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Found 125 papers, 51 papers with code
Sparse Adversarial Attack via Perturbation Factorization
Based on this factorization, we formulate the sparse attack problem as a mixed integer programming (MIP) to jointly optimize the binary selection factors and continuous perturbation magnitudes of all pixels, with a cardinality constraint on selection factors to explicitly control the degree of sparsity.
Inserting Anybody in Diffusion Models via Celeb Basis
Empowered by the proposed celeb basis, the new identity in our customized model showcases a better concept combination ability than previous personalization methods.
Make-Your-Video: Customized Video Generation Using Textual and Structural Guidance
Our method, dubbed Make-Your-Video, involves joint-conditional video generation using a Latent Diffusion Model that is pre-trained for still image synthesis and then promoted for video generation with the introduction of temporal modules.
Robust Backdoor Attack with Visible, Semantic, Sample-Specific, and Compatible Triggers
These triggers have demonstrated strong attack performance even under backdoor defense, which aims to eliminate or suppress the backdoor effect in the model.
TaleCrafter: Interactive Story Visualization with Multiple Characters
Accurate Story visualization requires several necessary elements, such as identity consistency across frames, the alignment between plain text and visual content, and a reasonable layout of objects in images.
UCF: Uncovering Common Features for Generalizable Deepfake Detection
Deepfake detection remains a challenging task due to the difficulty of generalizing to new types of forgeries.
Multi-User Matching and Resource Allocation in Vision Aided Communications
Visual perception is an effective way to obtain the spatial characteristics of wireless channels and to reduce the overhead for communications system.
Improved Test-Time Adaptation for Domain Generalization
Generally, a TTT strategy hinges its performance on two main factors: selecting an appropriate auxiliary TTT task for updating and identifying reliable parameters to update during the test phase.
High-Fidelity Clothed Avatar Reconstruction from a Single Image
This paper presents a framework for efficient 3D clothed avatar reconstruction.
Improving Fast Adversarial Training with Prior-Guided Knowledge
This initialization is generated by using high-quality adversarial perturbations from the historical training process.
IMF: Interactive Multimodal Fusion Model for Link Prediction
To this end, we propose a two-stage multimodal fusion framework to preserve modality-specific knowledge as well as take advantage of the complementarity between different modalities.
FateZero: Fusing Attentions for Zero-shot Text-based Video Editing
We also have a better zero-shot shape-aware editing ability based on the text-to-video model.
CoordFill: Efficient High-Resolution Image Inpainting via Parameterized Coordinate Querying
Thanks to the proposed structure, we only encode the high-resolution image in a relatively low resolution for larger reception field capturing.
NL4Opt Competition: Formulating Optimization Problems Based on Their Natural Language Descriptions
The Natural Language for Optimization (NL4Opt) Competition was created to investigate methods of extracting the meaning and formulation of an optimization problem based on its text description.
DPE: Disentanglement of Pose and Expression for General Video Portrait Editing
In this paper, we introduce a novel self-supervised disentanglement framework to decouple pose and expression without 3DMMs and paired data, which consists of a motion editing module, a pose generator, and an expression generator.
T2M-GPT: Generating Human Motion from Textual Descriptions with Discrete Representations
Additionally, we conduct analyses on HumanML3D and observe that the dataset size is a limitation of our approach.
Ranked #1 on
Motion Synthesis
on HumanML3D
Generating Human Motion From Textual Descriptions With Discrete Representations
Additionally, we conduct analyses on HumanML3D and observe that the dataset size is a limitation of our approach.
Generalizable Black-Box Adversarial Attack with Meta Learning
In the scenario of black-box adversarial attack, the target model's parameters are unknown, and the attacker aims to find a successful adversarial perturbation based on query feedback under a query budget.
Learning To Generate Language-Supervised and Open-Vocabulary Scene Graph Using Pre-Trained Visual-Semantic Space
Specifically, cheap scene graph supervision data can be easily obtained by parsing image language descriptions into semantic graphs.
IPProtect: protecting the intellectual property of visual datasets during data valuation
Data trading is essential to accelerate the development of data-driven machine learning pipelines.
