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Found 57 papers, 19 papers with code
Inductive Matrix Completion Using Graph Autoencoder
However, without node content (i. e., side information) for training, the user (or item) specific representation can not be learned in the inductive setting, that is, a model trained on one group of users (or items) cannot adapt to new users (or items).
An Efficient Group-based Search Engine Marketing System for E-Commerce
With the increasing scale of search engine marketing, designing an efficient bidding system is becoming paramount for the success of e-commerce companies.
Dual Attention Guided Gaze Target Detection in the Wild
In the third stage, we use the generated dual attention as guidance to perform two sub-tasks: (1) identifying whether the gaze target is inside or out of the image; (2) locating the target if inside.
The Medical Segmentation Decathlon
Segmentation is so far the most widely investigated medical image processing task, but the various segmentation challenges have typically been organized in isolation, such that algorithm development was driven by the need to tackle a single specific clinical problem.
Making CNNs Interpretable by Building Dynamic Sequential Decision Forests with Top-down Hierarchy Learning
In this paper, we propose a generic model transfer scheme to make Convlutional Neural Networks (CNNs) interpretable, while maintaining their high classification accuracy.
Glance-and-Gaze Vision Transformer
It is motivated by the Glance and Gaze behavior of human beings when recognizing objects in natural scenes, with the ability to efficiently model both long-range dependencies and local context.
Learning Inductive Attention Guidance for Partially Supervised Pancreatic Ductal Adenocarcinoma Prediction
We instantiate both the global and the local classifiers by multiple instance learning (MIL), where the attention guidance, indicating roughly where the PDAC regions are, is the key to bridging them: For global MIL based normal/PDAC classification, attention serves as a weight for each instance (voxel) during MIL pooling, which eliminates the distraction from the background; For local MIL based semi-supervised PDAC segmentation, the attention guidance is inductive, which not only provides bag-level pseudo-labels to training data without per-voxel annotations for MIL training, but also acts as a proxy of an instance-level classifier.
SpecTr: Spectral Transformer for Hyperspectral Pathology Image Segmentation
Hyperspectral imaging (HSI) unlocks the huge potential to a wide variety of applications relied on high-precision pathology image segmentation, such as computational pathology and precision medicine.
Batch Normalization with Enhanced Linear Transformation
Batch normalization (BN) is a fundamental unit in modern deep networks, in which a linear transformation module was designed for improving BN's flexibility of fitting complex data distributions.
Volumetric Medical Image Segmentation: A 3D Deep Coarse-to-fine Framework and Its Adversarial Examples
Although deep neural networks have been a dominant method for many 2D vision tasks, it is still challenging to apply them to 3D tasks, such as medical image segmentation, due to the limited amount of annotated 3D data and limited computational resources.
AutoADR: Automatic Model Design for Ad Relevance
We add the model designed by AutoADR as a sub-model into the production Ad Relevance model.
Shape-Texture Debiased Neural Network Training
To prevent models from exclusively attending on a single cue in representation learning, we augment training data with images with conflicting shape and texture information (eg, an image of chimpanzee shape but with lemon texture) and, most importantly, provide the corresponding supervisions from shape and texture simultaneously.
Ranked #233 on
Image Classification
on ImageNet
CO2: Consistent Contrast for Unsupervised Visual Representation Learning
Regarding the similarity of the query crop to each crop from other images as "unlabeled", the consistency term takes the corresponding similarity of a positive crop as a pseudo label, and encourages consistency between these two similarities.
Auxiliary-task Based Deep Reinforcement Learning for Participant Selection Problem in Mobile Crowdsourcing
Therefore, it is crucial to design a participant selection algorithm that applies to different MCS systems to achieve multiple goals.
Towards Unsupervised Learning for Instrument Segmentation in Robotic Surgery with Cycle-Consistent Adversarial Networks
Surgical tool segmentation in endoscopic images is an important problem: it is a crucial step towards full instrument pose estimation and it is used for integration of pre- and intra-operative images into the endoscopic view.
Statistical Issues and Recommendations for Clinical Trials Conducted During the COVID-19 Pandemic
The COVID-19 pandemic has had and continues to have major impacts on planned and ongoing clinical trials.
