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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.

Feature Non-Maximum Suppression, or FeatureNMS, is a post-processing step for object detection models that removes duplicates where there are multiple detections outputted per object. FeatureNMS recognizes duplicates not only based on the intersection over union between the bounding boxes, but also based on the difference of feature vectors. These feature vectors can encode more information like visual appearance.

A Feedback Transformer is a type of sequential transformer that exposes all previous representations to all future representations, meaning the lowest representation of the current timestep is formed from the highest-level abstract representation of the past. This feedback nature allows this architecture to perform recursive computation, building stronger representations iteratively upon previous states. To achieve this, the self-attention mechanism of the standard Transformer is modified so it attends to higher level representations rather than lower ones.

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.

Fraternal Dropout is a regularization method for recurrent neural networks that trains two identical copies of an RNN (that share parameters) with different dropout masks while minimizing the difference between their (pre-softmax) predictions. This encourages the representations of RNNs to be invariant to dropout mask, thus being robust.

Funnel Transformer is a type of Transformer that gradually compresses the sequence of hidden states to a shorter one and hence reduces the computation cost. By re-investing the saved FLOPs from length reduction in constructing a deeper or wider model, the model capacity is further improved. In addition, to perform token-level predictions as required by common pretraining objectives, Funnel-transformer is able to recover a deep representation for each token from the reduced hidden sequence via a decoder. The proposed model keeps the same overall skeleton of interleaved S-Attn and P-FFN sub-modules wrapped by residual connection and layer normalization. But differently, to achieve representation compression and computation reduction, THE model employs an encoder that gradually reduces the sequence length of the hidden states as the layer gets deeper. In addition, for tasks involving per-token predictions like pretraining, a simple decoder is used to reconstruct a full sequence of token-level representations from the compressed encoder output. Compression is achieved via a pooling operation,

G3D is a unified spatial-temporal graph convolutional operator that directly models cross-spacetime joint dependencies. It leverages dense cross-spacetime edges as skip connections for direct information propagation across the 3D spatial-temporal graph.

GAN-TTS is a generative adversarial network for text-to-speech synthesis. The architecture is composed of a conditional feed-forward generator producing raw speech audio, and an ensemble of discriminators which operate on random windows of different sizes. The discriminators analyze the audio both in terms of general realism, as well as how well the audio corresponds to the utterance that should be pronounced. The generator architecture consists of several GBlocks, which are residual based (dilated) convolution blocks. GBlocks 3–7 gradually upsample the temporal dimension of hidden representations by factors of 2, 2, 2, 3, 5, while the number of channels is reduced by GBlocks 3, 6 and 7 (by a factor of 2 each). The final convolutional layer with Tanh activation produces a single-channel audio waveform. Instead of a single discriminator, GAN-TTS uses an ensemble of Random Window Discriminators (RWDs) which operate on randomly sub-sampled fragments of the real or generated samples. The ensemble allows for the evaluation of audio in different complementary ways.

GBlock is a type of residual block used in the GAN-TTS text-to-speech architecture - it is a stack of two residual blocks. As the generator is producing raw audio (e.g. a 2s training clip corresponds to a sequence of 48000 samples), dilated convolutions are used to ensure that the receptive field of $G$ is large enough to capture long-term dependencies. The four kernel size-3 convolutions in each GBlock have increasing dilation factors: 1, 2, 4, 8. Convolutions are preceded by Conditional Batch Normalisation, conditioned on the linear embeddings of the noise term $z \sim N\left(0, \mathbf{I}\_{128}\right)$ in the single-speaker case, or the concatenation of $z$ and a one-hot representation of the speaker ID in the multi-speaker case. The embeddings are different for each BatchNorm instance. A GBlock contains two skip connections, the first of which in GAN-TTS performs upsampling if the output frequency is higher than the input, and it also contains a size-1 convolution if the number of output channels is different from the input.

