2,776 machine learning methods and techniques
CAMoE is a multi-stream Corpus Alignment network with single gate Mixture-of-Experts (MoE) for video-text retrieval. The CAMoE employs Mixture-of-Experts (MoE) to extract multi-perspective video representations, including action, entity, scene, etc., then align them with the corresponding part of the text. A Dual Softmax Loss (DSL) is used to avoid the one-way optimum-match which occurs in previous contrastive methods. Introducing the intrinsic prior of each pair in a batch, DSL serves as a reviser to correct the similarity matrix and achieves the dual optimal match.
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RFB Net is a one-stage object detector that utilises a receptive field block module. It utilises a VGG16 backbone, and is otherwise quite similar to the SSD architecture.
Multi-Level Feature Pyramid Network, or MLFPN, is a feature pyramid block used in object detection models, notably M2Det. We first fuse multi-level features (i.e. multiple layers) extracted by a backbone as a base feature, and then feed it into a block of alternating joint Thinned U-shape Modules (TUM) and Feature Fusion Modules (FFM) to extract more representative, multi-level multi-scale features. Finally, we gather up the feature maps with equivalent scales to construct the final feature pyramid for object detection. Decoder layers that form the final feature pyramid are much deeper than the layers in the backbone, namely, they are more representative. Moreover, each feature map in the final feature pyramid consists of the decoder layers from multiple levels. Hence, the feature pyramid block is called Multi-Level Feature Pyramid Network (MLFPN).
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.
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.
DIoU-NMS is a type of non-maximum suppression where we use Distance IoU rather than regular DIoU, in which the overlap area and the distance between two central points of bounding boxes are simultaneously considered when suppressing redundant boxes. In original NMS, the IoU metric is used to suppress the redundant detection boxes, where the overlap area is the unique factor, often yielding false suppression for the cases with occlusion. With DIoU-NMS, we not only consider the overlap area but also central point distance between two boxes.
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.
Pixel2Style2Pixel, or pSp, is an image-to-image translation framework that is based on a novel encoder that directly generates a series of style vectors which are fed into a pretrained StyleGAN generator, forming the extended latent space. Feature maps are first extracted using a standard feature pyramid over a ResNet backbone. Then, for each of target styles, a small mapping network is trained to extract the learned styles from the corresponding feature map, where styles are generated from the small feature map, from the medium feature map, and from the largest feature map. The mapping network, map2style, is a small fully convolutional network, which gradually reduces spatial size using a set of 2-strided convolutions followed by LeakyReLU activations. Each generated 512 vector, is fed into StyleGAN, starting from its matching affine transformation, .
Side-Aware Boundary Localization
Side-Aware Boundary Localization (SABL) is a methodology for precise localization in object detection where each side of the bounding box is respectively localized with a dedicated network branch. Empirically, the authors observe that when they manually annotate a bounding box for an object, it is often much easier to align each side of the box to the object boundary than to move the box as a whole while tuning the size. Inspired by this observation, in SABL each side of the bounding box is respectively positioned based on its surrounding context. As shown in the Figure, the authors devise a bucketing scheme to improve the localization precision. For each side of a bounding box, this scheme divides the target space into multiple buckets, then determines the bounding box via two steps. Specifically, it first searches for the correct bucket, i.e., the one in which the boundary resides. Leveraging the centerline of the selected buckets as a coarse estimate, fine regression is then performed by predicting the offsets. This scheme allows very precise localization even in the presence of displacements with large variance. Moreover, to preserve precisely localized bounding boxes in the non-maximal suppression procedure, the authors also propose to adjust the classification score based on the bucketing confidences, which leads to further performance gains.
ShapeConv, or Shape-aware Convolutional layer, is a convolutional layer for processing the depth feature in indoor RGB-D semantic segmentation. The depth feature is firstly decomposed into a shape-component and a base-component, next two learnable weights are introduced to cooperate with them independently, and finally a convolution is applied on the re-weighted combination of these two components.
