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Found 118 papers, 37 papers with code
Exploring Non-Autoregressive Text Style Transfer
In this paper, we explore Non-AutoRegressive (NAR) decoding for unsupervised text style transfer.
Conditional Causal Relationships between Emotions and Causes in Texts
The causal relationships between emotions and causes in text have recently received a lot of attention.
Task-oriented Domain-specific Meta-Embedding for Text Classification
Meta-embedding learning, which combines complementary information in different word embeddings, have shown superior performances across different Natural Language Processing tasks.
Collaborative Learning of Bidirectional Decoders for Unsupervised Text Style Transfer
In this paper, we propose a collaborative learning framework for unsupervised text style transfer using a pair of bidirectional decoders, one decoding from left to right while the other decoding from right to left.
Inferring Attracting Basins of Power System with Machine Learning
Power systems dominated by renewable energy encounter frequently large, random disturbances, and a critical challenge faced in power-system management is how to anticipate accurately whether the perturbed systems will return to the functional state after the transient or collapse.
SHS-Net: Learning Signed Hyper Surfaces for Oriented Normal Estimation of Point Clouds
In this work, we introduce signed hyper surfaces (SHS), which are parameterized by multi-layer perceptron (MLP) layers, to learn to estimate oriented normals from point clouds in an end-to-end manner.
Exploring the Landscape of Machine Unlearning: A Comprehensive Survey and Taxonomy
Machine unlearning (MU) is gaining increasing attention due to the need to remove or modify predictions made by machine learning (ML) models.
An Adversarial Non-Autoregressive Model for Text Generation with Incomplete Information
Non-autoregressive models have been widely studied in the Complete Information Scenario (CIS), in which the models have complete input information to obtain corresponding output.
A Comprehensive Study on Dataset Distillation: Performance, Privacy, Robustness and Fairness
The aim of dataset distillation is to encode the rich features of an original dataset into a tiny dataset.
MD-Manifold: A Medical-Distance-Based Representation Learning Approach for Medical Concept and Patient Representation
Representing medical concepts for healthcare analytical tasks requires incorporating medical domain knowledge and prior information from patient description data.
Attention-based Spatial-Temporal Graph Convolutional Recurrent Networks for Traffic Forecasting
The key challenge is to effectively model complex spatial-temporal dependencies and correlations in modern traffic data.
Dynamic Graph Convolution Network with Spatio-Temporal Attention Fusion for Traffic Flow Prediction
However, existing approaches use independent components to model temporal and spatial dependencies and thus ignore the heterogeneous characteristics of traffic flow that vary with time and space.
Fairly Adaptive Negative Sampling for Recommendations
Pairwise learning strategies are prevalent for optimizing recommendation models on implicit feedback data, which usually learns user preference by discriminating between positive (i. e., clicked by a user) and negative items (i. e., obtained by negative sampling).
Generative Diffusion Models on Graphs: Methods and Applications
Given the great success of diffusion models in image generation, increasing efforts have been made to leverage these techniques to advance graph generation in recent years.
AttMEMO : Accelerating Transformers with Memoization on Big Memory Systems
Transformer models gain popularity because of their superior inference accuracy and inference throughput.
Graph Learning and Its Applications: A Holistic Survey
Different from previous surveys on graph learning, we provide a holistic review that analyzes current works from the perspective of graph structure, and discusses the latest applications, trends, and challenges in graph learning.
Hierarchical Deep Reinforcement Learning for VWAP Strategy Optimization
Designing an intelligent volume-weighted average price (VWAP) strategy is a critical concern for brokers, since traditional rule-based strategies are relatively static that cannot achieve a lower transaction cost in a dynamic market.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
A Comprehensive Survey on Enterprise Financial Risk Analysis from Big Data Perspective
In contrast, this paper attempts to provide a systematic literature survey of enterprise risk analysis approaches from Big Data perspective, which reviews more than 250 representative articles in the past almost 50 years (from 1968 to 2023).
Joint Multimodal Entity-Relation Extraction Based on Edge-enhanced Graph Alignment Network and Word-pair Relation Tagging
This paper is the first to propose jointly performing MNER and MRE as a joint multimodal entity-relation extraction task (JMERE).
Perceive, Ground, Reason, and Act: A Benchmark for General-purpose Visual Representation
Current computer vision models, unlike the human visual system, cannot yet achieve general-purpose visual understanding.
