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Image Classification

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하위 태스크
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평가표
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Image Classification is a fundamental task in vision recognition that aims to understand and categorize an image as a whole under a specific label. Unlike object detection, which involves classification and location of multiple objects within an image, image classification typically pertains to single-object images. When the classification becomes highly detailed or reaches instance-level, it is often referred to as image retrieval, which also involves finding similar images in a large database.

Source: Metamorphic Testing for Object Detection Systems

기법

관련 논문에서 소개한 기법입니다. 이 태스크에서의 평가 여부는 평가 결과에서 확인하세요.

R-Mix

R-Mix (Random Mix-up) is a Mix-up family Data Augmentation method. It combines random Mix-up with Saliency-guided mix-up, producing a procedure that is fast and performant, while reserving good characteristics of Saliency-guided Mix-up such as low Expected Calibration Error and high Weakly-supervised Object Localization accuracy.

Expeditious Saliency-guided Mix-up through Random Gradient Thresholding

RegionViT

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.

RegionViT: Regional-to-Local Attention for Vision Transformers

SCARLET-NAS

SCARLET-NAS is a type of neural architecture search that utilises a learnable stabilizer to calibrate feature deviation, named the Equivariant Learnable Stabilizer (ELS). Previous one-shot approaches can be limited by fixed-depth search spaces. With SCARLET-NAS, we use the equivariant learnable stabilizer on each skip connection. This can lead to improved convergence, more reliable evaluation, and retained equivalence. The third benefit is deemed most important by the authors for scalability.

SCARLET-NAS: Bridging the Gap between Stability and Scalability in Weight-sharing Neural Architecture Search

SPP-Net

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.

Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

Shuffle-T

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.

Shuffle Transformer: Rethinking Spatial Shuffle for Vision Transformer

StarReLU

$s \cdot (\mathrm{ReLU}(x))^2 + b$ where $s \in \mathbb{R}$ and $b \in \mathbb{R}$ are shared for all channels and can be set as constants (s=0.8944, b=-0.4472) or learnable parameters.

MetaFormer Baselines for Vision

Strided EESP

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.

ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network

Twins-SVT

Twins-SVT is a type of vision transformer which utilizes a spatially separable attention mechanism (SSAM) which is composed of two types of attention operations—(i) locally-grouped self-attention (LSA), and (ii) global sub-sampled attention (GSA), where LSA captures the fine-grained and short-distance information and GSA deals with the long-distance and global information. On top of this, it utilizes conditional position encodings as well as the architectural design of the Pyramid Vision Transformer.

Twins: Revisiting the Design of Spatial Attention in Vision Transformers

Virtual Batch Normalization

Virtual Batch Normalization is a normalization method used for training generative adversarial networks that extends batch normalization. Regular batch normalization causes the output of a neural network for an input example $\mathbf{x}$ to be highly dependent on several other inputs $\mathbf{x}'$ in the same minibatch. To avoid this problem in virtual batch normalization (VBN), each example $\mathbf{x}$ is normalized based on the statistics collected on a reference batch of examples that are chosen once and fixed at the start of training, and on $\mathbf{x}$ itself. The reference batch is normalized using only its own statistics. VBN is computationally expensive because it requires running forward propagation on two minibatches of data, so the authors use it only in the generator network.

Improved Techniques for Training GANs

Visformer

Visformer, or Vision-friendly Transformer, is an architecture that combines Transformer-based architectural features with those from convolutional neural network architectures. Visformer adopts the stage-wise design for higher base performance. But self-attentions are only utilized in the last two stages, considering that self-attention in the high-resolution stage is relatively inefficient even when the FLOPs are balanced. Visformer employs bottleneck blocks in the first stage and utilizes group 3 × 3 convolutions in bottleneck blocks inspired by ResNeXt. It also introduces BatchNorm to patch embedding modules as in CNNs.

Visformer: The Vision-friendly Transformer

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