3D

Region-based Fully Convolutional Networks, or R-FCNs, are a type of region-based object detector. In contrast to previous region-based object detectors such as Fast/Faster R-CNN that apply a costly per-region subnetwork hundreds of times, R-FCN is fully convolutional with almost all computation shared on the entire image. To achieve this, R-FCN utilises position-sensitive score maps to address a dilemma between translation-invariance in image classification and translation-variance in object detection.

Hou et al. proposed coordinate attention, a novel attention mechanism which embeds positional information into channel attention, so that the network can focus on large important regions at little computational cost. The coordinate attention mechanism has two consecutive steps, coordinate information embedding and coordinate attention generation. First, two spatial extents of pooling kernels encode each channel horizontally and vertically. In the second step, a shared $1\times 1$ convolutional transformation function is applied to the concatenated outputs of the two pooling layers. Then coordinate attention splits the resulting tensor into two separate tensors to yield attention vectors with the same number of channels for horizontal and vertical coordinates of the input $X$ along. This can be written as \begin{align} z^h &= \text{GAP}^h(X) \end{align} \begin{align} z^w &= \text{GAP}^w(X) \end{align} \begin{align} f &= \delta(\text{BN}(\text{Conv}_1^{1\times 1}([z^h;z^w]))) \end{align} \begin{align} f^h, f^w &= \text{Split}(f) \end{align} \begin{align} s^h &= \sigma(\text{Conv}_h^{1\times 1}(f^h)) \end{align} \begin{align} s^w &= \sigma(\text{Conv}_w^{1\times 1}(f^w)) \end{align} \begin{align} Y &= X s^h s^w \end{align} where $\text{GAP}^h$ and $\text{GAP}^w$ denote pooling functions for vertical and horizontal coordinates, and $s^h \in \mathbb{R}^{C\times 1\times W}$ and $s^w \in \mathbb{R}^{C\times H\times 1}$ represent corresponding attention weights. Using coordinate attention, the network can accurately obtain the position of a targeted object. This approach has a larger receptive field than BAM and CBAM. Like an SE block, it also models cross-channel relationships, effectively enhancing the expressive power of the learned features. Due to its lightweight design and flexibility, it can be easily used in classical building blocks of mobile networks.

CoOp, or Context Optimization, is an automated prompt engineering method that avoids manual prompt tuning by modeling context words with continuous vectors that are end-to-end learned from data. The context could be shared among all classes or designed to be class-specific. During training, we simply minimize the prediction error using the cross-entropy loss with respect to the learnable context vectors, while keeping the pre-trained parameters fixed. The gradients can be back-propagated all the way through the text encoder, distilling the rich knowledge encoded in the parameters for learning task-relevant context.

Spatial-Reduction Attention, or SRA, is a multi-head attention module used in the Pyramid Vision Transformer architecture which reduces the spatial scale of the key $K$ and value $V$ before the attention operation. This reduces the computational/memory overhead. Details of the SRA in the stage $i$ can be formulated as follows: $$ \text{SRA}(Q, K, V)=\text { Concat }\left(\operatorname{head}\_{0}, \ldots \text { head }\_{N\_{i}}\right) W^{O} $$ $$\text{ head}\_{j}=\text { Attention }\left(Q W\_{j}^{Q}, \operatorname{SR}(K) W\_{j}^{K}, \operatorname{SR}(V) W\_{j}^{V}\right) $$ where Concat $(\cdot)$ is the concatenation operation. $W\_{j}^{Q} \in \mathbb{R}^{C\_{i} \times d\_{\text {head }}}$, $W\_{j}^{K} \in \mathbb{R}^{C\_{i} \times d\_{\text {head }}}$, $W\_{j}^{V} \in \mathbb{R}^{C\_{i} \times d\_{\text {head }}}$, and $W^{O} \in \mathbb{R}^{C\_{i} \times C\_{i}}$ are linear projection parameters. $N\_{i}$ is the head number of the attention layer in Stage $i$. Therefore, the dimension of each head (i.e. $\left.d\_{\text {head }}\right)$ is equal to $\frac{C\_{i}}{N\_{i}} . \text{SR}(\cdot)$ is the operation for reducing the spatial dimension of the input sequence ($K$ or $V$ ), which is written as: $$ \text{SR}(\mathbf{x})=\text{Norm}\left(\operatorname{Reshape}\left(\mathbf{x}, R\_{i}\right) W^{S}\right) $$ Here, $\mathbf{x} \in \mathbb{R}^{\left(H\_{i} W\_{i}\right) \times C\_{i}}$ represents a input sequence, and $R\_{i}$ denotes the reduction ratio of the attention layers in Stage $i .$ Reshape $\left(\mathbf{x}, R\_{i}\right)$ is an operation of reshaping the input sequence $\mathbf{x}$ to a sequence of size $\frac{H\_{i} W\_{i}}{R\_{i}^{2}} \times\left(R\_{i}^{2} C\_{i}\right)$. $W\_{S} \in \mathbb{R}^{\left(R\_{i}^{2} C\_{i}\right) \times C\_{i}}$ is a linear projection that reduces the dimension of the input sequence to $C\_{i}$. $\text{Norm}(\cdot)$ refers to layer normalization.