3D GAN Inversion with Facial Symmetry Prior
It is natural to associate 3D GANs with GAN inversion methods to project a real image into the generator's latent space, allowing free-view consistent synthesis and editing, referred as 3D GAN inversion.
High-fidelity Facial Avatar Reconstruction from Monocular Video with Generative Priors
Compared with existing works, we obtain superior novel view synthesis results and faithfully face reenactment performance.
VideoReTalking: Audio-based Lip Synchronization for Talking Head Video Editing In the Wild
Our system disentangles this objective into three sequential tasks: (1) face video generation with a canonical expression; (2) audio-driven lip-sync; and (3) face enhancement for improving photo-realism.
Fine-Grained Face Swapping via Regional GAN Inversion
We rethink face swapping from the perspective of fine-grained face editing, \textit{i. e., ``editing for swapping'' (E4S)}, and propose a framework that is based on the explicit disentanglement of the shape and texture of facial components.
Latent Video Diffusion Models for High-Fidelity Long Video Generation
Diffusion models have shown remarkable results recently but require significant computational resources.
Ranked #2 on
Video Generation
on Sky Time-lapse
SadTalker: Learning Realistic 3D Motion Coefficients for Stylized Audio-Driven Single Image Talking Face Animation
We present SadTalker, which generates 3D motion coefficients (head pose, expression) of the 3DMM from audio and implicitly modulates a novel 3D-aware face render for talking head generation.
Next3D: Generative Neural Texture Rasterization for 3D-Aware Head Avatars
We propose a novel 3D GAN framework for unsupervised learning of generative, high-quality and 3D-consistent facial avatars from unstructured 2D images.
DiffStyler: Controllable Dual Diffusion for Text-Driven Image Stylization
Despite the impressive results of arbitrary image-guided style transfer methods, text-driven image stylization has recently been proposed for transferring a natural image into the stylized one according to textual descriptions of the target style provided by the user.
Boosting the Transferability of Adversarial Attacks with Reverse Adversarial Perturbation
Furthermore, RAP can be naturally combined with many existing black-box attack techniques, to further boost the transferability.
Unsupervised Sentence Textual Similarity with Compositional Phrase Semantics
Measuring Sentence Textual Similarity (STS) is a classic task that can be applied to many downstream NLP applications such as text generation and retrieval.
Augmenting Operations Research with Auto-Formulation of Optimization Models from Problem Descriptions
We describe an augmented intelligence system for simplifying and enhancing the modeling experience for operations research.
A multi view multi stage and multi window framework for pulmonary artery segmentation from CT scans
In addition, in order to improve the segmentation performance, we adopt multi-view and multi-window level method, at the same time we employ a fine-tune strategy to mitigate the impact of inconsistent labeling.
Towards Real-World Video Deblurring by Exploring Blur Formation Process
We believe the novel realistic synthesis pipeline and the corresponding RAW video dataset can help the community to easily construct customized blur datasets to improve real-world video deblurring performance largely, instead of laboriously collecting real data pairs.
Knowledge-Injected Federated Learning
Federated learning is an emerging technique for training models from decentralized data sets.
SemAug: Semantically Meaningful Image Augmentations for Object Detection Through Language Grounding
Our method of semantically meaningful image augmentation for object detection via language grounding, SemAug, starts by calculating semantically appropriate new objects that can be placed into relevant locations in the image (the what and where problems).
Estimating Visual Information From Audio Through Manifold Learning
Therefore, audio-based methods can be useful even for applications in which only visual information is of interest Our framework is based on Manifold Learning and consists of two steps.
Prior-Guided Adversarial Initialization for Fast Adversarial Training
Based on the observation, we propose a prior-guided FGSM initialization method to avoid overfitting after investigating several initialization strategies, improving the quality of the AEs during the whole training process.
Revealing Unfair Models by Mining Interpretable Evidence
The key idea is to find solid evidence in the form of a group of data instances discriminated most by the model.
