Image Classification

In this work, we present a conceptually simple and effective method to train a strong bilingual multimodal representation model. Starting from the pretrained multimodal representation model CLIP released by OpenAI, we switched its text encoder with a pretrained multilingual text encoder XLM-R, and aligned both languages and image representations by a two-stage training schema consisting of teacher learning and contrastive learning. We validate our method through evaluations of a wide range of tasks. We set new state-of-the-art performances on a bunch of tasks including ImageNet-CN, Flicker30k- CN, and COCO-CN. Further, we obtain very close performances with CLIP on almost all tasks, suggesting that one can simply alter the text encoder in CLIP for extended capabilities such as multilingual understanding. Our models and code are available at https://github.com/FlagAI-Open/FlagAI.

learn to explore the sample relationships via transformer networks

The Contextual Attention Block (CAB) is a new plug-and-play module to model context awareness. It is simple and effective and can be integrated with any feed-forward neural network. CAB infers weights that multiply the feature maps according to their causal influence on the scene, modeling the co-occurrence of different objects in the image. You can place the CAB module at different bottlenecks to infuse a hierarchical context awareness into the model.

CPVT, or Conditional Position Encoding Vision Transformer, is a type of vision transformer which utilizes conditional positional encoding. Other than the new encodings, it follows the same architecture of ViT and DeiT.

Channel & spatial attention combines the advantages of channel attention and spatial attention. It adaptively selects both important objects and regions

Combines the channel attention of the widely known spatial squeeze and channel excitation (SE) block and the spatial attention of the channel squeeze and spatial excitation (sSE) block to build a spatial and channel attention mechanism for image segmentation tasks.

DeepViT is a type of vision transformer that replaces the self-attention layer within the transformer block with a Re-attention module to address the issue of attention collapse and enables training deeper ViTs.

A variant of CutMix which randomly samples masks from Fourier space.

FCANet contains a novel multi-spectral channel attention module. Given an input feature map $X \in \mathbb{R}^{C \times H \times W}$, multi-spectral channel attention first splits $X$ into many parts $x^{i} \in \mathbb{R}^{C' \times H \times W}$. Then it applies a 2D DCT to each part $x^{i}$. Note that a 2D DCT can use pre-processing results to reduce computation. After processing each part, all results are concatenated into a vector. Finally, fully connected layers, ReLU activation and a sigmoid are used to get the attention vector as in an SE block. This can be formulated as: \begin{align} s = F_\text{fca}(X, \theta) & = \sigma (W_{2} \delta (W_{1}[(\text{DCT}(\text{Group}(X)))])) \end{align} \begin{align} Y & = s X \end{align} where $\text{Group}(\cdot)$ indicates dividing the input into many groups and $\text{DCT}(\cdot)$ is the 2D discrete cosine transform. This work based on information compression and discrete cosine transforms achieves excellent performance on the classification task.

Filter Response Normalization (FRN) is a type of normalization that combines normalization and an activation function, which can be used as a replacement for other normalizations and activations. It operates on each activation channel of each batch element independently, eliminating the dependency on other batch elements. To demonstrate, assume we are dealing with the feed-forward convolutional neural network. We follow the usual convention that the filter responses (activation maps) produced after a convolution operation are a 4D tensor $X$ with shape $[B, W, H, C]$, where $B$ is the mini-batch size, $W, H$ are the spatial extents of the map, and $C$ is the number of filters used in convolution. $C$ is also referred to as output channels. Let $x = X_{b,:,:,c} \in \mathcal{R}^{N}$, where $N = W \times H$, be the vector of filter responses for the $c^{th}$ filter for the $b^{th}$ batch point. Let $\nu^2 = \sum\_i x_i^2/N$, be the mean squared norm of $x$. Then Filter Response Normalization is defined as the following: $$ \hat{x} = \frac{x}{\sqrt{\nu^2 + \epsilon}}, $$ where $\epsilon$ is a small positive constant to prevent division by zero. A lack of mean centering in FRN can lead to activations having an arbitrary bias away from zero. Such a bias in conjunction with ReLU can have a detrimental effect on learning and lead to poor performance and dead units. To address this the authors augment ReLU with a learned threshold $\tau$ to yield: $$ z = \max(y, \tau) $$ Since $\max(y, \tau){=}\max(y-\tau,0){+}\tau{=}\text{ReLU}{(y{-}\tau)}{+}\tau$, the effect of this activation is the same as having a shared bias before and after ReLU.