SpatialDropout is a type of dropout for convolutional networks. For a given convolution feature tensor of size $n\_{\text{feats}}$×height×width, we perform only $n\_{\text{feats}}$ dropout trials and extend the dropout value across the entire feature map. Therefore, adjacent pixels in the dropped-out feature map are either all 0 (dropped-out) or all active as illustrated in the figure to the right.

Stacked Hourglass Networks are a type of convolutional neural network for pose estimation. They are based on the successive steps of pooling and upsampling that are done to produce a final set of predictions.

Dense Prediction Transformers (DPT) are a type of vision transformer for dense prediction tasks. The input image is transformed into tokens (orange) either by extracting non-overlapping patches followed by a linear projection of their flattened representation (DPT-Base and DPT-Large) or by applying a ResNet-50 feature extractor (DPT-Hybrid). The image embedding is augmented with a positional embedding and a patch-independent readout token (red) is added. The tokens are passed through multiple transformer stages. The tokens are reassembled from different stages into an image-like representation at multiple resolutions (green). Fusion modules (purple) progressively fuse and upsample the representations to generate a fine-grained prediction.

Mixture model network (MoNet) is a general framework allowing to design convolutional deep architectures on non-Euclidean domains such as graphs and manifolds. Image and description from: Geometric deep learning on graphs and manifolds using mixture model CNNs

Res2Net is an image model that employs a variation on bottleneck residual blocks. The motivation is to be able to represent features at multiple scales. This is achieved through a novel building block for CNNs that constructs hierarchical residual-like connections within one single residual block. This represents multi-scale features at a granular level and increases the range of receptive fields for each network layer.

A Res2Net Block is an image model block that constructs hierarchical residual-like connections within one single residual block. It was proposed as part of the Res2Net CNN architecture. The block represents multi-scale features at a granular level and increases the range of receptive fields for each network layer. The $3 \times 3$ filters of $n$ channels is replaced with a set of smaller filter groups, each with $w$ channels. These smaller filter groups are connected in a hierarchical residual-like style to increase the number of scales that the output features can represent. Specifically, we divide input feature maps into several groups. A group of filters first extracts features from a group of input feature maps. Output features of the previous group are then sent to the next group of filters along with another group of input feature maps. This process repeats several times until all input feature maps are processed. Finally, feature maps from all groups are concatenated and sent to another group of $1 \times 1$ filters to fuse information altogether. Along with any possible path in which input features are transformed to output features, the equivalent receptive field increases whenever it passes a $3 \times 3$ filter, resulting in many equivalent feature scales due to combination effects. One way of thinking of these blocks is that they expose a new dimension, scale, alongside the existing dimensions of depth, width, and cardinality.

