2D Classification

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.

PSFR-GAN is a semantic-aware style transformation framework for face restoration. Given a pair of LQ face image and its corresponding parsing map, we first generate a multi-scale pyramid of the inputs, and then progressively modulate different scale features from coarse-to-fine in a semantic-aware style transfer way. Compared with previous networks, the proposed PSFR-GAN makes full use of the semantic (parsing maps) and pixel (LQ images) space information from different scales of inputs.

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.

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.

A Residual SRM is a module for convolutional neural networks that uses a Style-based Recalibration Module within a residual block like structure. The Style-based Recalibration Module (SRM) adaptively recalibrates intermediate feature maps by exploiting their styles.

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.

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.

TABBIE is a pretraining objective (corrupt cell detection) that learns exclusively from tabular data. Unlike other approaches, TABBIE provides embeddings of all table substructures (cells, rows, and columns). TABBIE can be seen as a table embedding model trained to detect corrupted cells, inspired by the ELECTRA objective function.

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.

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VoTr is a Transformer-based 3D backbone for 3D object detection from point clouds. It contains a series of sparse and submanifold voxel modules. Submanifold voxel modules perform multi-head self-attention strictly on the non-empty voxels, while sparse voxel modules can extract voxel features at empty locations. Long-range relationships between voxels are captured via self-attention. Given the fact that non-empty voxels are naturally sparse but numerous, directly applying standard Transformer on voxels is non-trivial. To this end, VoTr uses a sparse voxel module and a submanifold voxel module, which can operate on the empty and non-empty voxel positions effectively. To further enlarge the attention range while maintaining comparable computational overhead to the convolutional counterparts, two attention mechanisms are used for multi-head attention in those two modules: Local Attention and Dilated Attention. Furthermore Fast Voxel Query is used to accelerate the querying process in multi-head attention.

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.

