Search Results for author:
Found 72 papers, 37 papers with code
Copy Motion From One to Another: Fake Motion Video Generation
Furthermore, current methods typically employ GANs with a L2 loss to assess the authenticity of the generated videos, inherently requiring a large amount of training samples to learn the texture details for adequate video generation.
RSCFed: Random Sampling Consensus Federated Semi-supervised Learning
In this paper, we present a Random Sampling Consensus Federated learning, namely RSCFed, by considering the uneven reliability among models from fully-labeled clients, fully-unlabeled clients or partially labeled clients.
Multi-modal learning for predicting the genotype of glioma
In this study, we propose a multi-modal learning framework using three separate encoders to extract features of focal tumor image, tumor geometrics and global brain networks.
BoostMIS: Boosting Medical Image Semi-supervised Learning with Adaptive Pseudo Labeling and Informative Active Annotation
In this paper, we propose a novel semi-supervised learning (SSL) framework named BoostMIS that combines adaptive pseudo labeling and informative active annotation to unleash the potential of medical image SSL models: (1) BoostMIS can adaptively leverage the cluster assumption and consistency regularization of the unlabeled data according to the current learning status.
Weakly Supervised Object Localization as Domain Adaption
To avoid this problem, this work provides a novel perspective that models WSOL as a domain adaption (DA) task, where the score estimator trained on the source/image domain is tested on the target/pixel domain to locate objects.
Motion Prediction via Joint Dependency Modeling in Phase Space
In this paper, we introduce a novel convolutional neural model to effectively leverage explicit prior knowledge of motion anatomy, and simultaneously capture both spatial and temporal information of joint trajectory dynamics.
Boosting RGB-D Saliency Detection by Leveraging Unlabeled RGB Images
The depth estimation branch is trained with RGB-D images and then used to estimate the pseudo depth maps for all unlabeled RGB images to form the paired data.
Background-aware Classification Activation Map for Weakly Supervised Object Localization
In our B-CAM, two image-level features, aggregated by pixel-level features of potential background and object locations, are used to purify the object feature from the object-related background and to represent the feature of the pure-background sample, respectively.
VMAgent: Scheduling Simulator for Reinforcement Learning
A novel simulator called VMAgent is introduced to help RL researchers better explore new methods, especially for virtual machine scheduling.
Network-wide Multi-step Traffic Volume Prediction using Graph Convolutional Gated Recurrent Neural Network
For instance, our model reduces MAE by 25. 3%, RMSE by 29. 2%, and MAPE by 20. 2%, compared to the state-of-the-art Diffusion Convolutional Recurrent Neural Network (DCRNN) model using the hourly dataset.
Fast Camouflaged Object Detection via Edge-based Reversible Re-calibration Network
Camouflaged Object Detection (COD) aims to detect objects with similar patterns (e. g., texture, intensity, colour, etc) to their surroundings, and recently has attracted growing research interest.
Domain Adaptive Semantic Segmentation without Source Data
To address these problems, we investigate domain adaptive semantic segmentation without source data, which assumes that the model is pre-trained on the source domain, and then adapting to the target domain without accessing source data anymore.
Boundary-aware Transformers for Skin Lesion Segmentation
Skin lesion segmentation from dermoscopy images is of great importance for improving the quantitative analysis of skin cancer.
Ranked #3 on
Lesion Segmentation
on ISIC 2018
Towards Robust Cross-domain Image Understanding with Unsupervised Noise Removal
In multimedia analysis, domain adaptation studies the problem of cross-domain knowledge transfer from a label rich source domain to a label scarce target domain, thus potentially alleviates the annotation requirement for deep learning models.
VIL-100: A New Dataset and A Baseline Model for Video Instance Lane Detection
Lane detection plays a key role in autonomous driving.
MT-ORL: Multi-Task Occlusion Relationship Learning
Retrieving occlusion relation among objects in a single image is challenging due to sparsity of boundaries in image.
From Synthetic to Real: Image Dehazing Collaborating with Unlabeled Real Data
Our DID-Net predicts the three component maps by progressively integrating features across scales, and refines each map by passing an independent refinement network.