Domain Adaptive Relational Reasoning for 3D Multi-Organ Segmentation
In this paper, we present a novel unsupervised domain adaptation (UDA) method, named Domain Adaptive Relational Reasoning (DARR), to generalize 3D multi-organ segmentation models to medical data collected from different scanners and/or protocols (domains).
Segmentation for Classification of Screening Pancreatic Neuroendocrine Tumors
This work presents comprehensive results to detect in the early stage the pancreatic neuroendocrine tumors (PNETs), a group of endocrine tumors arising in the pancreas, which are the second common type of pancreatic cancer, by checking the abdominal CT scans.
Synthesize then Compare: Detecting Failures and Anomalies for Semantic Segmentation
The ability to detect failures and anomalies are fundamental requirements for building reliable systems for computer vision applications, especially safety-critical applications of semantic segmentation, such as autonomous driving and medical image analysis.
Detecting Pancreatic Ductal Adenocarcinoma in Multi-phase CT Scans via Alignment Ensemble
Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers among the population.
Deep Distance Transform for Tubular Structure Segmentation in CT Scans
Tubular structure segmentation in medical images, e. g., segmenting vessels in CT scans, serves as a vital step in the use of computers to aid in screening early stages of related diseases.
Deeply Shape-guided Cascade for Instance Segmentation
The key to a successful cascade architecture for precise instance segmentation is to fully leverage the relationship between bounding box detection and mask segmentation across multiple stages.
Rethinking Normalization and Elimination Singularity in Neural Networks
To address this issue, we propose BatchChannel Normalization (BCN), which uses batch knowledge to avoid the elimination singularities in the training of channel-normalized models.
TDAPNet: Prototype Network with Recurrent Top-Down Attention for Robust Object Classification under Partial Occlusion
Despite deep convolutional neural networks' great success in object classification, it suffers from severe generalization performance drop under occlusion due to the inconsistency between training and testing data.
Deep Differentiable Random Forests for Age Estimation
Both of them connect split nodes to the top layer of convolutional neural networks (CNNs) and deal with inhomogeneous data by jointly learning input-dependent data partitions at the split nodes and age distributions at the leaf nodes.
Learning to Find Correlated Features by Maximizing Information Flow in Convolutional Neural Networks
We argue that the discard of the correlated discriminative information is partially caused by the fact that the minimization of the classification loss doesn't ensure to learn the overall discriminative information but only the most discriminative information.
Stability and Optimization Error of Stochastic Gradient Descent for Pairwise Learning
From this fundamental trade-off, we obtain lower bounds for the optimization error of SGD algorithms and the excess expected risk over a class of pairwise losses.
Micro-Batch Training with Batch-Channel Normalization and Weight Standardization
Batch Normalization (BN) has become an out-of-box technique to improve deep network training.
Ranked #38 on
Instance Segmentation
on COCO minival
Learning from Adversarial Features for Few-Shot Classification
Then we use the adversarial region attention to aggregate the feature maps to obtain the adversarial features.
Learning to generate filters for convolutional neural networks
In this paper, we propose to generate sample-specific filters for convolutional layers in the forward pass.
Robust Face Detection via Learning Small Faces on Hard Images
In this paper, we propose that the robustness of a face detector against hard faces can be improved by learning small faces on hard images.
Ranked #2 on
Face Detection
on PASCAL Face
Generating Attention from Classifier Activations for Fine-grained Recognition
Recent advances in fine-grained recognition utilize attention maps to localize objects of interest.
Tackling Early Sparse Gradients in Softmax Activation Using Leaky Squared Euclidean Distance
However, we find that choosing squared Euclidean distance may cause distance explosion leading gradients to be extremely sparse in the early stage of back propagation.
PCL: Proposal Cluster Learning for Weakly Supervised Object Detection
The iterative instance classifier refinement is implemented online using multiple streams in convolutional neural networks, where the first is an MIL network and the others are for instance classifier refinement supervised by the preceding one.
Ranked #1 on
Weakly Supervised Object Detection
on ImageNet
Hifi: Hierarchical feature integration for skeleton detection
In natural images, the scales (thickness) of object skeletons may dramatically vary among objects and object parts.
Resisting Large Data Variations via Introspective Transformation Network
Experimental results verify that our approach significantly improves the ability of deep networks to resist large variations between training and testing data and achieves classification accuracy improvements on several benchmark datasets, including MNIST, affNIST, SVHN, CIFAR-10 and miniImageNet.
