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Optimize combinations of various neural network models for multimodal data with bayseian optimization.

IoU-guided NMS is a type of non-maximum suppression that help to eliminate the suppression failure caused by the misleading classification confidences. This is achieved through using the predicted IoU instead of the classification confidence as the ranking keyword for bounding boxes.

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Method that adapts expectation-maximization (EM) to aggregate annotations that work on tasks with combined localization and classification like object detection and instance segmentation.

Local Importance-based Pooling (LIP) is a pooling layer that can enhance discriminative features during the downsampling procedure by learning adaptive importance weights based on inputs. By using a learnable network $G$ in $F$, the importance function now is not limited in hand-crafted forms and able to learn the criterion for the discriminativeness of features. Also, the window size of LIP is restricted to be not less than stride to fully utilize the feature map and avoid the issue of fixed interval sampling scheme. More specifically, the importance function in LIP is implemented by a tiny fully convolutional network, which learns to produce the importance map based on inputs in an end-to-end manner.

MobileViTv3 is a vision transformer that is tuned to mobile device. MobileViTv3 introduced an improved fusion block to reduce cost than MobileViT

OA-Loss reduces the domain gap between the original and augmented images. OA-Loss enables the model to learn semantic relations of foreground and background instances from multi-domain.

OA-Mix is a general and effective data augmentation method for single-domain generalization in object detection. It increases image diversity while preserving important semantic features with multi-level transformations and object-aware mixing.

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.

PAFNet is an anchor-free detector for object detection that removes pre-defined anchors and regresses the locations directly, which can achieve higher efficiency. The overall network is composed of a backbone, an up-sampling module, an AGS module, a localization branch and a regression branch. Specifically, ResNet50-vd is chosen as the backbone for server side, and MobileNetV3 for mobile side. Besides, for mobile side, we replace traditional convolution layers with lite convolution operators.

PIoU Loss is a loss function for oriented object detection which is formulated to exploit both the angle and IoU for accurate oriented bounding box regression. The PIoU loss is derived from IoU metric with a pixel-wise form.

Prediction-aware One-To-One, or POTO, is an assignment rule for object detection which dynamically assigns the foreground samples according to the quality of classification and regression simultaneously.

PQ-Transformer, or PointQuad-Transformer, is a Transformer-based architecture that predicts 3D objects and layouts simultaneously, using point cloud inputs. Unlike existing methods that either estimate layout keypoints or edges, room layouts are directly parameterized as a set of quads. Along with the quad representation, a physical constraint loss function is used that discourages object-layout interference. Given an input 3D point cloud of $N$ points, the point cloud feature learning backbone extracts $M$ context-aware point features of $\left(3+C\right)$ dimensions, through sampling and grouping. A voting module and a farthest point sampling (FPS) module are used to generate $K\_{1}$ object proposals and $K\_{2}$ quad proposals respectively. Then the proposals are processed by a transformer decoder to further refine proposal features. Through several feedforward layers and non-maximum suppression (NMS), the proposals become the final object bounding boxes and layout quads.

PixLoc is a scene-agnostic neural network that estimates an accurate 6-DoF pose from an image and a 3D model. It is based on the direct alignment of multiscale deep features, casting camera localization as metric learning. PixLoc learns strong data priors by end-to-end training from pixels to pose and exhibits exceptional generalization to new scenes by separating model parameters and scene geometry. As the CNN never sees 3D points, PixLoc can generalize to any 3D structure available. This includes sparse SfM point clouds, dense depth maps from stereo or RGBD sensors, meshes, Lidar scans, but also lines and other primitives.

Point-GNN is a graph neural network for detecting objects from a LiDAR point cloud. It predicts the category and shape of the object that each vertex in the graph belongs to. In Point-GNN, there is an auto-registration mechanism to reduce translation variance, as well as a box merging and scoring operation to combine detections from multiple vertices accurately.

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A method to disentangle pose from other factors in a scene.

Probabilistic anchor assignment (PAA) adaptively separates a set of anchors into positive and negative samples for a GT box according to the learning status of the model associated with it. To do so we first define a score of a detected bounding box that reflects both the classification and localization qualities. We then identify the connection between this score and the training objectives and represent the score as the combination of two loss objectives. Based on this scoring scheme, we calculate the scores of individual anchors that reflect how the model finds useful cues to detect a target object in each anchor. With these anchor scores, we aim to find a probability distribution of two modalities that best represents the scores as positive or negative samples as in the Figure. Under the found probability distribution, anchors with probabilities from the positive component are high are selected as positive samples. This transforms the anchor assignment problem to a maximum likelihood estimation for a probability distribution where the parameters of the distribution is determined by anchor scores. Based on the assumption that anchor scores calculated by the model are samples drawn from a probability distribution, it is expected that the model can infer the sample separation in a probabilistic way, leading to easier training of the model compared to other non-probabilistic assignments. Moreover, since positive samples are adaptively selected based on the anchor score distribution, it does not require a pre-defined number of positive samples nor an IoU threshold.

RegionViT consists of two tokenization processes that convert an image into regional (upper path) and local tokens (lower path). Each tokenization is a convolution with different patch sizes, the patch size of regional tokens is $28^2$ while $4^2$ is used for local tokens with dimensions projected to $C$, which means that one regional token covers $7^2$ local tokens based on the spatial locality, leading to the window size of a local region to $7^2$. At stage 1, two set of tokens are passed through the proposed regional-to-local transformer encoders. However, for the later stages, to balance the computational load and to have feature maps at different resolution, the approach uses a downsampling process to halve the spatial resolution while doubling the channel dimension like CNN on both regional and local tokens before going to the next stage. Finally, at the end of the network, it simply averages the remaining regional tokens as the final embedding for the classification while the detection uses all local tokens at each stage since it provides more fine-grained location information. By having the pyramid structure, the ViT can generate multi-scale features and hence it could be easily extended to more vision applications, e.g., object detection, rather than image classification only.