Multi-Camera View Based Proactive BS Selection and Beam Switching for V2X
In this paper, we proposed a multi-camera view based proactive BS selection and beam switching that can predict the optimal BS of the user in the future frame and switch the corresponding beam pair.
AuxMix: Semi-Supervised Learning with Unconstrained Unlabeled Data
Critically, most recent work assume that such unlabeled data is drawn from the same distribution as the labeled data.
Deep Reinforcement Learning for Exact Combinatorial Optimization: Learning to Branch
We propose a new approach for solving the data labeling and inference latency issues in combinatorial optimization based on the use of the reinforcement learning (RL) paradigm.
Exploring Structure-aware Transformer over Interaction Proposals for Human-Object Interaction Detection
Such design decomposes the process of HOI set prediction into two subsequent phases, i. e., an interaction proposal generation is first performed, and then followed by transforming the non-parametric interaction proposals into HOI predictions via a structure-aware Transformer.
Ranked #2 on
Human-Object Interaction Detection
on V-COCO
Extending Momentum Contrast with Cross Similarity Consistency Regularization
Contrastive self-supervised representation learning methods maximize the similarity between the positive pairs, and at the same time tend to minimize the similarity between the negative pairs.
Spatial Cross-Attention Improves Self-Supervised Visual Representation Learning
Unsupervised representation learning methods like SwAV are proved to be effective in learning visual semantics of a target dataset.
Fast Adversarial Training with Adaptive Step Size
While adversarial training and its variants have shown to be the most effective algorithms to defend against adversarial attacks, their extremely slow training process makes it hard to scale to large datasets like ImageNet.
Cross-subject Action Unit Detection with Meta Learning and Transformer-based Relation Modeling
Ablation study and visualization show that our MARL can eliminate identity-caused differences, thus obtaining a robust and generalized AU discriminative embedding representation.
VDTR: Video Deblurring with Transformer
For multi-frame temporal modeling, we adapt Transformer to fuse multiple spatial features efficiently.
Asynchronous, Option-Based Multi-Agent Policy Gradient: A Conditional Reasoning Approach
Multi-agent policy gradient methods have demonstrated success in games and robotics but are often limited to problems with low-level action space.
Hierarchical Reinforcement Learning
Multi-agent Reinforcement Learning
+3
Self-supervised Learning of Adversarial Example: Towards Good Generalizations for Deepfake Detection
Following this principle, we propose to enrich the "diversity" of forgeries by synthesizing augmented forgeries with a pool of forgery configurations and strengthen the "sensitivity" to the forgeries by enforcing the model to predict the forgery configurations.
LAS-AT: Adversarial Training with Learnable Attack Strategy
In this paper, we propose a novel framework for adversarial training by introducing the concept of "learnable attack strategy", dubbed LAS-AT, which learns to automatically produce attack strategies to improve the model robustness.
Membership Privacy Protection for Image Translation Models via Adversarial Knowledge Distillation
Image-to-image translation models are shown to be vulnerable to the Membership Inference Attack (MIA), in which the adversary's goal is to identify whether a sample is used to train the model or not.
StyleHEAT: One-Shot High-Resolution Editable Talking Face Generation via Pre-trained StyleGAN
Our framework elevates the resolution of the synthesized talking face to 1024*1024 for the first time, even though the training dataset has a lower resolution.
E-LANG: Energy-Based Joint Inferencing of Super and Swift Language Models
As such, it can be applied to black-box pre-trained models without a need for architectural manipulations, reassembling of modules, or re-training.
How and what to learn:The modes of machine learning
We consider weights in pathways that link neurons longitudinally from input neurons to output neurons, or simply weight pathways, as the basic units for understanding a neural network, and decompose a neural network into a series of subnetworks of such weight pathways.
Self-Attention for Incomplete Utterance Rewriting
In this paper, we propose a novel method by directly extracting the coreference and omission relationship from the self-attention weight matrix of the transformer instead of word embeddings and edit the original text accordingly to generate the complete utterance.
EBHI:A New Enteroscope Biopsy Histopathological H&E Image Dataset for Image Classification Evaluation
Traditional machine learning methods achieve maximum accuracy of 76. 02% and deep learning method achieves a maximum accuracy of 95. 37%.