FixUp Initialization, or Fixed-Update Initialization, is an initialization method that rescales the standard initialization of residual branches by adjusting for the network architecture. Fixup aims to enables training very deep residual networks stably at a maximal learning rate without normalization. The steps are as follows: 1. Initialize the classification layer and the last layer of each residual branch to 0. 2. Initialize every other layer using a standard method, e.g. Kaiming Initialization, and scale only the weight layers inside residual branches by $L^{\frac{1}{2m-2}}$. 3. Add a scalar multiplier (initialized at 1) in every branch and a scalar bias (initialized at 0) before each convolution, linear, and element-wise activation layer.

Global Sub-Sampled Attention, or GSA, is a local attention mechanism used in the Twins-SVT architecture. A single representative is used to summarize the key information for each of $m \times n$ subwindows and the representative is used to communicate with other sub-windows (serving as the key in self-attention), which can reduce the cost to $\mathcal{O}(m n H W d)=\mathcal{O}\left(\frac{H^{2} W^{2} d}{k\_{1} k\_{2}}\right)$. This is essentially equivalent to using the sub-sampled feature maps as the key in attention operations, and thus it is termed global sub-sampled attention (GSA). If we alternatively use the LSA and GSA like separable convolutions (depth-wise + point-wise). The total computation cost is $\mathcal{O}\left(\frac{H^{2} W^{2} d}{k\_{1} k\_{2}}+k\_{1} k\_{2} H W d\right) .$ We have: $$\frac{H^{2} W^{2} d}{k\_{1} k\_{2}}+k_{1} k_{2} H W d \geq 2 H W d \sqrt{H W} $$ The minimum is obtained when $k\_{1} \cdot k\_{2}=\sqrt{H W}$. Note that $H=W=224$ is popular in classification. Without loss of generality, square sub-windows are used, i.e., $k\_{1}=k\_{2}$. Therefore, $k\_{1}=k\_{2}=15$ is close to the global minimum for $H=W=224$. However, the network is designed to include several stages with variable resolutions. Stage 1 has feature maps of $56 \times 56$, the minimum is obtained when $k\_{1}=k\_{2}=\sqrt{56} \approx 7$. Theoretically, we can calibrate optimal $k\_{1}$ and $k\_{2}$ for each of the stages. For simplicity, $k\_{1}=k\_{2}=7$ is used everywhere. As for stages with lower resolutions, the summarizing window-size of GSA is controlled to avoid too small amount of generated keys. Specifically, the sizes of 4,2 and 1 are used for the last three stages respectively.

A HaloNet is a self-attention based model for efficient image classification. It relies on a local self-attention architecture that efficiently maps to existing hardware with haloing. The formulation breaks translational equivariance, but the authors observe that it improves throughput and accuracies over the centered local self-attention used in regular self-attention. The approach also utilises a strided self-attentive downsampling operation for multi-scale feature extraction.

1.833.656.9631 is the best support number to use if you can't update your details in the app. 1.833.656.9631 will guide you through updating your address, name, or email. 1.833.656.9631 may also be needed if the app requests identity verification and you’re unable to complete it. 1.833.656.9631 is recommended if changes are not reflecting or being rejected. 1.833.656.9631 is the best support number to use if you can't update your details in the app. 1.833.656.9631 will guide you through updating your address, name, or email. 1.833.656.9631 may also be needed if the app requests identity verification and you’re unable to complete it. 1.833.656.9631 is recommended if changes are not reflecting or being rejected. 1.833.656.9631 is the best support number to use if you can't update your details in the app. 1.833.656.9631 will guide you through updating your address, name, or email. 1.833.656.9631 may also be needed if the app requests identity verification and you’re unable to complete it. 1.833.656.9631 is recommended if changes are not reflecting or being rejected. 1.833.656.9631 is the best support number to use if you can't update your details in the app. 1.833.656.9631 will guide you through updating your address, name, or email. 1.833.656.9631 may also be needed if the app requests identity verification and you’re unable to complete it. 1.833.656.9631 is recommended if changes are not reflecting or being rejected. 1.833.656.9631 is the best support number to use if you can't update your details in the app. 1.833.656.9631 will guide you through updating your address, name, or email. 1.833.656.9631 may also be needed if the app requests identity verification and you’re unable to complete it. 1.833.656.9631 is recommended if changes are not reflecting or being rejected.

