Search Results for author:
Found 156 papers, 48 papers with code
Cream of the Crop: Distilling Prioritized Paths For One-Shot Neural Architecture Search
The experiments on ImageNet verify such path distillation method can improve the convergence ratio and performance of the hypernetwork, as well as boosting the training of subnetworks.
HDMapNet: An Online HD Map Construction and Evaluation Framework
By introducing the method and metrics, we invite the community to study this novel map learning problem.
Quasi-Dense Similarity Learning for Multiple Object Tracking
Compared to methods with similar detectors, it boosts almost 10 points of MOTA and significantly decreases the number of ID switches on BDD100K and Waymo datasets.
Ranked #1 on
One-Shot Object Detection
on PASCAL VOC 2012 val
One Shot Face Swapping on Megapixels
Extensive experiments demonstrate the superiority of MegaFS and the first megapixel level face swapping database is released for research on DeepFake detection and face image editing in the public domain.
Ranked #8 on
Face Swapping
on FaceForensics++
SRDiff: Single Image Super-Resolution with Diffusion Probabilistic Models
Single image super-resolution (SISR) aims to reconstruct high-resolution (HR) images from the given low-resolution (LR) ones, which is an ill-posed problem because one LR image corresponds to multiple HR images.
Adversarial Learning Semantic Volume for 2D/3D Face Shape Regression in the Wild
Then, an end-to-end pipeline is designed to jointly regress the proposed volumetric representation and the coordinate vector.
Ranked #3 on
Face Alignment
on AFLW2000-3D
Negative Margin Matters: Understanding Margin in Few-shot Classification
This paper introduces a negative margin loss to metric learning based few-shot learning methods.
Projecting Your View Attentively: Monocular Road Scene Layout Estimation via Cross-View Transformation
Furthermore, our model runs at 35 FPS on a single GPU, which is efficient and applicable for real-time panorama HD map reconstruction.
Autonomous Driving
Monocular Cross-View Road Scene Parsing(Road)
+2
Spatial-Adaptive Network for Single Image Denoising
An encoder-decoder structure with a context block is introduced to capture multiscale information.
Ranked #7 on
Image Denoising
on DND
(using extra training data)
Realtime Robust Malicious Traffic Detection via Frequency Domain Analysis
To this end, we propose Whisper, a realtime ML based malicious traffic detection system that achieves both high accuracy and high throughput by utilizing frequency domain features.
Revisiting Transferable Adversarial Image Examples: Attack Categorization, Evaluation Guidelines, and New Insights
Transferable adversarial examples raise critical security concerns in real-world, black-box attack scenarios.
Strike (with) a Pose: Neural Networks Are Easily Fooled by Strange Poses of Familiar Objects
Using our framework and a self-assembled dataset of 3D objects, we investigate the vulnerability of DNNs to OoD poses of well-known objects in ImageNet.
HiGitClass: Keyword-Driven Hierarchical Classification of GitHub Repositories
With the massive number of repositories available, there is a pressing need for topic-based search.
Contrastive Learning for Cold-Start Recommendation
It aims to maximize the mutual dependencies between item content and collaborative signals.
Causality Extraction based on Self-Attentive BiLSTM-CRF with Transferred Embeddings
Causality extraction from natural language texts is a challenging open problem in artificial intelligence.
Learning Disentangled Representation for One-shot Progressive Face Swapping
In addition, semantic information is introduced into the semantic-guided fusion module to control the swapped area and model the pose and expression more accurately.
Ultra-high Resolution Image Segmentation via Locality-aware Context Fusion and Alternating Local Enhancement
In particular, we introduce a novel locality-aware context fusion based segmentation model to process local patches, where the relevance between local patch and its various contexts are jointly and complementarily utilized to handle the semantic regions with large variations.
Ranked #2 on
Land Cover Classification
on DeepGlobe
Optical Aberration Correction in Postprocessing using Imaging Simulation
Due to the spatial variation in optical aberrations, which cannot be avoided during the lens design process, recent commercial cameras have shifted some of these correction tasks from optical design to postprocessing systems.
TeraVR empowers precise reconstruction of complete 3-D neuronal morphology in the whole brain
Neuron morphology is recognized as a key determinant of cell type, yet the quantitative profiling of a mammalian neuron’s complete three-dimensional (3-D) morphology remains arduous when the neuron has complex arborization and long projection.
DFGC 2021: A DeepFake Game Competition
This competition provides a common platform for benchmarking the adversarial game between current state-of-the-art DeepFake creation and detection methods.