CTAL is a pre-training framework for strong audio-and-language representations with a Transformer, which aims to learn the intra-modality and inter-modalities connections between audio and language through two proxy tasks on a large amount of audio- and-language pairs: masked language modeling and masked cross-modal acoustic modeling. The pre-trained model is a Transformer for Audio and Language, i.e., CTAL, which consists of two modules, a language stream encoding module which adapts word as input element, and a text-referred audio stream encoder module which accepts both frame-level Mel-spectrograms and token-level output embeddings from the language stream
Single-Shot Multi-Object Tracker
Single-Shot Multi-Object Tracker or SMOT, is a tracking framework that converts any single-shot detector (SSD) model into an online multiple object tracker, which emphasizes simultaneously detecting and tracking of the object paths. Contrary to the existing tracking by detection approaches which suffer from errors made by the object detectors, SMOT adopts the recently proposed scheme of tracking by re-detection. The proposed SMOT consists of two stages. The first stage generates temporally consecutive tracklets by exploring the temporal and spatial correlations from previous frame. The second stage performs online linking of the tracklets to generate a face track for each person (better view in color).
Symmetrizing Contrastive Captioners with Attentive Masking for Multimodal Alignment
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.
Viewmaker Network is a type of generative model that learns to produce input-dependent views for contrastive learning. This network is trained jointly with an encoder network. The viewmaker network is trained adversarially to create views which increase the contrastive loss of the encoder network. Rather than directly outputting views for an image, the viewmaker instead outputs a stochastic perturbation that is added to the input. This perturbation is projected onto an sphere, controlling the effective strength of the view, similar to methods in adversarial robustness. This constrained adversarial training method enables the model to reduce the mutual information between different views while preserving useful input features for the encoder to learn from. Specifically, the encoder and viewmaker are optimized in alternating steps to minimize and maximize , respectively. An image-to-image neural network is used as the viewmaker network, with an architecture adapted from work on style transfer. This network ingests the input image and outputs a perturbation that is constrained to an sphere. The sphere's radius is determined by the volume of the input tensor times a hyperparameter , the distortion budget, which determines the strength of the applied perturbation. This perturbation is added to the input image and optionally clamped in the case of images to ensure all pixels are in .
Feature Fusion Module v2
Feature Fusion Module v2 is a feature fusion module from the M2Det object detection model, and is crucial for constructing the final multi-level feature pyramid. They use 1x1 convolution layers to compress the channels of the input features and use a concatenation operation to aggregate these feature map. FFMv2 takes the base feature and the largest output feature map of the previous Thinned U-Shape Module (TUM) – these two are of the same scale – as input, and produces the fused feature for the next TUM.
Pixel-BERT is a pre-trained model trained to align image pixels with text. The end-to-end framework includes a CNN-based visual encoder and cross-modal transformers for visual and language embedding learning. This model has three parts: one fully convolutional neural network that takes pixels of an image as input, one word-level token embedding based on BERT, and a multimodal transformer for jointly learning visual and language embedding. For language, it uses other pretraining works to use Masked Language Modeling (MLM) to predict masked tokens with surrounding text and images. For vision, it uses the random pixel sampling mechanism that makes up for the challenge of predicting pixel-level features. This mechanism is also suitable for solving overfitting issues and improving the robustness of visual features. It applies Image-Text Matching (ITM) to classify whether an image and a sentence pair match for vision and language interaction. Image captioning is required to understand language and visual semantics for cross-modality tasks like VQA. Region-based visual features extracted from object detection models like Faster RCNN are used for better performance in the newer version of the model.
PocketNet is a face recognition model family discovered through neural architecture search. The training is based on multi-step knowledge distillation.