RetinaMask is a one-stage object detection method that improves upon RetinaNet by adding the task of instance mask prediction during training, as well as an adaptive loss that improves robustness to parameter choice during training, and including more difficult examples in training.

Invertible multi-input multi-output coupling module. In RevBiFPN it is used as a bidirectional multi-scale feature pyramid fusion module that is invertible.

Region of Interest Warping, or RoIWarp, is a form of RoIPool that is differentiable with respect to the box position. In practice, this takes the form of a RoIWarp layer followed by a standard Max Pooling layer. The RoIWarp layer crops a feature map region and warps it into a target size by interpolation.

SPP-Net is a convolutional neural architecture that employs spatial pyramid pooling to remove the fixed-size constraint of the network. Specifically, we add an SPP layer on top of the last convolutional layer. The SPP layer pools the features and generates fixed-length outputs, which are then fed into the fully-connected layers (or other classifiers). In other words, we perform some information aggregation at a deeper stage of the network hierarchy (between convolutional layers and fully-connected layers) to avoid the need for cropping or warping at the beginning.

Self-Adjusting Smooth L1 Loss is a loss function used in object detection that was introduced with RetinaMask. This is an improved version of Smooth L1. For Smooth L1 loss we have: $$ f(x) = 0.5 \frac{x^{2}}{\beta} \text{ if } |x| < \beta $$ $$ f(x) = |x| -0.5\beta \text{ otherwise } $$ Here a point $\beta$ splits the positive axis range into two parts: $L2$ loss is used for targets in range $[0, \beta]$, and $L1$ loss is used beyond $\beta$ to avoid over-penalizing utliers. The overall function is smooth (continuous, together with its derivative). However, the choice of control point ($\beta$) is heuristic and is usually done by hyper parameter search. Instead, with self-adjusting smooth L1 loss, inside the loss function the running mean and variance of the absolute loss are recorded. We use the running minibatch mean and variance with a momentum of $0.9$ to update these two parameters.

The Shuffle Transformer Block consists of the Shuffle Multi-Head Self-Attention module (ShuffleMHSA), the Neighbor-Window Connection module (NWC), and the MLP module. To introduce cross-window connections while maintaining the efficient computation of non-overlapping windows, a strategy which alternates between WMSA and Shuffle-WMSA in consecutive Shuffle Transformer blocks is proposed. The first window-based transformer block uses regular window partition strategy and the second window-based transformer block uses window-based selfattention with spatial shuffle. Besides, the Neighbor-Window Connection moduel (NWC) is added into each block for enhancing connections among neighborhood windows. Thus the proposed shuffle transformer block could build rich cross-window connections and augments representation. Finally, the consecutive Shuffle Transformer blocks are computed as: $$ x^{l}=\mathbf{W M S A}\left(\mathbf{B N}\left(z^{l-1}\right)\right)+z^{l-1} $$ $$ y^{l}=\mathbf{N W C}\left(x^{l}\right)+x^{l} $$ $$ z^{l}=\mathbf{M L P}\left(\mathbf{B N}\left(y^{l}\right)\right)+y^{l} $$ $$ x^{l+1}=\mathbf{S h u f f l e - W M S A}\left(\mathbf{B N}\left(z^{l}\right)\right)+z^{l} $$ $$ y^{l+1}=\mathbf{N W C}\left(x^{l+1}\right)+x^{l+1} $$ $$ z^{l+1}=\mathbf{M L P}\left(\mathbf{B N}\left(y^{l+1}\right)\right)+y^{l+1} $$ where $x^l$, $y^l$ and $z^l$ denote the output features of the (Shuffle-)WMSA module, the Neighbor-Window Connection module and the MLP module for block $l$, respectively; WMSA and Shuffle-WMSA denote window-based multi-head self-attention without/with spatial shuffle, respectively.

Soft Split and Soft Composition are video frame based operations used in the FuseFormer architecture, specifically the FuseFormer blocks. We softly split each frame into overlapped patches and then softly composite them back, by using an unfold and fold operator with patch size $k$ being greater than patch stride $s$. When compositing patches back to its original spatial shape, we add up feature values at each overlapping spatial location of neighboring patches.

A Strided EESP unit is based on the EESP Unit but is modified to learn representations more efficiently at multiple scales. Depth-wise dilated convolutions are given strides, an average pooling operation is added instead of an identity connection, and the element-wise addition operation is replaced with a concatenation operation, which helps in expanding the dimensions of feature maps efficiently.

A TResNet is a variant on a ResNet that aim to boost accuracy while maintaining GPU training and inference efficiency. They contain several design tricks including a SpaceToDepth stem, Anti-Alias downsampling, In-Place Activated BatchNorm, Blocks selection and squeeze-and-excitation layers.

TradeS is an online joint detection and tracking model, coined as TRACK to DEtect and Segment, exploiting tracking clues to assist detection end-to-end. TraDeS infers object tracking offset by a cost volume, which is used to propagate previous object features for improving current object detection and segmentation.

UCNet is a probabilistic framework for RGB-D Saliency Detection that employs uncertainty by learning from the data labelling process. It utilizes conditional variational autoencoders to model human annotation uncertainty and generate multiple saliency maps for each input image by sampling in the latent space.