Combining Data-driven and Model-driven Methods for Robust Facial Landmark Detection
This Estimation-Correction-Tuning process perfectly combines the advantages of the global robustness of data-driven method (FCN), outlier correction capability of model-driven method (PDM) and non-parametric optimization of RLMS.
Empirical Study of Zero-Shot NER with ChatGPT
Large language models (LLMs) exhibited powerful capability in various natural language processing tasks.
Distantly Supervised Named Entity Recognition via Confidence-Based Multi-Class Positive and Unlabeled Learning
In this paper, we study the named entity recognition (NER) problem under distant supervision.
Trackerless freehand ultrasound with sequence modelling and auxiliary transformation over past and future frames
Little benefit was observed by adding frames more than one second away from the predicted transformation, with or without LSTM-based RNNs.
Privileged Anatomical and Protocol Discrimination in Trackerless 3D Ultrasound Reconstruction
Three-dimensional (3D) freehand ultrasound (US) reconstruction without using any additional external tracking device has seen recent advances with deep neural networks (DNNs).
Long-term Dependency for 3D Reconstruction of Freehand Ultrasound Without External Tracker
Significance: The proposed new methodology with publicly available volunteer data and code for parametersing the long-term dependency, experimentally shown to be valid sources of performance improvement, which could potentially lead to better model development and practical optimisation of the reconstruction application.
MAVD: The First Open Large-Scale Mandarin Audio-Visual Dataset with Depth Information
Audio-visual speech recognition (AVSR) gains increasing attention from researchers as an important part of human-computer interaction.
Free-form Description Guided 3D Visual Graph Network for Object Grounding in Point Cloud
There are three main challenges in 3D object grounding: to find the main focus in the complex and diverse description; to understand the point cloud scene; and to locate the target object.
Heterogeneous Graph Contrastive Multi-view Learning
How to mitigate the sampling bias for heterogeneous GCL is another important problem.
Self-Improving for Zero-Shot Named Entity Recognition with Large Language Models
Exploring the application of powerful large language models (LLMs) on the named entity recognition (NER) task has drawn much attention recently.
Deep Linear Array Pushbroom Image Restoration: A Degradation Pipeline and Jitter-Aware Restoration Network
Both the proposed JARNet and LAP image synthesis pipeline establish a foundation for addressing this intricate challenge.
A Regressive Convolution Neural network and Support Vector Regression Model for Electricity Consumption Forecasting
Electricity consumption forecasting has important implications for the mineral companies on guiding quarterly work, normal power system operation, and the management.
Truth Discovery in Sequence Labels from Crowds
The proposed Aggregation method for Sequential Labels from Crowds ($AggSLC$) jointly considers the characteristics of sequential labeling tasks, workers' reliabilities, and advanced machine learning techniques.
On the Privacy Risks of Cell-Based NAS Architectures
Existing studies on neural architecture search (NAS) mainly focus on efficiently and effectively searching for network architectures with better performance.
Authorship attribution of source code by using back propagation neural network based on particle swarm optimization
Then these metrics are input to neural network for supervised learning, the weights of which are output by PSO and BP hybrid algorithm.
Non-blind optical degradation correction via frequency self-adaptive and finetune tactics
In mobile photography applications, limited volume constraints the diversity of optical design.
Brain-on-Switch: Towards Advanced Intelligent Network Data Plane via NN-Driven Traffic Analysis at Line-Speed
Many types of NNs (such as Recurrent Neural Network (RNN), and transformers) that are designed to work with sequential data have advantages over tree-based models, because they can take raw network data as input without complex feature computations on the fly.
CARTL: Cooperative Adversarially-Robust Transfer Learning
In deep learning, a typical strategy for transfer learning is to freeze the early layers of a pre-trained model and fine-tune the rest of its layers on the target domain.
IdeaReader: A Machine Reading System for Understanding the Idea Flow of Scientific Publications
Understanding the origin and influence of the publication's idea is critical to conducting scientific research.
Improving Unsupervised Relation Extraction by Augmenting Diverse Sentence Pairs
We also identify the limitation of noise-contrastive estimation (NCE) loss for relation representation learning and propose to apply margin loss for sentence pairs.
OptSLA: an Optimization-Based Approach for Sequential Label Aggregation
Our results show that the proposed OptSLA outperforms the state-of-the-art aggregation methods, and the results are easier to interpret.
Zero-shot Approach to Overcome Perturbation Sensitivity of Prompts
We empirically demonstrate that the top-ranked prompts are high-quality and significantly outperform the base prompt and the prompts generated using few-shot learning for the binary sentence-level sentiment classification task.