Feature Fusion Module v1
Feature Fusion Module v1 is a feature fusion module from the M2Det object detection model, and feature fusion modules are crucial for constructing the final multi-level feature pyramid. They use 1x1 convolution layers to compress the channels of the input features and use concatenation operation to aggregate these feature map. FFMv1 takes two feature maps with different scales in backbone as input, it adopts one upsample operation to rescale the deep features to the same scale before the concatenation operation.
RIFE, or Real-time Intermediate Flow Estimation is an intermediate flow estimation algorithm for Video Frame Interpolation (VFI). Many recent flow-based VFI methods first estimate the bi-directional optical flows, then scale and reverse them to approximate intermediate flows, leading to artifacts on motion boundaries. RIFE uses a neural network named IFNet that can directly estimate the intermediate flows from coarse-to-fine with much better speed. It introduces a privileged distillation scheme for training intermediate flow model, which leads to a large performance improvement. In RIFE training, given two input frames , we directly feed them into the IFNet to approximate intermediate flows and the fusion map . During training phase, a privileged teacher refines student's results to get and based on ground truth . The student model and the teacher model are jointly trained from scratch using the reconstruction loss. The teacher's approximations are more accurate so that they can guide the student to learn.
DELG is a convolutional neural network for image retrieval that combines generalized mean pooling for global features and attentive selection for local features. The entire network can be learned end-to-end by carefully balancing the gradient flow between two heads – requiring only image-level labels. This allows for efficient inference by extracting an image’s global feature, detected keypoints and local descriptors within a single model. The model is enabled by leveraging hierarchical image representations that arise in CNNs, which are coupled to generalized mean pooling and attentive local feature detection. Secondly, a convolutional autoencoder module is adopted that can successfully learn low-dimensional local descriptors. This can be readily integrated into the unified model, and avoids the need of post-processing learning steps, such as PCA, that are commonly used. Finally, a procedure is used that enables end-to-end training of the proposed model using only image-level supervision. This requires carefully controlling the gradient flow between the global and local network heads during backpropagation, to avoid disrupting the desired representations.
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Laplacian Pyramid Network
LapStyle, or Laplacian Pyramid Network, is a feed-forward style transfer method. It uses a Drafting Network to transfer global style patterns in low-resolution, and adopts higher resolution Revision Networks to revise local styles in a pyramid manner according to outputs of multi-level Laplacian filtering of the content image. Higher resolution details can be generated by stacking Revision Networks with multiple Laplacian pyramid levels. The final stylized image is obtained by aggregating outputs of all pyramid levels. Specifically, we first generate image pyramid from content image with the help of Laplacian filter. Rough low-resolution stylized image are then generated by the Drafting Network. Then the Revision Network generates stylized detail image in high resolution. Then the final stylized image is generated by aggregating the outputs pyramid. and in an image represent Laplacian, concatenate and aggregation operation separately.
Fast-OCR is a new lightweight detection network that incorporates features from existing models focused on the speed/accuracy trade-off, such as YOLOv2, CR-NET, and Fast-YOLOv4.
NeuralRecon: Real-Time Coherent 3D Reconstruction from Monocular Video
NeuralRecon is a framework for real-time 3D scene reconstruction from a monocular video. Unlike previous methods that estimate single-view depth maps separately on each key-frame and fuse them later, NeuralRecon proposes to directly reconstruct local surfaces represented as sparse TSDF volumes for each video fragment sequentially by a neural network. A learning-based TSDF fusion module based on gated recurrent units is used to guide the network to fuse features from previous fragments. This design allows the network to capture local smoothness prior and global shape prior of 3D surfaces.
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.
