spatial pyramid (ESP), which is efficient in terms of computation, memory, and power.

A ResNest is a variant on a ResNet, which instead stacks Split-Attention blocks. The cardinal group representations are then concatenated along the channel dimension: $V = \text{Concat}${$V^{1},V^{2},\cdots{V}^{K}$}. As in standard residual blocks, the final output $Y$ of otheur Split-Attention block is produced using a shortcut connection: $Y=V+X$, if the input and output feature-map share the same shape. For blocks with a stride, an appropriate transformation $\mathcal{T}$ is applied to the shortcut connection to align the output shapes: $Y=V+\mathcal{T}(X)$. For example, $\mathcal{T}$ can be strided convolution or combined convolution-with-pooling.

A Spatial Attention-Guided Mask is a module for instance segmentation that predicts a segmentation mask on each detected box with a spatial attention map that helps to focus on informative pixels and suppress noise. The goal is to guide the mask head for spotlighting meaningful pixels and repressing uninformative ones. Once features inside the predicted RoIs are extracted by RoIAlign with 14×14 resolution, those features are fed into four conv layers and the spatial attention module (SAM) sequentially. To exploit the spatial attention map $A\_{sag}\left(X\_{i}\right) \in \mathcal{R}^{1\times{W}\times{H}}$ as a feature descriptor given input feature map $X\_{i} \in \mathcal{R}^{C×W×H}$, the SAM first generates pooled features $P\_{avg}, P\_{max} \in \mathcal{R}^{1\times{W}\times{H}}$ by both average and max pooling operations respectively along the channel axis and aggregates them via concatenation. Then it is followed by a 3 × 3 conv layer and normalized by the sigmoid function. The computation process is summarized as follow: $$ A\_{sag}\left(X\_{i}\right) = \sigma\left(F\_{3\times{3}}(P\_{max} \cdot P\_{avg})\right) $$ where $\sigma$ denotes the sigmoid function, $F\_{3\times{3}}$ is 3 × 3 conv layer and $\cdot$ represents the concatenate operation. Finally, the attention guided feature map $X\_{sag} ∈ \mathcal{R}^{C\times{W}\times{H}}$ is computed as: $$ X\_{sag} = A\_{sag}\left(X\_{i}\right) \otimes X\_{i} $$ where ⊗ denotes element-wise multiplication. After then, a 2 × 2 deconv upsamples the spatially attended feature map to 28 × 28 resolution. Lastly, a 1 × 1 conv is applied for predicting class-specific masks.

K-Net is a framework for unified semantic and instance segmentation that segments both instances and semantic categories consistently by a group of learnable kernels, where each kernel is responsible for generating a mask for either a potential instance or a stuff class. It begins with a set of kernels that are randomly initialized, and learns the kernels in accordance to the segmentation targets at hand, namely, semantic kernels for semantic categories and instance kernels for instance identities. A simple combination of semantic kernels and instance kernels allows panoptic segmentation naturally. In the forward pass, the kernels perform convolution on the image features to obtain the corresponding segmentation predictions. K-Net is formulated so that it dynamically updates the kernels to make them conditional to their activations on the image. Such a content-aware mechanism is crucial to ensure that each kernel, especially an instance kernel, responds accurately to varying objects in an image. Through applying this adaptive kernel update strategy iteratively, K-Net significantly improves the discriminative ability of the kernels and boosts the final segmentation performance. It is noteworthy that this strategy universally applies to kernels for all the segmentation tasks. It also utilises a bipartite matching strategy to assign learning targets for each kernel. This training approach is advantageous to conventional training strategies as it builds a one-to-one mapping between kernels and instances in an image. It thus resolves the problem of dealing with a varying number of instances in an image. In addition, it is purely mask-driven without involving boxes. Hence, K-Net is naturally NMS-free and box-free, which is appealing to real-time applications.

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 $\left.\_{i, j}\right)=\exp \left(-\frac{i o u\_{i, j}^{2}}{\sigma}\right)$.

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.

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).

One-Shot Aggregation with an Identity Mapping and eSE is an image model block that extends one-shot aggregation with a residual connection and effective squeeze-and-excitation block. It is proposed as part of the VoVNetV2 CNN architecture. The module adds an identity mapping to the OSA module - the input path is connected to the end of an OSA module that is able to backpropagate the gradients of every OSA module in an end-to-end manner on each stage like a ResNet. Additionally, a channel attention module - effective Squeeze-Excitation - is used which is like regular squeeze-and-excitation but uses only one FC layer with $C$ channels instead of two FCs without a channel dimension reduction, which maintains channel information.

Video Panoptic Segmentation Network, or VPSNet, is a model for video panoptic segmentation. On top of UPSNet, which is a method for image panoptic segmentation, VPSNet is designed to take an additional frame as the reference to correlate time information at two levels: pixel-level fusion and object-level tracking. To pick up the complementary feature points in the reference frame, a flow-based feature map alignment module is introduced along with an asymmetric attention block that computes similarities between the target and reference features to fuse them into one-frame shape. Additionally, to associate object instances across time, an object track head is added which learns the correspondence between the instances in the target and reference frames based on their RoI feature similarity.

VoVNetV2 is a convolutional neural network that improves upon VoVNet with two effective strategies: (1) residual connection for alleviating the optimization problem of larger VoVNets and (2) effective Squeeze-Excitation (eSE) dealing with the channel information loss problem of the original squeeze-and-excitation module.