Anti-Distillation Backdoor Attacks: Backdoors Can Really Survive in Knowledge Distillation
In this paper, we, for the first time, propose a novel Anti-Distillation Backdoor Attack (ADBA), in which the backdoor embedded in the public teacher model can survive the knowledge distillation process and thus be transferred to secret distilled student models.
A Comprehensive Evaluation of Parameter-Efficient Fine-Tuning on Software Engineering Tasks
We hope that our findings can provide a deeper understanding of PEFT methods on various PTMs and SE downstream tasks.
Have You Merged My Model? On The Robustness of Large Language Model IP Protection Methods Against Model Merging
Model merging is a promising lightweight model empowerment technique that does not rely on expensive computing devices (e. g., GPUs) or require the collection of specific training data.
Joint Voxel and Coordinate Regression for Accurate 3D Facial Landmark Localization
Then, a stacked hourglass network is adopted to estimate the volumetric representation from coarse to fine, followed by a 3D convolution network that takes the estimated volume as input and regresses 3D coordinates of the face shape.
Ranked #1 on
3D Facial Landmark Localization
on AFLW2000-3D
Global and Local Consistent Age Generative Adversarial Networks
To utilize both global and local facial information, we propose a Global and Local Consistent Age Generative Adversarial Network (GLCA-GAN).
Learning Deep Similarity Models with Focus Ranking for Fabric Image Retrieval
Fabric image retrieval is beneficial to many applications including clothing searching, online shopping and cloth modeling.
Deep Supervised Discrete Hashing
Benefit from recent advances in deep learning, deep hashing methods have achieved promising results for image retrieval.
Learning Word Sense Embeddings from Word Sense Definitions
Word embeddings play a significant role in many modern NLP systems.
Distributed Parameter Map-Reduce
But the premise of inference is that the map procedure holds parameters for all features in the sample.
Differential and integral invariants under Mobius transformation
According to the Liouville Theorem, an important part of the conformal transformation is the Mobius transformation, so we focus on Mobius transformation and propose two differential expressions that are invariable under 2-D and 3-D Mobius transformation respectively.
Attribute-aware Face Aging with Wavelet-based Generative Adversarial Networks
Since it is difficult to collect face images of the same subject over a long range of age span, most existing face aging methods resort to unpaired datasets to learn age mappings.
Finding Similar Medical Questions from Question Answering Websites
Patients who have medical information demands tend to post questions about their health conditions on these crowdsourced Q&A websites and get answers from other users.
Electricity consumption forecasting method based on MPSO-BP neural network model
This paper deals with the problem of the electricity consumption forecasting method.
Understanding the Importance of Single Directions via Representative Substitution
Understanding the internal representations of deep neural networks (DNNs) is crucal to explain their behavior.
A Data-driven Adversarial Examples Recognition Framework via Adversarial Feature Genome
In this regard, we propose the Adversarial Feature Genome (AFG), a novel type of data that contains both the differences and features about classes.
Comparison of the Impact of Word Segmentation on Name Tagging for Chinese and Japanese
As long as (2) is ensured, the performance of word segmentation does not have appreciable impact on Chinese and Japanese name tagging.
Age Progression and Regression with Spatial Attention Modules
Age progression and regression refers to aesthetically render-ing a given face image to present effects of face aging and rejuvenation, respectively.
Improving Relation Extraction with Knowledge-attention
While attention mechanisms have been proven to be effective in many NLP tasks, majority of them are data-driven.
A cost-effective method for improving and re-purposing large, pre-trained GANs by fine-tuning their class-embeddings
Large, pre-trained generative models have been increasingly popular and useful to both the research and wider communities.
Adversarial Example in Remote Sensing Image Recognition
This added adversarial perturbation image is called an adversarial example, which poses a serious security problem for systems based on CNN model recognition results.
Understanding the Importance of Single Directions via Representative Substitution
Understanding the internal representations of deep neural networks (DNNs) is crucal to explain their behavior.
A3GAN: An Attribute-aware Attentive Generative Adversarial Network for Face Aging
Face aging, which aims at aesthetically rendering a given face to predict its future appearance, has received significant research attention in recent years.
Dual affine moment invariants
In this paper, we propose a general framework to derive moment invariants under DAT for objects in M-dimensional space with N channels, which can be called dual-affine moment invariants (DAMI).
Beyond Camera Motion Blur Removing: How to Handle Outliers in Deblurring
In this paper, we propose a novel method to handle camera motion blur with outliers.
Spatio-Temporal Dual Affine Differential Invariant for Skeleton-based Action Recognition
The dynamics of human skeletons have significant information for the task of action recognition.
Partially-Typed NER Datasets Integration: Connecting Practice to Theory
While typical named entity recognition (NER) models require the training set to be annotated with all target types, each available datasets may only cover a part of them.