Context-aware Visual Attention-based (CoVA) webpage object detection pipeline
Context-Aware Visual Attention-based end-to-end pipeline for Webpage Object Detection (CoVA) aims to learn function f to predict labels y = [] for a webpage containing N elements. The input to CoVA consists of: 1. a screenshot of a webpage, 2. list of bounding boxes [x, y, w, h] of the web elements, and 3. neighborhood information for each element obtained from the DOM tree. This information is processed in four stages: 1. the graph representation extraction for the webpage, 2. the Representation Network (RN), 3. the Graph Attention Network (GAT), and 4. a fully connected (FC) layer. The graph representation extraction computes for every web element i its set of K neighboring web elements . The RN consists of a Convolutional Neural Net (CNN) and a positional encoder aimed to learn a visual representation for each web element i ∈ {1, ..., N}. The GAT combines the visual representation of the web element i to be classified and those of its neighbors, i.e., ∀k ∈ to compute the contextual representation for web element i. Finally, the visual and contextual representations of the web element are concatenated and passed through the FC layer to obtain the classification output.
Big-Little Net is a convolutional neural network architecture for learning multi-scale feature representations. This is achieved by using a multi-branch network, which has different computational complexity at different branches with different resolutions. Through frequent merging of features from branches at distinct scales, the model obtains multi-scale features while using less computation. It consists of Big-Little Modules, which have two branches: each of which represents a separate block from a deep model and a less deep counterpart. The two branches are fused with linear combination + unit weights. These two branches are known as Big-Branch (more layers and channels at low resolutions) and Little-Branch (fewer layers and channels at high resolution).
Bilateral Guided Aggregation Layer is a feature fusion layer for semantic segmentation that aims to enhance mutual connections and fuse different types of feature representation. It was used in the BiSeNet V2 architecture. Specifically, within the BiSeNet implementation, the layer was used to employ the contextual information of the Semantic Branch to guide the feature response of Detail Branch. With different scale guidance, different scale feature representations can be captured, which inherently encodes the multi-scale information.
M2Det is a one-stage object detection model that utilises a Multi-Level Feature Pyramid Network (MLFPN) to extract features from the input image, and then similar to SSD, produces dense bounding boxes and category scores based on the learned features, followed by the non-maximum suppression (NMS) operation to produce the final results.
DALL·E 2 is a generative text-to-image model made up of two main components: a prior that generates a CLIP image embedding given a text caption, and a decoder that generates an image conditioned on the image embedding.
Polynomial Convolution
PolyConv learns continuous distributions as the convolutional filters to share the weights across different vertices of graphs or points of point clouds.
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.
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.
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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.
RPDet, or RepPoints Detector, is a anchor-free, two-stage object detection model based on deformable convolutions. RepPoints serve as the basic object representation throughout the detection system. Starting from the center points, the first set of RepPoints is obtained via regressing offsets over the center points. The learning of these RepPoints is driven by two objectives: 1) the top-left and bottom-right points distance loss between the induced pseudo box and the ground-truth bounding box; 2) the object recognition loss of the subsequent stage.
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Video Language Graph Matching Network
VLG-Net leverages recent advantages in Graph Neural Networks (GCNs) and leverages a novel multi-modality graph-based fusion method for the task of natural language video grounding.
Adversarial-Learned Loss for Domain Adaptation is a method for domain adaptation that combines adversarial learning with self-training. Specifically, the domain discriminator has to produce different corrected labels for different domains, while the feature generator aims to confuse the domain discriminator. The adversarial process finally leads to a proper confusion matrix on the target domain. In this way, ALDA takes the strengths of domain-adversarial learning and self-training based methods.
bilayer convolutional neural network
Poly-CAM
A variant of CutMix which randomly samples masks from Fourier space.
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.
BiSeNet V2 is a two-pathway architecture for real-time semantic segmentation. One pathway is designed to capture the spatial details with wide channels and shallow layers, called Detail Branch. In contrast, the other pathway is introduced to extract the categorical semantics with narrow channels and deep layers, called Semantic Branch. The Semantic Branch simply requires a large receptive field to capture semantic context, while the detail information can be supplied by the Detail Branch. Therefore, the Semantic Branch can be made very lightweight with fewer channels and a fast-downsampling strategy. Both types of feature representation are merged to construct a stronger and more comprehensive feature representation.
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