Animatable Reconstruction of Clothed Humans is an end-to-end framework for accurate reconstruction of animation-ready 3D clothed humans from a monocular image. ARCH is a learned pose-aware model that produces detailed 3D rigged full-body human avatars from a single unconstrained RGB image. A Semantic Space and a Semantic Deformation Field are created using a parametric 3D body estimator. They allow the transformation of 2D/3D clothed humans into a canonical space, reducing ambiguities in geometry caused by pose variations and occlusions in training data. Detailed surface geometry and appearance are learned using an implicit function representation with spatial local features.

Cascade Mask R-CNN extends Cascade R-CNN to instance segmentation, by adding a mask head to the cascade. In the Mask R-CNN, the segmentation branch is inserted in parallel to the detection branch. However, the Cascade R-CNN has multiple detection branches. This raises the questions of 1) where to add the segmentation branch and 2) how many segmentation branches to add. The authors consider three strategies for mask prediction in the Cascade R-CNN. The first two strategies address the first question, adding a single mask prediction head at either the first or last stage of the Cascade R-CNN. Since the instances used to train the segmentation branch are the positives of the detection branch, their number varies in these two strategies. Placing the segmentation head later on the cascade leads to more examples. However, because segmentation is a pixel-wise operation, a large number of highly overlapping instances is not necessarily as helpful as for object detection, which is a patch-based operation. The third strategy addresses the second question, adding a segmentation branch to each cascade stage. This maximizes the diversity of samples used to learn the mask prediction task. At inference time, all three strategies predict the segmentation masks on the patches produced by the final object detection stage, irrespective of the cascade stage on which the segmentation mask is implemented and how many segmentation branches there are.

MobileViT is a vision transformer that is tuned to mobile phone

Non-maximum suppression is an integral part of the object detection pipeline. First, it sorts all detection boxes on the basis of their scores. The detection box $M$ with the maximum score is selected and all other detection boxes with a significant overlap (using a pre-defined threshold) with $M$ are suppressed. This process is recursively applied on the remaining boxes. As per the design of the algorithm, if an object lies within the predefined overlap threshold, it leads to a miss. Soft-NMS solves this problem by decaying the detection scores of all other objects as a continuous function of their overlap with M. Hence, no object is eliminated in this process.

Neighborhood Attention is a restricted self attention pattern in which each token's receptive field is limited to its nearest neighboring pixels. It was proposed in Neighborhood Attention Transformer as an alternative to other local attention mechanisms used in Hierarchical Vision Transformers. NA is in concept similar to stand alone self attention (SASA), in that both can be implemented with a raster scan sliding window operation over the key value pair. However, NA would require a modification to handle corner pixels, which helps maintain a fixed receptive field size and an increased number of relative positions. The primary challenge in experimenting with both NA and SASA has been computation. Simply extracting key values for each query is slow, takes up a large amount of memory, and is eventually intractable at scale. NA was therefore implemented through a new CUDA extension to PyTorch, NATTEN.

ShuffleNet V2 Block is an image model block used in the ShuffleNet V2 architecture, where speed is the metric optimized for (instead of indirect ones like FLOPs). It utilizes a simple operator called channel split. At the beginning of each unit, the input of $c$ feature channels are split into two branches with $c - c'$ and $c'$ channels, respectively. Following G3, one branch remains as identity. The other branch consists of three convolutions with the same input and output channels to satisfy G1. The two $1\times1$ convolutions are no longer group-wise, unlike the original ShuffleNet. This is partially to follow G2, and partially because the split operation already produces two groups. After convolution, the two branches are concatenated. So, the number of channels keeps the same (G1). The same “channel shuffle” operation as in ShuffleNet is then used to enable information communication between the two branches. The motivation behind channel split is that alternative architectures, where pointwise group convolutions and bottleneck structures are used, lead to increased memory access cost. Additionally more network fragmentation with group convolutions reduces parallelism (less friendly for GPU), and the element-wise addition operation, while they have low FLOPs, have high memory access cost. Channel split is an alternative where we can maintain a large number of equally wide channels (equally wide minimizes memory access cost) without having dense convolutions or too many groups.