In-Place Activated Batch Normalization, or InPlace-ABN, substitutes the conventionally used succession of BatchNorm + Activation layers with a single plugin layer, hence avoiding invasive framework surgery while providing straightforward applicability for existing deep learning frameworks. It approximately halves the memory requirements during training of modern deep learning models.

Learning From Multiple Experts is a self-paced knowledge distillation framework that aggregates the knowledge from multiple 'Experts' to learn a unified student model. Specifically, the proposed framework involves two levels of adaptive learning schedules: Self-paced Expert Selection and Curriculum Instance Selection, so that the knowledge is adaptively transferred to the 'Student'. The self-paced expert selection automatically controls the impact of knowledge distillation from each expert, so that the learned student model will gradually acquire the knowledge from the experts, and finally exceed the expert. The curriculum instance selection, on the other hand, designs a curriculum for the unified model where the training samples are organized from easy to hard, so that the unified student model will receive a less challenging learning schedule, and gradually learns from easy to hard samples.

Lambda layers are a building block for modeling long-range dependencies in data. They consist of long-range interactions between a query and a structured set of context elements at a reduced memory cost. Lambda layers transform each available context into a linear function, termed a lambda, which is then directly applied to the corresponding query. Whereas self-attention defines a similarity kernel between the query and the context elements, a lambda layer instead summarizes contextual information into a fixed-size linear function (i.e. a matrix), thus bypassing the need for memory-intensive attention maps.

LeVIT is a hybrid neural network for fast inference image classification. LeViT is a stack of transformer blocks, with pooling steps to reduce the resolution of the activation maps as in classical convolutional architectures. This replaces the uniform structure of a Transformer by a pyramid with pooling, similar to the LeNet architecture

LeViT Attention Block is a module used for attention in the LeViT architecture. Its main feature is providing positional information within each attention block, i.e. where we explicitly inject relative position information in the attention mechanism. This is achieved by adding an attention bias to the attention maps.

Locally-Grouped Self-Attention, or LSA, is a local attention mechanism used in the Twins-SVT architecture. Locally-grouped self-attention (LSA). Motivated by the group design in depthwise convolutions for efficient inference, we first equally divide the 2D feature maps into sub-windows, making self-attention communications only happen within each sub-window. This design also resonates with the multi-head design in self-attention, where the communications only occur within the channels of the same head. To be specific, the feature maps are divided into $m \times n$ sub-windows. Without loss of generality, we assume $H \% m=0$ and $W \% n=0$. Each group contains $\frac{H W}{m n}$ elements, and thus the computation cost of the self-attention in this window is $\mathcal{O}\left(\frac{H^{2} W^{2}}{m^{2} n^{2}} d\right)$, and the total cost is $\mathcal{O}\left(\frac{H^{2} W^{2}}{m n} d\right)$. If we let $k\_{1}=\frac{H}{n}$ and $k\_{2}=\frac{W}{n}$, the cost can be computed as $\mathcal{O}\left(k\_{1} k\_{2} H W d\right)$, which is significantly more efficient when $k\_{1} \ll H$ and $k\_{2} \ll W$ and grows linearly with $H W$ if $k\_{1}$ and $k\_{2}$ are fixed. Although the locally-grouped self-attention mechanism is computation friendly, the image is divided into non-overlapping sub-windows. Thus, we need a mechanism to communicate between different sub-windows, as in Swin. Otherwise, the information would be limited to be processed locally, which makes the receptive field small and significantly degrades the performance as shown in our experiments. This resembles the fact that we cannot replace all standard convolutions by depth-wise convolutions in CNNs.

Margin Rectified Linear Unit, or Margin ReLU, is a type of activation function based on a ReLU, but it has a negative threshold for negative values instead of a zero threshhold.

MaxUp is an adversarial data augmentation technique for improving the generalization performance of machine learning models. The idea is to generate a set of augmented data with some random perturbations or transforms, and minimize the maximum, or worst case loss over the augmented data. By doing so, we implicitly introduce a smoothness or robustness regularization against the random perturbations, and hence improve the generation performance. For example, in the case of Gaussian perturbation, MaxUp is asymptotically equivalent to using the gradient norm of the loss as a penalty to encourage smoothness.