Social media are nowadays one of the main news sources for millions of people around the globe due to their low cost, easy access and rapid dissemination. This however comes at the cost of dubious trustworthiness and significant risk of exposure to 'fake news', intentionally written to mislead the readers. Automatically detecting fake news poses challenges that defy existing content-based analysis approaches. One of the main reasons is that often the interpretation of the news requires the knowledge of political or social context or 'common sense', which current NLP algorithms are still missing. Recent studies have shown that fake and real news spread differently on social media, forming propagation patterns that could be harnessed for the automatic fake news detection. Propagation-based approaches have multiple advantages compared to their content-based counterparts, among which is language independence and better resilience to adversarial attacks. In this paper we show a novel automatic fake news detection model based on geometric deep learning. The underlying core algorithms are a generalization of classical CNNs to graphs, allowing the fusion of heterogeneous data such as content, user profile and activity, social graph, and news propagation. Our model was trained and tested on news stories, verified by professional fact-checking organizations, that were spread on Twitter. Our experiments indicate that social network structure and propagation are important features allowing highly accurate (92.7% ROC AUC) fake news detection. Second, we observe that fake news can be reliably detected at an early stage, after just a few hours of propagation. Third, we test the aging of our model on training and testing data separated in time. Our results point to the promise of propagation-based approaches for fake news detection as an alternative or complementary strategy to content-based approaches.

Graph Finite-State Automaton, or GFSA, is a differentiable layer for learning graph structure that adds a new edge type (expressed as a weighted adjacency matrix) to a base graph. This layer can be trained end-to-end to add derived relationships (edges) to arbitrary graph-structured data based on performance on a downstream task.

Although significant effort has been applied to fact-checking, the prevalence of fake news over social media, which has profound impact on justice, public trust and our society, remains a serious problem. In this work, we focus on propagation-based fake news detection, as recent studies have demonstrated that fake news and real news spread differently online. Specifically, considering the capability of graph neural networks (GNNs) in dealing with non-Euclidean data, we use GNNs to differentiate between the propagation patterns of fake and real news on social media. In particular, we concentrate on two questions: (1) Without relying on any text information, e.g., tweet content, replies and user descriptions, how accurately can GNNs identify fake news? Machine learning models are known to be vulnerable to adversarial attacks, and avoiding the dependence on text-based features can make the model less susceptible to the manipulation of advanced fake news fabricators. (2) How to deal with new, unseen data? In other words, how does a GNN trained on a given dataset perform on a new and potentially vastly different dataset? If it achieves unsatisfactory performance, how do we solve the problem without re-training the model on the entire data from scratch? We study the above questions on two datasets with thousands of labelled news items, and our results show that: (1) GNNs can achieve comparable or superior performance without any text information to state-of-the-art methods. (2) GNNs trained on a given dataset may perform poorly on new, unseen data, and direct incremental training cannot solve the problem---this issue has not been addressed in the previous work that applies GNNs for fake news detection. In order to solve the problem, we propose a method that achieves balanced performance on both existing and new datasets, by using techniques from continual learning to train GNNs incrementally.

GroIE is an RoI extractor which intends to overcome the limitation of existing extractors which select only one (the best) layer from the FPN. The intuition is that all the layers of FPN retain useful information. Therefore, the proposed layer introduces non-local building blocks and attention mechanisms to boost the performance.

Gated Attention Networks (GaAN) is a new architecture for learning on graphs. Unlike the traditional multi-head attention mechanism, which equally consumes all attention heads, GaAN uses a convolutional sub-network to control each attention head’s importance. Image credit: GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs

The Segment Anything Model (SAM) shows remarkable segmentation ability with sparse prompts like points. However, manual prompt is not always feasible, as it may not be accessible in real-world application. In this work, we aim to eliminate the need for manual prompt.The key idea is to employ Cross-modal Chains of Thought Prompting (CCTP) to reason visual prompts using the semantic information given by a generic text prompt. We introduce a test-time adaptation per-instance mechanism called Generalizable SAM (GenSAM) to automatically generate and optimize visual prompts the generic task prompt. CCTP maps a single generic text prompt onto image-specific consensus foreground and background heatmaps using vision-language models, acquiring reliable visual prompts. Moreover, to test-time adapt the visual prompts, we further propose Progressive Mask Generation (PMG) to iteratively reweight the input image, guiding the model to focus on the targets in a coarse-to-fine manner.Crucially, all network parameters are fixed, avoiding the need for additional training.Experiments demonstrate the superiority of GenSAM. Experiments on three benchmarks demonstrate that GenSAM outperforms point supervision approaches and achieves comparable results to scribble supervision ones, solely relying on general task descriptions as prompts.