Adaptive Feature Pooling pools features from all levels for each proposal in object detection and fuses them for the following prediction. For each proposal, we map them to different feature levels. Following the idea of Mask R-CNN, RoIAlign is used to pool feature grids from each level. Then a fusion operation (element-wise max or sum) is utilized to fuse feature grids from different levels. The motivation for this technique is that in an FPN we assign proposals to different feature levels based on the size of proposals, which could be suboptimal if images with small differences are assigned to different levels, or if the importance of features is not strongly correlated to their level which they belong.

AugMix mixes augmented images through linear interpolations. Consequently it is like Mixup but instead mixes augmented versions of the same image.

MetaFormer is a general architecture abstracted from Transformers by not specifying the token mixer.

Many machine learning tasks such as multiple instance learning, 3D shape recognition, and few-shot image classification are defined on sets of instances. Since solutions to such problems do not depend on the order of elements of the set, models used to address them should be permutation invariant. We present an attention-based neural network module, the Set Transformer, specifically designed to model interactions among elements in the input set. The model consists of an encoder and a decoder, both of which rely on attention mechanisms. In an effort to reduce computational complexity, we introduce an attention scheme inspired by inducing point methods from sparse Gaussian process literature. It reduces the computation time of self-attention from quadratic to linear in the number of elements in the set. We show that our model is theoretically attractive and we evaluate it on a range of tasks, demonstrating the state-of-the-art performance compared to recent methods for set-structured data.

Meta Face Recognition (MFR) is a meta-learning face recognition method. MFR synthesizes the source/target domain shift with a meta-optimization objective, which requires the model to learn effective representations not only on synthesized source domains but also on synthesized target domains. Specifically, domain-shift batches are built through a domain-level sampling strategy and back-propagated gradients/meta-gradients are obtained on synthesized source/target domains by optimizing multi-domain distributions. The gradients and meta-gradients are further combined to update the model to improve generalization.

Random Erasing is a data augmentation method for training the convolutional neural network (CNN), which randomly selects a rectangle region in an image and erases its pixels with random values. In this process, training images with various levels of occlusion are generated, which reduces the risk of over-fitting and makes the model robust to occlusion. Random Erasing is parameter learning free, easy to implement, and can be integrated with most of the CNN-based recognition models. Random Erasing is complementary to commonly used data augmentation techniques such as random cropping and flipping, and can be implemented in various vision tasks, such as image classification, object detection, semantic segmentation. In the Albumentations library, there is a generalization of RandomErasing called CoarseDropout, which allows masking an arbitrary number of regions of rectangular shape. It could be applied to images, segmentation masks, and key points. Documentation for CoarseDropout

Path Aggregation Network, or PANet, aims to boost information flow in a proposal-based instance segmentation framework. Specifically, the feature hierarchy is enhanced with accurate localization signals in lower layers by bottom-up path augmentation, which shortens the information path between lower layers and topmost feature. Additionally, adaptive feature pooling is employed, which links feature grid and all feature levels to make useful information in each feature level propagate directly to following proposal subnetworks. A complementary branch capturing different views for each proposal is created to further improve mask prediction.

Self-Cure Network, or SCN, is a method for suppressing uncertainties for large-scale facial expression recognition, prventing deep networks from overfitting uncertain facial images. Specifically, SCN suppresses the uncertainty from two different aspects: 1) a self-attention mechanism over mini-batch to weight each training sample with a ranking regularization, and 2) a careful relabeling mechanism to modify the labels of these samples in the lowest-ranked group.

ALBEF introduces a contrastive loss to align the image and text representations before fusing them through cross-modal attention. This enables more grounded vision and language representation learning. ALBEF also doesn't require bounding box annotations. The model consists of an image encode, a text encoder, and a multimodal encoder. The image-text contrastive loss helps to align the unimodal representations of an image-text pair before fusion. The image-text matching loss and a masked language modeling loss are applied to learn multimodal interactions between image and text. In addition, momentum distillation is used to generate pseudo-targets. This improves learning with noisy data.