Ranked #2 on
Image Dehazing
on Haze4k
Unifying Nonlocal Blocks for Neural Networks
The nonlocal-based blocks are designed for capturing long-range spatial-temporal dependencies in computer vision tasks.
Adversarial Energy Disaggregation for Non-intrusive Load Monitoring
Energy disaggregation, also known as non-intrusive load monitoring (NILM), challenges the problem of separating the whole-home electricity usage into appliance-specific individual consumptions, which is a typical application of data analysis.
A Label Management Mechanism for Retinal Fundus Image Classification of Diabetic Retinopathy
Diabetic retinopathy (DR) remains the most prevalent cause of vision impairment and irreversible blindness in the working-age adults.
A Multi-Task Network for Joint Specular Highlight Detection and Removal
Specular highlight detection and removal are fundamental and challenging tasks.
Smart Contract Vulnerability Detection: From Pure Neural Network to Interpretable Graph Feature and Expert Pattern Fusion
In this paper, we explore combining deep learning with expert patterns in an explainable fashion.
Cross-Domain Gradient Discrepancy Minimization for Unsupervised Domain Adaptation
Previous bi-classifier adversarial learning methods only focus on the similarity between the outputs of two distinct classifiers.
UGRec: Modeling Directed and Undirected Relations for Recommendation
In particular, for a directed relation, we transform the head and tail entities into the corresponding relation space to model their relation; and for an undirected co-occurrence relation, we project head and tail entities into a unique hyperplane in the entity space to minimize their distance.
DA-GCN: A Domain-aware Attentive Graph Convolution Network for Shared-account Cross-domain Sequential Recommendation
Shared-account Cross-domain Sequential recommendation (SCSR) is the task of recommending the next item based on a sequence of recorded user behaviors, where multiple users share a single account, and their behaviours are available in multiple domains.
Global Guidance Network for Breast Lesion Segmentation in Ultrasound Images
The BD modules learn additional breast lesion boundary map to enhance the boundary quality of a segmentation result refinement.
Learning the Superpixel in a Non-iterative and Lifelong Manner
Superpixel is generated by automatically clustering pixels in an image into hundreds of compact partitions, which is widely used to perceive the object contours for its excellent contour adherence.
Triple-cooperative Video Shadow Detection
The bottleneck is the lack of a well-established dataset with high-quality annotations for video shadow detection.
Involution: Inverting the Inherence of Convolution for Visual Recognition
Convolution has been the core ingredient of modern neural networks, triggering the surge of deep learning in vision.
Ranked #401 on
Image Classification
on ImageNet
Feature-level Attentive ICF for Recommendation
To demonstrate the effectiveness of our method, we design a light attention neural network to integrate both item-level and feature-level attention for neural ICF models.
Interest-aware Message-Passing GCN for Recommendation
To form the subgraphs, we design an unsupervised subgraph generation module, which can effectively identify users with common interests by exploiting both user feature and graph structure.
Deep Texture-Aware Features for Camouflaged Object Detection
Camouflaged object detection is a challenging task that aims to identify objects having similar texture to the surroundings.
A Unified Framework to Analyze and Design the Nonlocal Blocks for Neural Networks
When choosing Chebyshev graph filter, a generalized formulation can be derived for explaining the existing nonlocal-based blocks (e. g. nonlocal block, nonlocal stage, double attention block) and uses to analyze their irrationality.
Mitigating Intensity Bias in Shadow Detection via Feature Decomposition and Reweighting
These two phenomenons reveal that deep shadow detectors heavily depend on the intensity cue, which we refer to as intensity bias.
MLCask: Efficient Management of Component Evolution in Collaborative Data Analytics Pipelines
In this paper, we identify two main challenges that arise during the deployment of machine learning pipelines, and address them with the design of versioning for an end-to-end analytics system MLCask.
Learning to Detect Specular Highlights from Real-world Images
Specular highlight detection is a challenging problem, and has many applications such as shiny object detection and light source estimation.