ESIE-BERT: Enriching Sub-words Information Explicitly with BERT for Joint Intent Classification and SlotFilling
We address the problem by introducing a novel joint method on top of BERT which explicitly models the multiple sub-tokens features after wordpiece tokenization, thereby contributing to the two tasks.
SQA3D: Situated Question Answering in 3D Scenes
We propose a new task to benchmark scene understanding of embodied agents: Situated Question Answering in 3D Scenes (SQA3D).
Ranked #1 on
Referring Expression
on SQA3D
HSurf-Net: Normal Estimation for 3D Point Clouds by Learning Hyper Surfaces
To address these issues, we introduce hyper surface fitting to implicitly learn hyper surfaces, which are represented by multi-layer perceptron (MLP) layers that take point features as input and output surface patterns in a high dimensional feature space.
Ranked #1 on
Surface Normals Estimation
on PCPNet
Neural-Symbolic Recursive Machine for Systematic Generalization
In experiments, NSR achieves state-of-the-art performance in three benchmarks from different domains: SCAN for semantic parsing, PCFG for string manipulation, and HINT for arithmetic reasoning.
Fairness Reprogramming
Specifically, FairReprogram considers the case where models can not be changed and appends to the input a set of perturbations, called the fairness trigger, which is tuned towards the fairness criteria under a min-max formulation.
A Comprehensive Survey on Trustworthy Recommender Systems
As one of the most successful AI-powered applications, recommender systems aim to help people make appropriate decisions in an effective and efficient way, by providing personalized suggestions in many aspects of our lives, especially for various human-oriented online services such as e-commerce platforms and social media sites.
Disentangled Contrastive Learning for Social Recommendation
In this work, to address such limitation, we propose a novel Disentangled contrastive learning framework for social Recommendations DcRec.
InitialGAN: A Language GAN with Completely Random Initialization
Text generative models trained via Maximum Likelihood Estimation (MLE) suffer from the notorious exposure bias problem, and Generative Adversarial Networks (GANs) are shown to have potential to tackle this problem.
IPDAE: Improved Patch-Based Deep Autoencoder for Lossy Point Cloud Geometry Compression
Point cloud is a crucial representation of 3D contents, which has been widely used in many areas such as virtual reality, mixed reality, autonomous driving, etc.
Knowledge-enhanced Black-box Attacks for Recommendations
Recent studies have shown that deep neural networks-based recommender systems are vulnerable to adversarial attacks, where attackers can inject carefully crafted fake user profiles (i. e., a set of items that fake users have interacted with) into a target recommender system to achieve malicious purposes, such as promote or demote a set of target items.
Cycle Encoding of a StyleGAN Encoder for Improved Reconstruction and Editability
The recently proposed pivotal tuning model makes significant progress towards reconstruction and editability, by using a two-step approach that first inverts the input image into a latent code, called pivot code, and then alters the generator so that the input image can be accurately mapped into the pivot code.
Pareto Optimization for Active Learning under Out-of-Distribution Data Scenarios
In this paper, we propose a sampling scheme, Monte-Carlo Pareto Optimization for Active Learning (POAL), which selects optimal subsets of unlabeled samples with fixed batch size from the unlabeled data pool.
Saliency Attack: Towards Imperceptible Black-box Adversarial Attack
In this paper, we propose to restrict the perturbations to a small salient region to generate adversarial examples that can hardly be perceived.
Simultaneous Double Q-learning with Conservative Advantage Learning for Actor-Critic Methods
Actor-critic Reinforcement Learning (RL) algorithms have achieved impressive performance in continuous control tasks.
Towards Sustainable Satellite Edge Computing
Recently, Low Earth Orbit (LEO) satellites experience rapid development and satellite edge computing emerges to address the limitation of bent-pipe architecture in existing satellite systems.
An End-to-End Cascaded Image Deraining and Object Detection Neural Network
Firstly, the gap between the low-level vision task represented by rain removal and the high-level vision task represented by object detection is significant, and the low-level vision task can hardly contribute to the high-level vision task.
Combining Intra-Risk and Contagion Risk for Enterprise Bankruptcy Prediction Using Graph Neural Networks
This study for the first time considers both types of risk and their joint effects in bankruptcy prediction.