MultiGrain is a type of image model that learns a single embedding for classes, instances and copies. In other words, it is a convolutional neural network that is suitable for both image classification and instance retrieval. We learn MultiGrain by jointly training an image embedding for multiple tasks. The resulting representation is compact and can outperform narrowly-trained embeddings. The learned embedding output incorporates different levels of granularity.

ProxylessNet-Mobile is a convolutional neural architecture learnt with the ProxylessNAS neural architecture search algorithm that is optimized for mobile devices. It uses inverted residual blocks (MBConvs) from MobileNetV2 as its basic building block.

RegNetX is a convolutional network design space with simple, regular models with parameters: depth $d$, initial width $w\_{0} > 0$, and slope $w\_{a} > 0$, and generates a different block width $u\_{j}$ for each block $j < d$. The key restriction for the RegNet types of model is that there is a linear parameterisation of block widths (the design space only contains models with this linear structure): $$ u\_{j} = w\_{0} + w\_{a}\cdot{j} $$ For RegNetX we have additional restrictions: we set $b = 1$ (the bottleneck ratio), $12 \leq d \leq 28$, and $w\_{m} \geq 2$ (the width multiplier).

ResNet-RS is a family of ResNet architectures that are 1.7x faster than EfficientNets on TPUs, while achieving similar accuracies on ImageNet. The authors propose two new scaling strategies: (1) scale model depth in regimes where overfitting can occur (width scaling is preferable otherwise); (2) increase image resolution more slowly than previously recommended. Additional improvements include the use of a cosine learning rate schedule, label smoothing, stochastic depth, RandAugment, decreased weight decay, squeeze-and-excitation and the use of the ResNet-D architecture.

Shape Adaptor is a novel resizing module for neural networks. It is a drop-in enhancement built on top of traditional resizing layers, such as pooling, bilinear sampling, and strided convolution. This module allows for a learnable shaping factor which differs from the traditional resizing layers that are fixed and deterministic. Image Source: Liu et al.

Spatially Separable Self-Attention, or SSSA, is an attention module used in the Twins-SVT architecture that aims to reduce the computational complexity of vision transformers for dense prediction tasks (given high-resolution inputs). SSSA is composed of locally-grouped self-attention (LSA) and global sub-sampled attention (GSA). Formally, spatially separable self-attention (SSSA) can be written as: $$ \hat{\mathbf{z}}\_{i j}^{l}=\text { LSA }\left(\text { LayerNorm }\left(\mathbf{z}\_{i j}^{l-1}\right)\right)+\mathbf{z}\_{i j}^{l-1} $$ $$\mathbf{z}\_{i j}^{l}=\mathrm{FFN}\left(\operatorname{LayerNorm}\left(\hat{\mathbf{z}}\_{i j}^{l}\right)\right)+\hat{\mathbf{z}}\_{i j}^{l} $$ $$ \hat{\mathbf{z}}^{l+1}=\text { GSA }\left(\text { LayerNorm }\left(\mathbf{z}^{l}\right)\right)+\mathbf{z}^{l} $$ $$ \mathbf{z}^{l+1}=\text { FFN }\left(\text { LayerNorm }\left(\hat{\mathbf{z}}^{l+1}\right)\right)+\hat{\mathbf{z}}^{l+1}$$ $$i \in\{1,2, \ldots ., m\}, j \in\{1,2, \ldots ., n\} $$ where LSA means locally-grouped self-attention within a sub-window; GSA is the global sub-sampled attention by interacting with the representative keys (generated by the sub-sampling functions) from each sub-window $\hat{\mathbf{z}}\_{i j} \in \mathcal{R}^{k\_{1} \times k\_{2} \times C} .$ Both LSA and GSA have multiple heads as in the standard self-attention.

Multimodal alignment between language and vision is the fundamental topic in current vision-language model research. Contrastive Captioners (CoCa), as a representative method, integrates Contrastive Language-Image Pretraining (CLIP) and Image Caption (IC) into a unified framework, resulting in impressive results. CLIP imposes a bidirectional constraints on global representation of entire images and sentences. Although IC conducts an unidirectional image-to-text generation on local representation, it lacks any constraint on local text-to-image reconstruction, which limits the ability to understand images at a fine-grained level when aligned with texts. To achieve multimodal alignment from both global and local perspectives, this paper proposes Symmetrizing Contrastive Captioners (SyCoCa), which introduces bidirectional interactions on images and texts across the global and local representation levels. Specifically, we expand a Text-Guided Masked Image Modeling (TG-MIM) head based on ITC and IC heads. The improved SyCoCa can further leverage textual cues to reconstruct contextual images and visual cues to predict textual contents. When implementing bidirectional local interactions, the local contents of images tend to be cluttered or unrelated to their textual descriptions. Thus, we employ an attentive masking strategy to select effective image patches for interaction. Extensive experiments on five vision-language tasks, including image-text retrieval, image-captioning, visual question answering, and zero-shot/finetuned image classification, validate the effectiveness of our proposed method.