Jointly Learning Knowledge Embedding and Neighborhood Consensus with Relational Knowledge Distillation for Entity Alignment
We adopt GCN-based models to learn the representation of entities by considering the graph structure and incorporating the relation semantic information into GCN via knowledge distillation.
Fair and efficient contribution valuation for vertical federated learning
We show that VerFedSV not only satisfies many desirable properties for fairness but is also efficient to compute, and can be adapted to both synchronous and asynchronous vertical federated learning algorithms.
ML4CO: Is GCNN All You Need? Graph Convolutional Neural Networks Produce Strong Baselines For Combinatorial Optimization Problems, If Tuned and Trained Properly, on Appropriate Data
The 2021 NeurIPS Machine Learning for Combinatorial Optimization (ML4CO) competition was designed with the goal of improving state-of-the-art combinatorial optimization solvers by replacing key heuristic components with machine learning models.
Mining Minority-class Examples With Uncertainty Estimates
In the real world, the frequency of occurrence of objects is naturally skewed forming long-tail class distributions, which results in poor performance on the statistically rare classes.
A recurrent neural network approach for remaining useful life prediction utilizing a novel trend features construction method
Data-driven methods for remaining useful life (RUL) prediction normally learn features from a fixed window size of a priori of degradation, which may lead to less accurate prediction results on different datasets because of the variance of local features.
FENeRF: Face Editing in Neural Radiance Fields
2D GANs can generate high fidelity portraits but with low view consistency.
Adaptive Multi-receptive Field Spatial-Temporal Graph Convolutional Network for Traffic Forecasting
AMF-STGCN extends GCN by (1) jointly modeling the complex spatial-temporal dependencies in mobile networks, (2) applying attention mechanisms to capture various Receptive Fields of heterogeneous base stations, and (3) introducing an extra decoder based on a fully connected deep network to conquer the error propagation challenge with multi-step forecasting.
Model Composition: Can Multiple Neural Networks Be Combined into a Single Network Using Only Unlabeled Data?
As an attempt to mitigate this dilemma, this paper investigates the idea of combining multiple trained neural networks using unlabeled data.
EBJR: Energy-Based Joint Reasoning for Adaptive Inference
To this end, we propose an Energy-Based Joint Reasoning (EBJR) framework that adaptively distributes the samples between shallow and deep models to achieve an accuracy close to the deep model, but latency close to the shallow one.
Repaint: Improving the Generalization of Down-Stream Visual Tasks by Generating Multiple Instances of Training Examples
Through an extensive set of experiments, we demonstrate the usefulness of the repainted examples in training, for the tasks of image classification (ImageNet) and object detection (COCO), over several state-of-the-art network architectures at different capacities, and across different data availability regimes.
Boosting Fast Adversarial Training with Learnable Adversarial Initialization
Adversarial training (AT) has been demonstrated to be effective in improving model robustness by leveraging adversarial examples for training.
Robust Physical-World Attacks on Face Recognition
Face recognition has been greatly facilitated by the development of deep neural networks (DNNs) and has been widely applied to many safety-critical applications.
Improving Fairness for Data Valuation in Horizontal Federated Learning
The success of federated learning depends largely on the participation of data owners.
Achieving Model Fairness in Vertical Federated Learning
To solve it in a federated and privacy-preserving manner, we consider the equivalent dual form of the problem and develop an asynchronous gradient coordinate-descent ascent algorithm, where some active data parties perform multiple parallelized local updates per communication round to effectively reduce the number of communication rounds.
High-Fidelity GAN Inversion for Image Attribute Editing
With a low bit-rate latent code, previous works have difficulties in preserving high-fidelity details in reconstructed and edited images.
FedFair: Training Fair Models In Cross-Silo Federated Learning
In this paper, we first propose a federated estimation method to accurately estimate the fairness of a model without infringing the data privacy of any party.
Generating Self-Contained and Summary-Centric Question Answer Pairs via Differentiable Reward Imitation Learning
Motivated by suggested question generation in conversational news recommendation systems, we propose a model for generating question-answer pairs (QA pairs) with self-contained, summary-centric questions and length-constrained, article-summarizing answers.