Dual-level Semantic Transfer Deep Hashing for Efficient Social Image Retrieval
Specifically, we design a complementary dual-level semantic transfer mechanism to efficiently discover the potential semantics of tags and seamlessly transfer them into binary hash codes.
Constrained Multi-shape Evolution for Overlapping Cytoplasm Segmentation
In the shape evolution, we compensate intensity deficiency for the segmentation by introducing not only the modeled local shape priors but also global shape priors (clump--level) modeled by considering mutual shape constraints of cytoplasms in the clump.
Task-adaptive Asymmetric Deep Cross-modal Hashing
Unlike previous cross-modal hashing approaches, our learning framework jointly optimizes semantic preserving that transforms deep features of multimedia data into binary hash codes, and the semantic regression which directly regresses query modality representation to explicit label.
Multi-Feature Discrete Collaborative Filtering for Fast Cold-start Recommendation
Additionally, we develop a fast discrete optimization algorithm to directly compute the binary hash codes with simple operations.
Robust Medical Instrument Segmentation Challenge 2019
The validation of the competing methods for the three tasks (binary segmentation, multi-instance detection and multi-instance segmentation) was performed in three different stages with an increasing domain gap between the training and the test data.
A^2-GCN: An Attribute-aware Attentive GCN Model for Recommendation
Considering the fact that for different users, the attributes of an item have different influence on their preference for this item, we design a novel attention mechanism to filter the message passed from an item to a target user by considering the attribute information.
Neural Networks Weights Quantization: Target None-retraining Ternary (TNT)
Quantization of weights of deep neural networks (DNN) has proven to be an effective solution for the purpose of implementing DNNs on edge devices such as mobiles, ASICs and FPGAs, because they have no sufficient resources to support computation involving millions of high precision weights and multiply-accumulate operations.
DDNet: Dual-path Decoder Network for Occlusion Relationship Reasoning
Besides, considering the restriction of occlusion orientation presentation to occlusion orientation learning, we design a new orthogonal representation for occlusion orientation and proposed the Orthogonal Orientation Regression loss which can get rid of the unfitness between occlusion representation and learning and further prompt the occlusion orientation learning.
CANet: Cross-disease Attention Network for Joint Diabetic Retinopathy and Diabetic Macular Edema Grading
In this paper, we present a novel cross-disease attention network (CANet) to jointly grade DR and DME by exploring the internal relationship between the diseases with only image-level supervision.
A Spectral Nonlocal Block for Neural Networks
The nonlocal-based blocks are designed for capturing long-range spatial-temporal dependencies in computer vision tasks.
Distribution Matching Prototypical Network for Unsupervised Domain Adaptation
State-of-the-art Unsupervised Domain Adaptation (UDA) methods learn transferable features by minimizing the feature distribution discrepancy between the source and target domains.
Spectral Nonlocal Block for Neural Network
The nonlocal network is designed for capturing long-range spatial-temporal dependencies in several computer vision tasks.
Alleviating Feature Confusion for Generative Zero-shot Learning
An inevitable issue of such a paradigm is that the synthesized unseen features are prone to seen references and incapable to reflect the novelty and diversity of real unseen instances.
Cycle-consistent Conditional Adversarial Transfer Networks
Domain adaptation investigates the problem of cross-domain knowledge transfer where the labeled source domain and unlabeled target domain have distinctive data distributions.
Ranked #2 on
Domain Adaptation
on USPS-to-MNIST
Personalized Hashtag Recommendation for Micro-videos
The hashtags, that a user provides to a post (e. g., a micro-video), are the ones which in her mind can well describe the post content where she is interested in.
Enhancing Underexposed Photos using Perceptually Bidirectional Similarity
To obtain high-quality results free of these artifacts, we present a novel underexposed photo enhancement approach that is able to maintain the perceptual consistency.
Deep Attentive Features for Prostate Segmentation in 3D Transrectal Ultrasound
Our attention module utilizes the attention mechanism to selectively leverage the multilevel features integrated from different layers to refine the features at each individual layer, suppressing the non-prostate noise at shallow layers of the CNN and increasing more prostate details into features at deep layers.