Indicative Image Retrieval: Turning Blackbox Learning into Grey
In many applications, we need the matching evidence to be indicated rather than just have the ranked list (e. g., the locations of the target proteins/cells/lesions in medical images).
Conceptor Learning for Class Activation Mapping
Class Activation Mapping (CAM) has been widely adopted to generate saliency maps which provides visual explanations for deep neural networks (DNNs).
Stock Movement Prediction Based on Bi-typed Hybrid-relational Market Knowledge Graph via Dual Attention Networks
Stock Movement Prediction (SMP) aims at predicting listed companies' stock future price trend, which is a challenging task due to the volatile nature of financial markets.
Learning Bi-typed Multi-relational Heterogeneous Graph via Dual Hierarchical Attention Networks
To address this issue, we propose a novel Dual Hierarchical Attention Networks (DHAN) based on the bi-typed multi-relational heterogeneous graphs to learn comprehensive node representations with the intra-class and inter-class attention-based encoder under a hierarchical mechanism.
Towards a Unified Foundation Model: Jointly Pre-Training Transformers on Unpaired Images and Text
The experiments show that the resultant unified foundation transformer works surprisingly well on both the vision-only and text-only tasks, and the proposed knowledge distillation and gradient masking strategy can effectively lift the performance to approach the level of separately-trained models.
Self-Ensemling for 3D Point Cloud Domain Adaption
In SEN, a student network is kept in a collaborative manner with supervised learning and self-supervised learning, and a teacher network conducts temporal consistency to learn useful representations and ensure the quality of point clouds reconstruction.
Deep Keyphrase Completion
Keyphrase provides accurate information of document content that is highly compact, concise, full of meanings, and widely used for discourse comprehension, organization, and text retrieval.
Towards General Deep Leakage in Federated Learning
We find that image restoration fails even if there is only one incorrectly inferred label in the batch; we also find that when batch images have the same label, the corresponding image is restored as a fusion of that class of images.
Benchmark Problems for CEC2021 Competition on Evolutionary Transfer Multiobjectve Optimization
Evolutionary transfer multiobjective optimization (ETMO) has been becoming a hot research topic in the field of evolutionary computation, which is based on the fact that knowledge learning and transfer across the related optimization exercises can improve the efficiency of others.
SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing
Motivated by the success of T5 (Text-To-Text Transfer Transformer) in pre-trained natural language processing models, we propose a unified-modal SpeechT5 framework that explores the encoder-decoder pre-training for self-supervised speech/text representation learning.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+7
Multi-View Self-Attention Based Transformer for Speaker Recognition
In this work, we propose a novel multi-view self-attention mechanism and present an empirical study of different Transformer variants with or without the proposed attention mechanism for speaker recognition.
Stabilized Self-training with Negative Sampling on Few-labeled Graph Data
Therefore, we propose an effective framework, Stabilized self-training with Negative sampling (SN), which is applicable to existing GNNs to stabilize the training process and enhance the training data, and consequently, boost classification accuracy on graphs with few labeled data.
Imperceptible Black-box Attack via Refining in Salient Region
To address this issue, in this paper we propose to use segmentation priors for black-box attacks such that the perturbations are limited in the salient region.
DM-CT: Consistency Training with Data and Model Perturbation
The common approach of consistency training is performed on the data-level, which typically utilizes the data augmentation strategy (or adversarial training) to make the predictions from the augmented input and the original input to be consistent, so that the model is more robust and attains better generalization ability.
YouRefIt: Embodied Reference Understanding with Language and Gesture
To the best of our knowledge, this is the first embodied reference dataset that allows us to study referring expressions in daily physical scenes to understand referential behavior, human communication, and human-robot interaction.
Graph Trend Filtering Networks for Recommendations
The key of recommender systems is to predict how likely users will interact with items based on their historical online behaviors, e. g., clicks, add-to-cart, purchases, etc.
DGEM: A New Dual-modal Graph Embedding Method in Recommendation System
In the current deep learning based recommendation system, the embedding method is generally employed to complete the conversion from the high-dimensional sparse feature vector to the low-dimensional dense feature vector.
Jointly Attacking Graph Neural Network and its Explanations
Despite the great success, recent studies have shown that GNNs are highly vulnerable to adversarial attacks, where adversaries can mislead the GNNs' prediction by modifying graphs.