An Optimal Resource Allocator of Elastic Training for Deep Learning Jobs on Cloud
Cloud training platforms, such as Amazon Web Services and Huawei Cloud provide users with computational resources to train their deep learning jobs.
Auto-Split: A General Framework of Collaborative Edge-Cloud AI
At the same time, large amounts of input data are collected at the edge of cloud.
DAE-GAN: Dynamic Aspect-aware GAN for Text-to-Image Synthesis
Text-to-image synthesis refers to generating an image from a given text description, the key goal of which lies in photo realism and semantic consistency.
Data Pricing in Machine Learning Pipelines
We also investigate pricing in the step of collaborative training of machine learning models, and overview pricing machine learning models for end users in the step of machine learning deployment.
Finding Representative Interpretations on Convolutional Neural Networks
Interpreting the decision logic behind effective deep convolutional neural networks (CNN) on images complements the success of deep learning models.
An Effective and Robust Detector for Logo Detection
Moreover, we have applied the proposed methods to competition ACM MM2021 Robust Logo Detection that is organized by Alibaba on the Tianchi platform and won top 2 in 36489 teams.
SimROD: A Simple Adaptation Method for Robust Object Detection
This paper presents a Simple and effective unsupervised adaptation method for Robust Object Detection (SimROD).
Robust Counterfactual Explanations on Graph Neural Networks
Massive deployment of Graph Neural Networks (GNNs) in high-stake applications generates a strong demand for explanations that are robust to noise and align well with human intuition.
Semi-Autoregressive Transformer for Image Captioning
To tackle this issue, non-autoregressive image captioning models have recently been proposed to significantly accelerate the speed of inference by generating all words in parallel.
GasHisSDB: A New Gastric Histopathology Image Dataset for Computer Aided Diagnosis of Gastric Cancer
In order to prove that the methods of different periods in the field of image classification have discrepancies on GasHisSDB, we select a variety of classifiers for evaluation.
Metro Passenger Flow Prediction via Dynamic Hypergraph Convolution Networks
Metro passenger flow prediction is a strategically necessary demand in an intelligent transportation system to alleviate traffic pressure, coordinate operation schedules, and plan future constructions.
Generalizing Face Forgery Detection with High-frequency Features
The second is the residual-guided spatial attention module that guides the low-level RGB feature extractor to concentrate more on forgery traces from a new perspective.
Probabilistic Modeling of Semantic Ambiguity for Scene Graph Generation
The ambiguity naturally leads to the issue of \emph{implicit multi-label}, motivating the need for diverse predictions.
A Hierarchical Conditional Random Field-based Attention Mechanism Approach for Gastric Histopathology Image Classification
The HCRF-AM model consists of an Attention Mechanism (AM) module and an Image Classification (IC) module.
A Comprehensive Review of Computer-aided Whole-slide Image Analysis: from Datasets to Feature Extraction, Segmentation, Classification, and Detection Approaches
This paper reviews the methods of WSI analysis based on machine learning.
Targeted Attack against Deep Neural Networks via Flipping Limited Weight Bits
By utilizing the latest technique in integer programming, we equivalently reformulate this BIP problem as a continuous optimization problem, which can be effectively and efficiently solved using the alternating direction method of multipliers (ADMM) method.
QoS-aware Link Scheduling Strategy for Data Transmission in SDVN
The original contributions of this paper are summarized as follows: (1) Model the packets collision probability of broadcast or NACK transmission in VANET with the combination theory and investigate the potential influence of miss my packets (MMP) problem.
Networking and Internet Architecture
CaEGCN: Cross-Attention Fusion based Enhanced Graph Convolutional Network for Clustering
In this paper, we propose a cross-attention based deep clustering framework, named Cross-Attention Fusion based Enhanced Graph Convolutional Network (CaEGCN), which contains four main modules: the cross-attention fusion module which innovatively concatenates the Content Auto-encoder module (CAE) relating to the individual data and Graph Convolutional Auto-encoder module (GAE) relating to the relationship between the data in a layer-by-layer manner, and the self-supervised model that highlights the discriminative information for clustering tasks.