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.

We are introducing a universal handwritten image augmentation method that is language-agnostic. This groundbreaking technique can be applied to handwritten images in any language worldwide, marking it as the first of its kind. There are four methods for handwritten images which are ThickOCR, ThinOCR, Elongate OCR, Line Erase OCR.

Hierarchical Style Disentanglement, or HiSD, aims to disentangle different styles in image-to-image translation models. It organizes the labels into a hierarchical structure, where independent tags, exclusive attributes, and disentangled styles are allocated from top to bottom. To make the styles identified to the tags and attributes, the authors carefully redesign the modules, phases, and objectives.

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The hybrid model couples existing macro-meteorological models developed for similar microclimates along with some minimal amount of locally-acquired meteorological and $C_n^2$ data. The hybrid model framework consists of two components, a baseline macro-meteorological model and a machine learning model trained on that baseline macro-meteorological model’s residual error over the locally-acquired training measurements.

Invertible Image Conversion Net, or IICNet, is a generic framework for reversible image conversion tasks. Unlike previous encoder-decoder based methods, IICNet maintains a highly invertible structure based on invertible neural networks (INNs) to better preserve the information during conversion. It uses a relation module and a channel squeeze layer to improve the INN nonlinearity to extract cross-image relations and the network flexibility, respectively.

Population-based intrinsically motivated goal exploration algorithms applied to real world robot learning of complex skills like tool use.

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.

InstaBoost is a data augmentation technique for instance segmentation that utilises existing instance mask annotations. Intuitively in a small neighbor area of $(x_0, y_0, 1, 0)$, the probability map $P(x, y, s, r)$ should be high-valued since images are usually continuous and redundant in pixel level. Based on this, InstaBoost is a form of augmentation where we apply object jittering that randomly samples transformation tuples from the neighboring space of identity transform $(x_0, y_0, 1, 0)$ and paste the cropped object following affine transform $\mathbf{H}$.

Kaleido-BERT(CVPR2021) is the pioneering work that focus on solving PTM in e-commerce field. It achieves SOTA performances compared with many models published in general domain.

L2M is a learning algorithm that can work for most cross-domain distribution matching tasks. It automatically learns the cross-domain distribution matching without relying on hand-crafted priors on the matching loss. Instead, L2M reduces the inductive bias by using a meta-network to learn the distribution matching loss in a data-driven way.

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.

LLM-SR pioneers the use of LLMs for scientific equation discovery and symbolic regression and shows how LLMs, with their vast scientific knowledge and coding capability, enhance equation discovery across various scientific fields.

Rana Mostafa, Hoda Baraka and AbdelMoniem Bayoumi LMOT, i.e., Light-weight Multi-Object Tracker, performs joint pedestrian detection and tracking. LMOT introduces a simplified DLA-34 encoder network to extract detection features for the current image that are computationally efficient. Furthermore, we generate efficient tracking features using a linear transformer for the prior image frame and its corresponding detection heatmap. After that, LMOT fuses both detection and tracking feature maps in a multi-layer scheme and performs a two-stage online data association relying on the Kalman filter to generate tracklets. We evaluated our model on the challenging real-world MOT16/17/20 datasets, showing LMOT significantly outperforms the state-of-the-art trackers concerning runtime while maintaining high robustness. LMOT is approximately ten times faster than state-of-the-art trackers while being only 3.8% behind in performance accuracy on average leading to a much computationally lighter model. Code: https://github.com/RanaMostafaAbdElMohsen/LMOT Paper: https://doi.org/10.1109/ACCESS.2022.3197157

LTLS is a technique for multiclass and multilabel prediction that can perform training and inference in logarithmic time and space. LTLS embeds large classification problems into simple structured prediction problems and relies on efficient dynamic programming algorithms for inference. It tackles extreme multi-class and multi-label classification problems where the size $C$ of the output space is extremely large.

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.