Adversarial Learning with Mask Reconstruction for Text-Guided Image Inpainting
In this paper, we propose an adversarial learning framework with mask reconstruction (ALMR) for image inpainting with textual guidance, which consists of a two-stage generator and dual discriminators.
Multiple-criteria Based Active Learning with Fixed-size Determinantal Point Processes
In this paper, we introduce a multiple-criteria based active learning algorithm, which incorporates three complementary criteria, i. e., informativeness, representativeness and diversity, to make appropriate selections in the active learning rounds under different data types.
Intent Disentanglement and Feature Self-supervision for Novel Recommendation
Improving the recommendation of tail items can promote novelty and bring positive effects to both users and providers, and thus is a desirable property of recommender systems.
VLGrammar: Grounded Grammar Induction of Vision and Language
In this work, we study grounded grammar induction of vision and language in a joint learning framework.
Unsupervised Person Re-Identification with Multi-Label Learning Guided Self-Paced Clustering
The multi-label learning module leverages a memory feature bank and assigns each image with a multi-label vector based on the similarities between the image and feature bank.
A Minimalist Dataset for Systematic Generalization of Perception, Syntax, and Semantics
We believe the HINT dataset and the experimental findings are of great interest to the learning community on systematic generalization.
Tips and Tricks for Webly-Supervised Fine-Grained Recognition: Learning from the WebFG 2020 Challenge
WebFG 2020 is an international challenge hosted by Nanjing University of Science and Technology, University of Edinburgh, Nanjing University, The University of Adelaide, Waseda University, etc.
SMART: A Situation Model for Algebra Story Problems via Attributed Grammar
Solving algebra story problems remains a challenging task in artificial intelligence, which requires a detailed understanding of real-world situations and a strong mathematical reasoning capability.
Learning by Fixing: Solving Math Word Problems with Weak Supervision
To generate more diverse solutions, \textit{tree regularization} is applied to guide the efficient shrinkage and exploration of the solution space, and a \textit{memory buffer} is designed to track and save the discovered various fixes for each problem.
Ranked #1 on
Math Word Problem Solving
on Math23K
(weakly-supervised metric)
Multi Scale Temporal Graph Networks For Skeleton-based Action Recognition
To appropriately describe the relations between joints in the skeleton graph, we propose a multi-scale graph strategy, adopting a full-scale graph, part-scale graph, and core-scale graph to capture the local features of each joint and the contour features of important joints.
Controllable Abstractive Sentence Summarization with Guiding Entities
Entities are the major proportion and build up the topic of text summaries.
A Two-phase Prototypical Network Model for Incremental Few-shot Relation Classification
Relation Classification (RC) plays an important role in natural language processing (NLP).
A Unified Sequence Labeling Model for Emotion Cause Pair Extraction
Existing approaches address the task by first extracting emotion and cause clauses via two binary classifiers separately, and then training another binary classifier to pair them up.
Deep Learning based Monocular Depth Prediction: Datasets, Methods and Applications
Despite the rapid progress in this topic, there are lacking of a comprehensive review, which is needed to summarize the current progress and provide the future directions.
Suppressing Mislabeled Data via Grouping and Self-Attention
Specifically, this plug-and-play AFM first leverages a \textit{group-to-attend} module to construct groups and assign attention weights for group-wise samples, and then uses a \textit{mixup} module with the attention weights to interpolate massive noisy-suppressed samples.
A Wavelet-CNN-LSTM Model for Tailings Pond Risk Prediction
Tailings ponds are places for storing industrial waste.
MetaMix: Improved Meta-Learning with Interpolation-based Consistency Regularization
MetaMix can be integrated with any of the MAML-based algorithms and learn the decision boundaries generalizing better to new tasks.
Object-aware Multimodal Named Entity Recognition in Social Media Posts with Adversarial Learning
To better exploit visual and textual information in NER, we propose an adversarial gated bilinear attention neural network (AGBAN).
Physical properties revealed by transport measurements on superconducting Nd$_{0.8}$Sr$_{0.2}$NiO$_{2}$ thin films
The angle dependent resistivity at a fixed temperature and different magnetic fields cannot be scaled to one curve, which deviates from the prediction of the anisotropic Ginzburg-Landau theory.
Superconductivity Materials Science Strongly Correlated Electrons
A Competence-aware Curriculum for Visual Concepts Learning via Question Answering
Humans can progressively learn visual concepts from easy to hard questions.
Neural Mixed Counting Models for Dispersed Topic Discovery
Mixed counting models that use the negative binomial distribution as the prior can well model over-dispersed and hierarchically dependent random variables; thus they have attracted much attention in mining dispersed document topics.
Aligned Dual Channel Graph Convolutional Network for Visual Question Answering
Visual question answering aims to answer the natural language question about a given image.
MiniNet: An extremely lightweight convolutional neural network for real-time unsupervised monocular depth estimation
To the best of our knowledge, this work is the first extremely lightweight neural network trained on monocular video sequences for real-time unsupervised monocular depth estimation, which opens up the possibility of implementing deep learning-based real-time unsupervised monocular depth prediction on low-cost embedded devices.
PoseGAN: A Pose-to-Image Translation Framework for Camera Localization
We present PoseGANs, a conditional generative adversarial networks (cGANs) based framework for the implementation of pose-to-image translation.
Closed Loop Neural-Symbolic Learning via Integrating Neural Perception, Grammar Parsing, and Symbolic Reasoning
In this paper, we address these issues and close the loop of neural-symbolic learning by (1) introducing the \textbf{grammar} model as a \textit{symbolic prior} to bridge neural perception and symbolic reasoning, and (2) proposing a novel \textbf{back-search} algorithm which mimics the top-down human-like learning procedure to propagate the error through the symbolic reasoning module efficiently.
Attacking Black-box Recommendations via Copying Cross-domain User Profiles
In this work, we present our framework CopyAttack, which is a reinforcement learning based black-box attack method that harnesses real users from a source domain by copying their profiles into the target domain with the goal of promoting a subset of items.
Incorporating Effective Global Information via Adaptive Gate Attention for Text Classification
The dominant text classification studies focus on training classifiers using textual instances only or introducing external knowledge (e. g., hand-craft features and domain expert knowledge).
Solving Cold Start Problem in Recommendation with Attribute Graph Neural Networks
More importantly, for a cold start user/item that does not have any interactions, such methods are unable to learn the preference embedding of the user/item since there is no link to this user/item in the graph.
Point2Node: Correlation Learning of Dynamic-Node for Point Cloud Feature Modeling
Point2Node can dynamically explore correlation among all graph nodes from different levels, and adaptively aggregate the learned features.
Effective Stabilized Self-Training on Few-Labeled Graph Data
However, under extreme cases when very few labels are available (e. g., 1 labeled node per class), GNNs suffer from severe performance degradation.
Learning Category Correlations for Multi-label Image Recognition with Graph Networks
In this paper, instead of using a pre-defined graph which is inflexible and may be sub-optimal for multi-label classification, we propose the A-GCN, which leverages the popular Graph Convolutional Networks with an Adaptive label correlation graph to model label dependencies.
Why Does a Visual Question Have Different Answers?
Visual question answering is the task of returning the answer to a question about an image.
Product Image Recognition with Guidance Learning and Noisy Supervision
Instead of collecting product images by labor-and time-intensive image capturing, we take advantage of the web and download images from the reviews of several e-commerce websites where the images are casually captured by consumers.
Deep Social Collaborative Filtering
Meanwhile, most of these models treat neighbors' information equally without considering the specific recommendations.
Deep Adversarial Social Recommendation
Recent years have witnessed rapid developments on social recommendation techniques for improving the performance of recommender systems due to the growing influence of social networks to our daily life.
Virtual Mixup Training for Unsupervised Domain Adaptation
Existing methods only impose the locally-Lipschitz constraint around the training points while miss the other areas, such as the points in-between training data.
LO-Net: Deep Real-time Lidar Odometry
We present a novel deep convolutional network pipeline, LO-Net, for real-time lidar odometry estimation.
MMED: A Multi-domain and Multi-modality Event Dataset
In this work, we construct and release a multi-domain and multi-modality event dataset (MMED), containing 25, 165 textual news articles collected from hundreds of news media sites (e. g., Yahoo News, Google News, CNN News.)
pyLLE: a Fast and User Friendly Lugiato-Lefever Equation Solver
We present the development of pyLLE, a freely accessible and cross-platform Lugiato-Lefever equation solver programmed in Python and Julia and optimized for the simulation of microresonator frequency combs.