PrIme Sample Attention (PISA) directs the training of object detection frameworks towards prime samples. These are samples that play a key role in driving the detection performance. The authors define Hierarchical Local Rank (HLR) as a metric of importance. Specifically, they use IoU-HLR to rank positive samples and ScoreHLR to rank negative samples in each mini-batch. This ranking strategy places the positive samples with highest IoUs around each object and the negative samples with highest scores in each cluster to the top of the ranked list and directs the focus of the training process to them via a simple re-weighting scheme. The authors also devise a classification-aware regression loss to jointly optimize the classification and regression branches. Particularly, this loss would suppress those samples with large regression loss, thus reinforcing the attention to prime samples.

SNIP, or Scale Normalization for Image Pyramids, is a multi-scale training scheme that selectively back-propagates the gradients of object instances of different sizes as a function of the image scale. SNIP is a modified version of MST where only the object instances that have a resolution close to the pre-training dataset, which is typically 224x224, are used for training the detector. In multi-scale training (MST), each image is observed at different resolutions therefore, at a high resolution (like 1400x2000) large objects are hard to classify and at a low resolution (like 480x800) small objects are hard to classify. Fortunately, each object instance appears at several different scales and some of those appearances fall in the desired scale range. In order to eliminate extreme scale objects, either too large or too small, training is only performed on objects that fall in the desired scale range and the remainder are simply ignored during back-propagation. Effectively, SNIP uses all the object instances during training, which helps capture all the variations in appearance and pose, while reducing the domain-shift in the scale-space for the pre-trained network.

The Contour Proposal Network (CPN) detects possibly overlapping objects in an image while simultaneously fitting pixel-precise closed object contours. The CPN can incorporate state of the art object detection architectures as backbone networks into a fast single-stage instance segmentation model that can be trained end-to-end.

ShuffleNet V2 Downsampling Block is a block for spatial downsampling used in the ShuffleNet V2 architecture. Unlike the regular ShuffleNet V2 block, the channel split operator is removed so the number of output channels is doubled.

Spatial Broadcast Decoder is an architecture that aims to improve disentangling, reconstruction accuracy, and generalization to held-out regions in data space. It provides a particularly dramatic benefit when applied to datasets with small objects. Source: Watters et al. Image source: Watters et al.

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.

ACER, or Actor Critic with Experience Replay, is an actor-critic deep reinforcement learning agent with experience replay. It can be seen as an off-policy extension of A3C, where the off-policy estimator is made feasible by: - Using Retrace Q-value estimation. - Using truncated importance sampling with bias correction. - Using a trust region policy optimization method. - Using a stochastic dueling network architecture.

Florence is a computer vision foundation model aiming to learn universal visual-language representations that be adapted to various computer vision tasks, visual question answering, image captioning, video retrieval, among other tasks. Florence's workflow consists of data curation, unified learning, Transformer architectures and adaption. Florence is pre-trained in an image-label-description space, utilizing a unified image-text contrastive learning. It involves a two-tower architecture: 12-layer Transformer for the language encoder, and a Vision Transformer for the image encoder. Two linear projection layers are added on top of the image encoder and language encoder to match the dimensions of image and language features. Compared to previous methods for cross-modal shared representations, Florence expands beyond simple classification and retrieval capabilities to advanced representations that support object level, multiple modality, and videos respectively.

A Global Context Block is an image model block for global context modeling. The aim is to have both the benefits of the simplified non-local block with effective modeling of long-range dependencies, and the squeeze-excitation block with lightweight computation. In the Global Context framework, we have (a) global attention pooling, which adopts a 1x1 convolution $W_{k}$ and softmax function to obtain the attention weights, and then performs the attention pooling to obtain the global context features, (b) feature transform via a 1x1 convolution $W\_{v}$; (c) feature aggregation, which employs addition to aggregate the global context features to the features of each position. Taken as a whole, the GC block is proposed as a lightweight way to achieve global context modeling.

MixConv, or Mixed Depthwise Convolution, is a type of depthwise convolution that naturally mixes up multiple kernel sizes in a single convolution. It is based on the insight that depthwise convolution applies a single kernel size to all channels, which MixConv overcomes by combining the benefits of multiple kernel sizes. It does this by partitioning channels into groups and applying a different kernel size to each group.

MixNet is a type of convolutional neural network discovered via AutoML that utilises MixConvs instead of regular depthwise convolutions.