Probabilistic Multilayer Regularization Network for Unsupervised 3D Brain Image Registration
This paper presents a novel framework for unsupervised 3D brain image registration by capturing the feature-level transformation relationships between the unaligned image and reference image.
From Zero-Shot Learning to Cold-Start Recommendation
This work, for the first time, formulates CSR as a ZSL problem, and a tailor-made ZSL method is proposed to handle CSR.
PAC-GAN: An Effective Pose Augmentation Scheme for Unsupervised Cross-View Person Re-identification
In this paper, we introduce a novel unsupervised pose augmentation cross-view person Re-Id scheme called PAC-GAN to overcome these limitations.
Fusion-supervised Deep Cross-modal Hashing
Deep hashing has recently received attention in cross-modal retrieval for its impressive advantages.
Exploring Auxiliary Context: Discrete Semantic Transfer Hashing for Scalable Image Retrieval
To address the problem, in this paper, we propose a novel hashing approach, dubbed as \emph{Discrete Semantic Transfer Hashing} (DSTH).
Adaptive Collaborative Similarity Learning for Unsupervised Multi-view Feature Selection
We propose to dynamically learn the collaborative similarity structure, and further integrate it with the ultimate feature selection into a unified framework.
Discrete Optimal Graph Clustering
2) the relaxing process of cluster labels may cause significant information loss.
Leveraging the Invariant Side of Generative Zero-Shot Learning
In this paper, we take the advantage of generative adversarial networks (GANs) and propose a novel method, named leveraging invariant side GAN (LisGAN), which can directly generate the unseen features from random noises which are conditioned by the semantic descriptions.
Ranked #3 on
Generalized Zero-Shot Learning
on SUN Attribute
SAC-Net: Spatial Attenuation Context for Salient Object Detection
This paper presents a new deep neural network design for salient object detection by maximizing the integration of local and global image context within, around, and beyond the salient objects.
Explicit Interaction Model towards Text Classification
To address this problem, we introduce the interaction mechanism to incorporate word-level matching signals into the text classification task.
Ranked #4 on
Text Classification
on Yahoo! Answers
MMALFM: Explainable Recommendation by Leveraging Reviews and Images
Then the aspect importance is integrated into a novel aspect-aware latent factor model (ALFM), which learns user's and item's latent factors based on ratings.
Bidirectional Feature Pyramid Network with Recurrent Attention Residual Modules for Shadow Detection
Second, we develop a bidirectional feature pyramid network (BFPN) to aggregate shadow contexts spanned across different CNN layers by deploying two series of RAR modules in the network to iteratively combine and refine context features: one series to refine context features from deep to shallow layers, and another series from shallow to deep layers.
Ranked #3 on
Shadow Detection
on SBU
Direction-aware Spatial Context Features for Shadow Detection and Removal
This paper presents a novel deep neural network design for shadow detection and removal by analyzing the spatial image context in a direction-aware manner.
Ranked #3 on
Shadow Removal
on ISTD
Direction-aware Spatial Context Features for Shadow Detection
To achieve this, we first formulate the direction-aware attention mechanism in a spatial recurrent neural network (RNN) by introducing attention weights when aggregating spatial context features in the RNN.
Ranked #2 on
RGB Salient Object Detection
on SBU
Joint Bi-Layer Optimization for Single-Image Rain Streak Removal
A third prior is defined on the rain-streak layer R, based on similarity of patches to the extracted rain patches.
Leveraging Weak Semantic Relevance for Complex Video Event Classification
In this paper, we propose a novel approach to automatically maximize the utility of weak semantic annotations (formalized as the semantic relevance of video shots to the target event) to facilitate video event classification.
Saliency Pattern Detection by Ranking Structured Trees
We show that the linear combination of structured labels can well model the saliency distribution in local regions.
Discrete Multi-modal Hashing with Canonical Views for Robust Mobile Landmark Search
Finally, compact binary codes are learned on intermediate representation within a tailored discrete binary embedding model which preserves visual relations of images measured with canonical views and removes the involved noises.
A Non-Local Low-Rank Framework for Ultrasound Speckle Reduction
`Speckle' refers to the granular patterns that occur in ultrasound images due to wave interference.