Adaptive Tree Wasserstein Minimization for Hierarchical Generative Modeling
In this work, we consider OT over tree metrics, which is more general than the sliced Wasserstein and includes the sliced Wasserstein as a special case, and we propose a fast minimization algorithm in $O(n)$ for the optimal Wasserstein-1 transport plan between two distributions in the tree structure.
Meta-Attack: Class-Agnostic and Model-Agnostic Physical Adversarial Attack
To tackle these issues, we propose a class-agnostic and model-agnostic physical adversarial attack model (Meta-Attack), which is able to not only generate robust physical adversarial examples by simulating color and shape distortions, but also generalize to attacking novel images and novel DNN models by accessing a few digital and physical images.
Automated Prostate Cancer Diagnosis Based on Gleason Grading Using Convolutional Neural Network
The Gleason grading system using histological images is the most powerful diagnostic and prognostic predictor of prostate cancer.
Dual ResGCN for Balanced Scene GraphGeneration
We propose to incorporate the prior about the co-occurrence of relation pairs into the graph to further help alleviate the class imbalance issue.
1.23-Tb/s per Wavelength Single-Waveguide On-Chip Optical Interconnect Enabled by Mode-division Multiplexing
We experimentally demonstrate a record net capacity per wavelength of 1. 23~Tb/s over a single silicon-on-insulator (SOI) multimode waveguide for optical interconnects employing on-chip mode-division multiplexing and 11$\times$11 multiple-in-multiple-out (MIMO) digital signal processing.
Semi-Supervised Bilingual Lexicon Induction with Two-way Interaction
In this paper, we propose a new semi-supervised BLI framework to encourage the interaction between the supervised signal and unsupervised alignment.
A Relaxed Matching Procedure for Unsupervised BLI
Recently unsupervised Bilingual Lexicon Induction (BLI) without any parallel corpus has attracted much research interest.
Too Much Information Kills Information: A Clustering Perspective
Clustering is one of the most fundamental tools in the artificial intelligence area, particularly in the pattern recognition and learning theory.
Personalized Cross-Silo Federated Learning on Non-IID Data
Non-IID data present a tough challenge for federated learning.
Effective and Robust Detection of Adversarial Examples via Benford-Fourier Coefficients
In this work, we study the detection of adversarial examples, based on the assumption that the output and internal responses of one DNN model for both adversarial and benign examples follow the generalized Gaussian distribution (GGD), but with different parameters (i. e., shape factor, mean, and variance).
Controllable Descendant Face Synthesis
Most of the existing methods train models for one-versus-one kin relation, which only consider one parent face and one child face by directly using an auto-encoder without any explicit control over the resemblance of the synthesized face to the parent face.
Structural-Aware Sentence Similarity with Recursive Optimal Transport
Moreover, we further develop Recursive Optimal Similarity (ROTS) for sentences with the valuable semantic insights from the connections between cosine similarity of weighted average of word vectors and optimal transport.
Context-Aware Cross-Attention for Skeleton-Based Human Action Recognition
To facilitate end-to-end training, we further develop a scenario context information extraction branch to extract context information from raw RGB video directly.
Ranked #66 on
Skeleton Based Action Recognition
on NTU RGB+D
Ensemble emotion recognizing with multiple modal physiological signals
Multiple physiological signals fusing models, building the uniform classification model by means of consistent and complementary information from different emotions to improve recognition performance.
LAC-Nav: Collision-Free Mutiagent Navigation Based on The Local Action Cells
Based on the realtime updated local action cells, we propose the LAC-Nav approach to navigate the agent with the properly selected velocity; and furthermore, we coupled the local action cell with an adaptive learning framework, in which the effect of selections are evaluated and used as the references for making decisions in the following updates.
Context-Aware Feature and Label Fusion for Facial Action Unit Intensity Estimation With Partially Labeled Data
The latter enables the model to be trained on a partially annotated database.
