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Dynamic Keypoint Head

Dynamic Keypoint Head is an output head for pose estimation that are conditioned on each instance (person), and can encode the instance concept in the dynamically-generated weights of their filters. They are used in the FCPose architecture. The Figure shows the core idea. $F$ denotes a level of feature maps. "Rel. Coord." means the relative coordinates, denoting the relative offsets from the locations of $F$ to the location where the filters are generated. Refer to the text for details. $f\_{\theta\_{i}}$ is the dynamically-generated keypoint head for the $i$-th person instance. Note that each person instance has its own keypoint head.

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Exact Fusion Model

Exact Fusion Model (EFM) is a method for aggregating a feature pyramid. The EFM is based on YOLOv3, which assigns exactly one bounding-box prior to each ground truth object. Each ground truth bounding box corresponds to one anchor box that surpasses the threshold IoU. If the size of an anchor box is equivalent to the field-of-view of the grid cell, then for the grid cells of the $s$-th scale, the corresponding bounding box will be lower bounded by the $(s − 1)$th scale and upper bounded by the (s + 1)th scale. Therefore, the EFM assembles features from the three scales.

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FCPose

FCPose is a fully convolutional multi-person pose estimation framework using dynamic instance-aware convolutions. Different from existing methods, which often require ROI (Region of Interest) operations and/or grouping post-processing, FCPose eliminates the ROIs and grouping pre-processing with dynamic instance aware keypoint estimation heads. The dynamic keypoint heads are conditioned on each instance (person), and can encode the instance concept in the dynamically-generated weights of their filters. Overall, FCPose is built upon the one-stage object detector FCOS. The controller that generates the weights of the keypoint heads is attached to the FCOS heads. The weights $\theta\_{i}$ generated by the controller is used to fulfill the keypoint head $f$ for the instance $i$. Moreover, a keypoint refinement module is introduced to predict the offsets from each location of the heatmaps to the ground-truth keypoints. Finally, the coordinates derived from the predicted heatmaps are refined by the offsets predicted by the keypoint refinement module, resulting in the final keypoint results. "Rel. coord." is a map of the relative coordinates from all the locations of the feature maps $F$ to the location where the weights are generated. The relative coordinate map is concatenated to $F$ as the input to the keypoint head.

FCPose: Fully Convolutional Multi-Person Pose Estimation with Dynamic Instance-Aware Convolutions

FFB6D

FFB6D is a full flow bidirectional fusion network for 6D pose estimation of known objects from a single RGBD image. Unlike previous works that extract the RGB and point cloud features independently and fuse them in the final stage, FFB6D builds bidirectional fusion modules as communication bridges in the full flow of the two networks. In this way, the two networks can obtain complementary information from the other and learn representations containing rich appearance and geometry information of the scene.

FFB6D: A Full Flow Bidirectional Fusion Network for 6D Pose Estimation

Feature Intertwiner

Feature Intertwiner is an object detection module that leverages the features from a more reliable set to help guide the feature learning of another less reliable set. The mutual learning process helps two sets to have closer distance within the cluster in each class. The intertwiner is applied on the object detection task, where a historical buffer is proposed to address the sample missing problem during one mini-batch and the optimal transport (OT) theory is introduced to enforce the similarity among the two sets.

Feature Intertwiner for Object Detection

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GHM-C

GHM-C is a loss function designed to balance the gradient flow for anchor classification. The GHM first performs statistics on the number of examples with similar attributes w.r.t their gradient density and then attaches a harmonizing parameter to the gradient of each example according to the density. The modification of gradient can be equivalently implemented by reformulating the loss function. Embedding the GHM into the classification loss is denoted as GHM-C loss. Since the gradient density is a statistical variable depending on the examples distribution in a mini-batch, GHM-C is a dynamic loss that can adapt to the change of data distribution in each batch as well as to the updating of the model.

Gradient Harmonized Single-stage Detector

GHM-R

GHM-R is a loss function designed to balance the gradient flow for bounding box refinement. The GHM first performs statistics on the number of examples with similar attributes w.r.t their gradient density and then attaches a harmonizing parameter to the gradient of each example according to the density. The modification of gradient can be equivalently implemented by reformulating the loss function. Embedding the GHM into the bounding box regression branch is denoted as GHM-R loss.

Gradient Harmonized Single-stage Detector

Gumbel Activation

Gumbel activation function, is defined using the cumulative Gumbel distribution and it can be used to perform Gumbel regression. Gumbel activation is an alternative activation function to the sigmoid or softmax activation functions and can be used to transform the unormalised output of a model to probability. Gumbel activation $\eta_{Gumbel}$ is defined as follows: $\eta_{Gumbel}(q_i) = exp(-exp(-q_i))$ It can be combined with Cross Entropy loss function to solve long-tailed classification problems. Gumbel Cross Entropy (GCE) is defined as follows: $GCE(\eta_{Gumbel}(q_i),y_i) = -y_i \log(\eta_{Gumbel}(q_i))+ (1-y_i) \log(1-\eta_{Gumbel}(q_i))$

Long-tailed Instance Segmentation using Gumbel Optimized Loss

HITNet

HITNet is a framework for neural network based depth estimation which overcomes the computational disadvantages of operating on a 3D volume by integrating image warping, spatial propagation and a fast high resolution initialization step into the network architecture, while keeping the flexibility of a learned representation by allowing features to flow through the network. The main idea of the approach is to represent image tiles as planar patches which have a learned compact feature descriptor attached to them. The basic principle of the approach is to fuse information from the high resolution initialization and the current hypotheses using spatial propagation. The propagation is implemented via a convolutional neural network module that updates the estimate of the planar patches and their attached features. In order for the network to iteratively increase the accuracy of the disparity predictions, the network is provided a local cost volume in a narrow band (±1 disparity) around the planar patch using in-network image warping allowing the network to minimize image dissimilarity. To reconstruct fine details while also capturing large texture-less areas we start at low resolution and hierarchically upsample predictions to higher resolution. A critical feature of the architecture is that at each resolution, matches from the initialization module are provided to facilitate recovery of thin structures that cannot be represented at low resolution.

HITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo Matching

Hierarchical-Split Block

Hierarchical-Split Block is a representational block for multi-scale feature representations. It contains many hierarchical split and concatenate connections within one single residual block. Specifically, ordinary feature maps in deep neural networks are split into $s$ groups, each with $w$ channels. As shown in the Figure, only the first group of filters can be straightly connected to next layer. The second group of feature maps are sent to a convolution of $3 \times 3$ filters to extract features firstly, then the output feature maps are split into two sub-groups in the channel dimension. One sub-group of feature maps straightly connected to next layer, while the other sub-group is concatenated with the next group of input feature maps in the channel dimension. The concatenated feature maps are operated by a set of $3 \times 3$ convolutional filters. This process repeats several times until the rest of input feature maps are processed. Finally, features maps from all input groups are concatenated and sent to another layer of $1 \times 1$ filters to rebuild the features.

HS-ResNet: Hierarchical-Split Block on Convolutional Neural Network

Hit-Detector

Hit-Detector is a neural architectures search algorithm that simultaneously searches all components of an object detector in an end-to-end manner. It is a hierarchical approach to mine the proper subsearch space from the large volume of operation candidates. It consists of two main procedures. First, given a large search space containing all the operation candidates, we screen out the customized sub search space suitable for each part of detector with the help of group sparsity regularization. Secondly, we search the architectures for each part within the corresponding sub search space by adopting the differentiable manner.

Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection

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