Abdominal multi-organ segmentation with organ-attention networks and statistical fusion
To address these challenges, we introduce a novel framework for multi-organ segmentation by using organ-attention networks with reverse connections (OAN-RCs) which are applied to 2D views, of the 3D CT volume, and output estimates which are combined by statistical fusion exploiting structural similarity.
Training Multi-organ Segmentation Networks with Sample Selection by Relaxed Upper Confident Bound
Based on the fact that very hard samples might have annotation errors, we propose a new sample selection policy, named Relaxed Upper Confident Bound (RUCB).
Semi-Supervised Multi-Organ Segmentation via Deep Multi-Planar Co-Training
In multi-organ segmentation of abdominal CT scans, most existing fully supervised deep learning algorithms require lots of voxel-wise annotations, which are usually difficult, expensive, and slow to obtain.
Deep Co-Training for Semi-Supervised Image Recognition
We present Deep Co-Training, a deep learning based method inspired by the Co-Training framework.
Hi-Fi: Hierarchical Feature Integration for Skeleton Detection
In natural images, the scales (thickness) of object skeletons may dramatically vary among objects and object parts, making object skeleton detection a challenging problem.
Ranked #2 on
Object Skeleton Detection
on SK-LARGE
Deep Regression Forests for Age Estimation
Age estimation from facial images is typically cast as a nonlinear regression problem.
A 3D Coarse-to-Fine Framework for Volumetric Medical Image Segmentation
In this paper, we adopt 3D Convolutional Neural Networks to segment volumetric medical images.
Single-Shot Object Detection with Enriched Semantics
Our motivation is to enrich the semantics of object detection features within a typical deep detector, by a semantic segmentation branch and a global activation module.
Gradually Updated Neural Networks for Large-Scale Image Recognition
Our method is by introducing computation orderings to the channels within convolutional layers or blocks, based on which we gradually compute the outputs in a channel-wise manner.
Few-Shot Image Recognition by Predicting Parameters from Activations
In this paper, we are interested in the few-shot learning problem.
ScaleNet: Guiding Object Proposal Generation in Supermarkets and Beyond
We argue that estimation of object scales in images is helpful for generating object proposals, especially for supermarket images where object scales are usually within a small range.
Multi-stage Multi-recursive-input Fully Convolutional Networks for Neuronal Boundary Detection
This design is biologically-plausible, as it likes a human visual system to compare different possible segmentation solutions to address the ambiguous boundary issue.
Label Distribution Learning Forests
This paper presents label distribution learning forests (LDLFs) - a novel label distribution learning algorithm based on differentiable decision trees, which have several advantages: 1) Decision trees have the potential to model any general form of label distributions by a mixture of leaf node predictions.
A Fixed-Point Model for Pancreas Segmentation in Abdominal CT Scans
Deep neural networks have been widely adopted for automatic organ segmentation from abdominal CT scans.
Learning Residual Images for Face Attribute Manipulation
The transformation networks are responsible for the attribute manipulation and its dual operation and the discriminative network is used to distinguish the generated images from real images.
DeepSkeleton: Learning Multi-task Scale-associated Deep Side Outputs for Object Skeleton Extraction in Natural Images
By observing the relationship between the receptive field sizes of the different layers in the network and the skeleton scales they can capture, we introduce two scale-associated side outputs to each stage of the network.
Shape Recognition by Bag of Skeleton-associated Contour Parts
Contour and skeleton are two complementary representations for shape recognition.
Multi-Oriented Text Detection with Fully Convolutional Networks
In this paper, we propose a novel approach for text detec- tion in natural images.
Ranked #33 on
Scene Text Detection
on ICDAR 2015
Object Skeleton Extraction in Natural Images by Fusing Scale-associated Deep Side Outputs
Object skeleton is a useful cue for object detection, complementary to the object contour, as it provides a structural representation to describe the relationship among object parts.
DeepContour: A Deep Convolutional Feature Learned by Positive-Sharing Loss for Contour Detection
Contour detection serves as the basis of a variety of computer vision tasks such as image segmentation and object recognition.
Symmetry-Based Text Line Detection in Natural Scenes
Recently, a variety of real-world applications have triggered huge demand for techniques that can extract textual information from natural scenes.