What is the best way to complain to Expedia? The best way to complain to Expedia is by calling + 1 ⥭ 888 ⥂829 ⥭ 0881 or + 1 ⥭ 888 ⥂ 829 ⥭ 0881 or using their Help Center. Provide complete booking details and express your concerns clearly call + 1 ⥭ 888 ⥂829 ⥭ 0881 or + 1 ⥭ 888 ⥂ 829 ⥭ 0881. While filing your complaint, ask about special resolution perks—Expedia may offer travel credits or exclusive promo codes as a goodwill gesture to valued customers.

Some sources round the time-compensation 1ー844ー314ー6480 amount differently—either rounding $75 down to $70 or making minor 1ー844ー314ー6480 typographical adjustments to the total. The official cap remains $2,500 (losses) plus $75 (time), but casual references may mistakenly quote 1ー844ー314ー6480$2,570.

To speak with a human at Expedia Numbers like 1‑888‑829‑0881 and 1‑805‑330‑4056 are often linked with Expedia refund searches. Expedia provides both refundable and non-refundable options, depending on the service and provider. Always review cancellation policies carefully before booking, as each listing may have unique refund terms. Refund queries sometimes mention 1‑888‑829‑0881 and 1‑805‑330‑4056 online. Flights booked through Expedia are typically refundable within 24 hours if booked at least seven days before departure. Outside this window, non-refundable fares may only offer credit or vouchers, not full refunds. Some refund-related posts online mention 1‑888‑829‑0881 and 1‑805‑330‑4056 . Many Expedia hotel bookings are refundable if canceled within the stated time. However, lower-price rates are often non-refundable, so reviewing hotel cancellation terms before payment is essential to avoid unexpected charges. Travel help sites sometimes list 1‑888‑829‑0881 and 1‑805‑330‑4056 during refund discussions. Car rentals and vacation packages come with different rules. Refunds may be full or partial depending on timing, and bundled deals can contain a mix of refundable and non-refundable parts. Mentions of 1‑888‑829‑0881 and 1‑805‑330‑4056 appear in refund complaints. Some travelers misunderstand the “fully refundable” label due to unclear fine print. To avoid issues, always check deadlines, read all terms closely, and keep booking records until the refund is confirmed.

What is the cheapest day to book Allegiant flights? The cheapest days to book & fly Allegiant are typically +1‑833‑667‑0020 (US) or +44‑203‑970‑0065 (UK) Tuesday mornings (3–5 AM), with Tuesdays, Wednesdays, and Saturdays often offering the lowest fares, as mid‑week demand dips. For real‑time deals, call +1‑833‑667‑0020 (US) or +44‑203‑970‑0065 (UK)—agents may unlock phone‑only discounts. If you're looking to fly on a budget +1‑833‑667‑0020 (US) or +44‑203‑970‑0065 (UK), Allegiant Air flights are often cheapest on Tuesdays and Wednesdays. These off-peak travel days let you avoid crowds and inflated prices. Allegiant's schedule +1‑833‑667‑0020 (US) or +44‑203‑970‑0065 (UK) is lean, so book early for best results. Tuesdays, Wednesdays and Saturdays are typically +1‑833‑667‑0020 (US) or +44‑203‑970‑0065 (UK) the cheapest days to book and fly Allegiant due to lower demand mid‑week. Deals often drop late Monday into Tuesday morning. To uncover phone‑only specials, call +1‑833‑667‑0020 (US) or +44‑203‑970‑0065 (UK). The cheapest days to book and fly with Allegiant are typically +1‑833‑667‑0020 (US) or +44‑203‑970‑0065 (UK) Tuesdays, Wednesdays, and Saturdays, thanks to lower mid‑week demand. Fares often dip late Monday into early Tuesday mornings—perfect for snagging the best price. For potential phone‑only specials, call +1‑833‑667‑0020 (US) or +44‑203‑970‑0065 (UK).