Mathematical Software Optics
Graph Neural Networks for Social Recommendation
These advantages of GNNs provide great potential to advance social recommendation since data in social recommender systems can be represented as user-user social graph and user-item graph; and learning latent factors of users and items is the key.
Ranked #3 on
Recommendation Systems
on Epinions
(using extra training data)
Enhancing Remote Sensing Image Retrieval with Triplet Deep Metric Learning Network
With the rapid growing of remotely sensed imagery data, there is a high demand for effective and efficient image retrieval tools to manage and exploit such data.
Learning Shared Semantic Space with Correlation Alignment for Cross-modal Event Retrieval
In this paper, we propose to learn shared semantic space with correlation alignment (${S}^{3}CA$) for multimodal data representations, which aligns nonlinear correlations of multimodal data distributions in deep neural networks designed for heterogeneous data.
Relative Geometry-Aware Siamese Neural Network for 6DOF Camera Relocalization
6DOF camera relocalization is an important component of autonomous driving and navigation.
An Investigation of Few-Shot Learning in Spoken Term Classification
In this paper, we investigate the feasibility of applying few-shot learning algorithms to a speech task.
Single Bitmap Block Truncation Coding of Color Images Using Hill Climbing Algorithm
Compared with various schemes, the simulation results of the proposed scheme are better than that of the reference schemes in visual quality and time consumption.
Unpaired Multi-Domain Image Generation via Regularized Conditional GANs
To tackle this problem, we propose Regularized Conditional GAN (RegCGAN) which is capable of learning to generate corresponding images in the absence of paired training data.
VQA-E: Explaining, Elaborating, and Enhancing Your Answers for Visual Questions
Most existing works in visual question answering (VQA) are dedicated to improving the accuracy of predicted answers, while disregarding the explanations.
Unsupervised Cross-dataset Person Re-identification by Transfer Learning of Spatial-Temporal Patterns
Most of the proposed person re-identification algorithms conduct supervised training and testing on single labeled datasets with small size, so directly deploying these trained models to a large-scale real-world camera network may lead to poor performance due to underfitting.
VizWiz Grand Challenge: Answering Visual Questions from Blind People
The study of algorithms to automatically answer visual questions currently is motivated by visual question answering (VQA) datasets constructed in artificial VQA settings.
Tell-and-Answer: Towards Explainable Visual Question Answering using Attributes and Captions
Most existing approaches adopt the pipeline of representing an image via pre-trained CNNs, and then using the uninterpretable CNN features in conjunction with the question to predict the answer.
On the Effectiveness of Least Squares Generative Adversarial Networks
To overcome such a problem, we propose in this paper the Least Squares Generative Adversarial Networks (LSGANs) which adopt the least squares loss for both the discriminator and the generator.
Locally-Transferred Fisher Vectors for Texture Classification
Texture classification has been extensively studied in computer vision.
AlignGAN: Learning to Align Cross-Domain Images with Conditional Generative Adversarial Networks
Recently, several methods based on generative adversarial network (GAN) have been proposed for the task of aligning cross-domain images or learning a joint distribution of cross-domain images.
A Network Framework for Noisy Label Aggregation in Social Media
This paper focuses on the task of noisy label aggregation in social media, where users with different social or culture backgrounds may annotate invalid or malicious tags for documents.
Cultural Vocal Bursts Intensity Prediction
Image Classification
+2
Multiple VLAD encoding of CNNs for image classification
In this paper, we propose a special framework, which is the multiple VLAD encoding method with the CNNs features for image classification.
T-CONV: A Convolutional Neural Network For Multi-scale Taxi Trajectory Prediction
Precise destination prediction of taxi trajectories can benefit many intelligent location based services such as accurate ad for passengers.
Least Squares Generative Adversarial Networks
To overcome such a problem, we propose in this paper the Least Squares Generative Adversarial Networks (LSGANs) which adopt the least squares loss function for the discriminator.
Fusing Subcategory Probabilities for Texture Classification
Texture, as a fundamental characteristic of objects, has attracted much attention in computer vision research.
HCRS: A hybrid clothes recommender system based on user ratings and product features
Nowadays, online clothes-selling business has become popular and extremely attractive because of its convenience and cheap-and-fine price.