A Novel Deep Neural Network Based Approach for Sparse Code Multiple Access
Furthermore, by manipulating the mapping vectors, an autoencoder is able to generalize SCMA, thus a dense code multiple access (DCMA) scheme is proposed.
Joint Representation and Estimator Learning for Facial Action Unit Intensity Estimation
Accurate AU intensity estimation depends on three major elements: image representation, intensity estimator, and supervisory information.
Improving Distributed Similarity Join in Metric Space with Error-bounded Sampling
To address the limitation, we propose SP-Join, an end-to-end framework to support distributed similarity join in metric space based on the MapReduce paradigm, which (i) employs an estimation-based stratified sampling method to produce pivots with quality guarantees for any sample size, and (ii) devises an effective cost model as the guideline to split the whole datasets into partition in map and reduce phases according to the sampled pivots.
Databases
Exact Adversarial Attack to Image Captioning via Structured Output Learning with Latent Variables
Due to the sequential dependencies among words in a caption, we formulate the generation of adversarial noises for targeted partial captions as a structured output learning problem with latent variables.
Wasserstein-Fisher-Rao Document Distance
As a fundamental problem of natural language processing, it is important to measure the distance between different documents.
Tencent ML-Images: A Large-Scale Multi-Label Image Database for Visual Representation Learning
In this work, we propose to train CNNs from images annotated with multiple tags, to enhance the quality of visual representation of the trained CNN model.
Unifying Topic, Sentiment & Preference in an HDP-Based Rating Regression Model for Online Reviews
TSPRA combines topics (i. e. product aspects), word sentiment and user preference as regression factors, and is able to perform topic clustering, review rating prediction, sentiment analysis and what we invent as "critical aspect" analysis altogether in one framework.
Siamese Networks for Semantic Pattern Similarity
Semantic Pattern Similarity is an interesting, though not often encountered NLP task where two sentences are compared not by their specific meaning, but by their more abstract semantic pattern (e. g., preposition or frame).
A deep learning-based remaining useful life prediction approach for bearings
In industrial applications, nearly half the failures of motors are caused by the degradation of rolling element bearings (REBs).
Human activity recognition based on time series analysis using U-Net
Traditional human activity recognition (HAR) based on time series adopts sliding window analysis method.
Weakly-Supervised Deep Convolutional Neural Network Learning for Facial Action Unit Intensity Estimation
Facial action unit (AU) intensity estimation plays an important role in affective computing and human-computer interaction.
Bilateral Ordinal Relevance Multi-Instance Regression for Facial Action Unit Intensity Estimation
The majority of methods directly apply supervised learning techniques to AU intensity estimation while few methods exploit unlabeled samples to improve the performance.
Classifier Learning With Prior Probabilities for Facial Action Unit Recognition
To alleviate this issue, we propose a knowledge-driven method for jointly learning multiple AU classifiers without any AU annotation by leveraging prior probabilities on AUs, including expression-independent and expression-dependent AU probabilities.
MainiwayAI at IJCNLP-2017 Task 2: Ensembles of Deep Architectures for Valence-Arousal Prediction
This paper introduces Mainiway AI Labs submitted system for the IJCNLP 2017 shared task on Dimensional Sentiment Analysis of Chinese Phrases (DSAP), and related experiments.
The Sentimental Value of Chinese Sub-Character Components
Sub-character components of Chinese characters carry important semantic information, and recent studies have shown that utilizing this information can improve performance on core semantic tasks.
Targeted Advertising Based on Browsing History
Audience interest, demography, purchase behavior and other possible classifications are ex- tremely important factors to be carefully studied in a targeting campaign.
Schatten-$p$ Quasi-Norm Regularized Matrix Optimization via Iterative Reweighted Singular Value Minimization
In particular, we first introduce a class of first-order stationary points for them, and show that the first-order stationary points introduced in [11] for an SPQN regularized $vector$ minimization problem are equivalent to those of an SPQN regularized $matrix$ minimization reformulation.
Penalty Decomposition Methods for Rank Minimization
In this paper we consider general rank minimization problems with rank appearing in either objective function or constraint.