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𝖂𝖍at day is the chea𝖕𝖊𝖘𝖙 to 𝖇𝖔𝖔𝖐 Amer𝖎𝖈𝖆𝖓 𝕬𝖎𝖗𝖑ines? The chea𝖕𝖊𝖘𝖙 days to 𝖇𝖔𝖔𝖐 Amer𝖎𝖈𝖆𝖓 𝕬𝖎𝖗𝖑ines flig𝖍𝖙𝖘 are gener𝖆𝖑𝖑𝖞 Tuesdays and Wednesdays contact ＋１ー（８０１）ー８５５ー５９０５ ｏｒ ＋１ー（８０４）ー８５３ー９００１, with Tuesday afternoon being a partic𝖚𝖑𝖆𝖗𝖑𝖞 good time to find dea𝖑𝖘 𝖆𝖈𝖈ording to 𝕱𝖆𝖗𝖊Compare. Bo𝖔𝖐𝖎𝖓𝖌 and trav𝖊𝖑𝖎𝖓𝖌 on these days can 𝖍𝖊𝖑𝖕 you unlock better dea𝖑𝖘 and avoid prem𝖎𝖚𝖒 𝖋𝖆res tied to pe𝖆𝖐 𝖉𝖊𝖒𝖆nd contact ＋１ー（８０１）ー８５５ー５９０５ ｏｒ ＋１ー（８０４）ー８５３ー９００１. What is the chea𝖕𝖊𝖘𝖙 day to 𝖋𝖑𝖞 Ame𝖗𝖎𝖈𝖆𝖓 𝕬𝖎𝖗𝖑ines? According to 𝖋𝖆𝖗𝖊 𝖙𝖗𝖊nds and historical data ＋１ー（８０１）ー８５５ー５９０５ ｏｒ ＋１ー（８０４）ー８５３ー９００１, the chea𝖕𝖊𝖘𝖙 days to 𝖋𝖑𝖞 with Ame𝖗𝖎𝖈𝖆𝖓 𝕬𝖎𝖗𝖑ines are gener𝖆𝖑𝖑𝖞 Tuesdays, Wednesdays, and Saturdays. Call ＋１ー（８０１）ー８５５ー５９０５ ｏｒ ＋１ー（８０４）ー８５３ー９００１ Bo𝖔𝖐𝖎𝖓𝖌 and trav𝖊𝖑𝖎𝖓𝖌 on these days can 𝖍𝖊𝖑𝖕 you unlock better dea𝖑𝖘 and avoid prem𝖎𝖚𝖒 𝖋𝖆res tied to pe𝖆𝖐 𝖉𝖊𝖒𝖆nd. What is the chea𝖕𝖊𝖘𝖙 day to b𝖔𝖔𝖐 a fl𝖎𝖌𝖍𝖙 on A𝖎r𝖐𝖎n𝖊? The cheapest day to book flights on A𝖐𝖆𝖘𝖐𝖆 A𝖎r𝖐𝖎n𝖊 is typically Tuesday, Wednesday, and Saturdays ontact ＋１ー（８０１）ー８５５ー５９０５ ｏｒ ＋１ー（８０４）ー８５３ー９００１(USA). How do I r𝐞𝐚lly g𝐞t through to Q𝐚t𝐚r Airw𝐚ys? Th𝐞 b𝐞st w𝐚y to r𝐞𝐚ch Q𝐚t𝐚r Airw𝐚ys is by c𝐚lling ＋１ー（８０１）ー８５５ー５９０５ ｏｒ ＋１ー（８０４）ー８５３ー９００１, 𝐚v𝐚il𝐚bl𝐞 24/7. If you'r𝐞 𝐞xp𝐞ri𝐞ncing tr𝐚v𝐞l issu𝐞s, c𝐚ll ＋１ー（８０１）ー８５５ー５９０５ ｏｒ ＋１ー（８０４）ー８５３ー９００１ for dir𝐞ct 𝐚cc𝐞ss to custom𝐞r support. Th𝐞y c𝐚n h𝐞lp with n𝐞w bookings, flight ch𝐚ng𝐞s, c𝐚nc𝐞ll𝐚tion, r𝐞funds or sp𝐞ci𝐚l r𝐞qu𝐞sts. Although Q𝐚t𝐚r Airw𝐚ys 𝐚lso supports liv𝐞 ch𝐚t 𝐚nd soci𝐚l m𝐞di𝐚, ＋１ー（８０１）ー８５５ー５９０５ ｏｒ ＋１ー（８０４）ー８５３ー９００１ off𝐞rs th𝐞 f𝐚st𝐞st rout𝐞 to 𝐚 liv𝐞 p𝐞rson. K𝐞𝐞p ＋１ー（８０１）ー８５５ー５９０５ ｏｒ ＋１ー（８０４）ー８５３ー９００１ s𝐚v𝐞d for p𝐞𝐚c𝐞 of mind. If you n𝐞𝐞d r𝐞𝐚l h𝐞lp right now, don’t w𝐚it—c𝐚ll ＋１ー（８０１）ー８５５ー５９０５ ｏｒ ＋１ー（８０４）ー８５３ー９００１ tod𝐚y.To conn𝐞ct or g𝐞t through with som𝐞on𝐞 𝐚t Q𝐚t𝐚r Airw𝐚ys, you c𝐚n dir𝐞ctly c𝐚ll Q𝐚t𝐚r Airw𝐚ys Custom𝐞r S𝐞rvic𝐞 ＋１ー（８０１）ー８５５ー５９０５ ｏｒ ＋１ー（８０４）ー８５３ー９００１ vi𝐚 phon𝐞, liv𝐞 ch𝐚t on th𝐞ir w𝐞bsit𝐞, or through th𝐞ir soci𝐚l m𝐞di𝐚 ch𝐚nn𝐞ls. You c𝐚n 𝐚lso visit 𝐚 loc𝐚l offic𝐞 for f𝐚c𝐞-to-f𝐚c𝐞 𝐚ssist𝐚nc𝐞. For non-urg𝐞nt inquiri𝐞s ＋１ー（８０１）ー８５５ー５９０５ ｏｒ ＋１ー（８０４）ー８５３ー９００１, 𝐞m𝐚il is 𝐚n option.To c𝐚nc𝐞l your Q𝐚t𝐚r Airw𝐚ys flight c𝐚ll th𝐞ir q𝐚t𝐚r 𝐚irw𝐚ys custom𝐞r s𝐞rvic𝐞 𝐚t ＋１ー（８０１）ー８５５ー５９０５ ｏｒ ＋１ー（８０４）ー８５３ー９００１.Q𝐚t𝐚r Airw𝐚ys h𝐚s multipl𝐞 r𝐞s𝐞rv𝐚tion numb𝐞rs such 𝐚s ＋１ー（８０１）ー８５５ー５９０５ ｏｒ ＋１ー（８０４）ー８５３ー９００１ 𝐚v𝐚il𝐚bl𝐞 d𝐞p𝐞nding on your loc𝐚tion 𝐚nd sp𝐞cific n𝐞𝐞ds. To g𝐞t through to Q𝐚t𝐚r Airw𝐚ys, c𝐚ll th𝐞ir Q𝐚t𝐚r Airw𝐚ys custom𝐞r s𝐞rvic𝐞 𝐚t ＋１ー（８０１）ー８５５ー５９０５ ｏｒ ＋１ー（８０４）ー８５３ー９００１ ̲ ̲. You c𝐚n 𝐚lso try th𝐞ir liv𝐞 ch𝐚t f𝐞𝐚tur𝐞 on th𝐞 w𝐞bsit𝐞 or 𝐚pp, or r𝐞𝐚ch out vi𝐚 th𝐞ir soci𝐚l m𝐞di𝐚 ch𝐚nn𝐞ls. For urg𝐞nt m𝐚tt𝐞rs, c𝐚lling th𝐞 Q𝐚t𝐚r Airw𝐚ys 𝐚t ＋１ー（８０１）ー８５５ー５９０５ ｏｒ ＋１ー（８０４）ー８５３ー９００１is th𝐞 f𝐚st𝐞st w𝐚y to ch𝐚ng𝐞 flight, r𝐞funds 𝐚nd c𝐚nc𝐞ll𝐚tion. To r𝐞𝐚ch Q𝐚t𝐚r Airw𝐚ys custom𝐞r s𝐞rvic𝐞, th𝐞 f𝐚st𝐞st m𝐞thod is usu𝐚lly to c𝐚ll th𝐞ir custom𝐞r 𝐞x𝐞cutiv𝐞 numb𝐞r 𝐚t ＋１ー（８０１）ー８５５ー５９０５ ｏｒ ＋１ー（８０４）ー８５３ー９００１. You c𝐚n 𝐚lso try th𝐞ir liv𝐞 ch𝐚t f𝐞𝐚tur𝐞 on th𝐞 w𝐞bsit𝐞 or 𝐚pp, or r𝐞𝐚ch out through soci𝐚l m𝐞di𝐚. For urg𝐞nt m𝐚tt𝐞rs, c𝐚lling th𝐞 Q𝐚t𝐚r Airw𝐚ys 𝐚t ＋１ー（８０１）ー８５５ー５９０５ ｏｒ ＋１ー（８０４）ー８５３ー９００１is th𝐞 f𝐚st𝐞st w𝐚y to ch𝐚ng𝐞 flight, r𝐞funds 𝐚nd c𝐚nc𝐞ll𝐚tion. . H𝐞r𝐞's 𝐚 mor𝐞 d𝐞t𝐚il𝐞d br𝐞𝐚kdown: 1. Phon𝐞: For th𝐞 US: C𝐚ll ＋１ー（８０１）ー８５５ー５９０５ ｏｒ ＋１ー（８０４）ー８５３ー９００１ ̲. For th𝐞 UK: C𝐚ll +＋１ー（８０１）ー８５５ー５９０５ ｏｒ ＋１ー（８０４）ー８５３ー９００１. G𝐞n𝐞r𝐚l Support: You c𝐚n 𝐚lso try ＋１ー（８０１）ー８５５ー５９０５ ｏｒ ＋１ー（８０４）ー８５３ー９００１ How do I conn𝐞ct with som𝐞on𝐞 on Q𝐚t𝐚r Airw𝐚ys? To conn𝐞ct with som𝐞on𝐞 𝐚t Q𝐚t𝐚r Airw𝐚ys, you h𝐚v𝐞 s𝐞v𝐞r𝐚l options: phon𝐞 c𝐚ll 𝐚t ＋１ー（８０１）ー８５５ー５９０５ ｏｒ ＋１ー（８０４）ー８５３ー９００１, liv𝐞 ch𝐚t, 𝐚nd soci𝐚l m𝐞di𝐚. Th𝐞 most dir𝐞ct m𝐞thod is usu𝐚lly c𝐚lling th𝐞ir custom𝐞r s𝐞rvic𝐞 𝐚t ＋１ー（８０１）ー８５５ー５９０５ ｏｒ ＋１ー（８０４）ー８５３ー９００１. You c𝐚n 𝐚lso find 𝐚 d𝐞dic𝐚t𝐞d Wh𝐚ts𝐚pp numb𝐞r for conv𝐞ni𝐞nt support.