Reference-guided Face Component Editing
Face portrait editing has achieved great progress in recent years.
EVIDENCEMINER: Textual Evidence Discovery for Life Sciences
EVIDENCEMINER is constructed in a completely automated way without any human effort for training data annotation.
Low-light Image Restoration with Short- and Long-exposure Raw Pairs
Using our data generation method and the proposed LSFNet, we can recover the details and color of the original scene, and improve the low-light image quality effectively.
Network Service Dependencies in Commodity Internet-of-Things Devices
We continue to see increasingly widespread deployment of IoT devices, with apparent intent to embed them in our built environment likely to accelerate if smart city and related programmes succeed.
Networking and Internet Architecture
Removing Backdoor-Based Watermarks in Neural Networks with Limited Data
In this paper, we benchmark the robustness of watermarking, and propose a novel backdoor-based watermark removal framework using limited data, dubbed WILD.
A novel combination of theoretical analysis and data-driven method for reconstruction of structural defects
To address this issue, a novel approach to quantitative reconstruction of defects using the integration of data-driven method with the guided wave scattering analysis has been proposed in this paper.
Computational Engineering, Finance, and Science J.2
Manifold learning-based feature extraction for structural defect reconstruction
Data-driven quantitative defect reconstructions using ultrasonic guided waves has recently demonstrated great potential in the area of non-destructive testing.
Style Intervention: How to Achieve Spatial Disentanglement with Style-based Generators?
Generative Adversarial Networks (GANs) with style-based generators (e. g. StyleGAN) successfully enable semantic control over image synthesis, and recent studies have also revealed that interpretable image translations could be obtained by modifying the latent code.
Robust Attacks on Deep Learning Face Recognition in the Physical World
Deep neural networks (DNNs) have been increasingly used in face recognition (FR) systems.
Supervised prediction of aging-related genes from a context-specific protein interaction subnetwork
In a systematic and comprehensive evaluation, we find that in many of the evaluation tests: (i) using an aging-specific subnetwork indeed yields more accurate aging-related gene predictions than using the entire network, and (ii) predictive methods from our framework that have not previously been used for supervised prediction of aging-related genes outperform existing prominent methods for the same purpose.
Improved supervised prediction of aging-related genes via weighted dynamic network analysis
Instead, we recently inferred a dynamic aging-specific subnetwork using a methodologically more advanced notion of network propagation (NP), which improved upon Induced dynamic aging-specific subnetwork in a different task, that of unsupervised analyses of the aging process.
Automatic Historical Feature Generation through Tree-based Method in Ads Prediction
Historical features are important in ads click-through rate (CTR) prediction, because they account for past engagements between users and ads.
Data Poisoning Attacks to Deep Learning Based Recommender Systems
Specifically, we formulate our attack as an optimization problem, such that the injected ratings would maximize the number of normal users to whom the target items are recommended.
Detecting Localized Adversarial Examples: A Generic Approach using Critical Region Analysis
Particularly, the localized adversarial examples only perturb a small and contiguous region of the target object, so that they are robust and effective in both digital and physical worlds.
Truncation-Free Matching System for Display Advertising at Alibaba
When receiving a user request, matching system (i) finds the crowds that the user belongs to; (ii) retrieves all ads that have targeted those crowds.
Data Poisoning Attacks and Defenses to Crowdsourcing Systems
Our empirical results show that the proposed defenses can substantially reduce the estimation errors of the data poisoning attacks.
Optical properties of elongated conducting grains
Extremely elongated, conducting dust particles (also known as metallic "needles" or "whiskers") are seen in carbonaceous chondrites and in samples brought back from the Itokawa asteroid.
Astrophysics of Galaxies
COSINE: A Web Server for Clonal and Subclonal Structure Inference and Evolution in Cancer Genomics
In the past decade, a variety of methods have been developed for subclonal reconstruction using bulk tumor sequencing data.
Few-shot Intent Classification and Slot Filling with Retrieved Examples
Few-shot learning arises in important practical scenarios, such as when a natural language understanding system needs to learn new semantic labels for an emerging, resource-scarce domain.
Class agnostic moving target detection by color and location prediction of moving area
Then, the color and location probability map of the moving area will be calculated through maximum a posteriori probability.
AdaptCL: Efficient Collaborative Learning with Dynamic and Adaptive Pruning
In a highly heterogeneous environment, AdaptCL achieves a training speedup of 6. 2x with a slight loss of accuracy.
Face Sketch Synthesis via Semantic-Driven Generative Adversarial Network
To tackle these challenges, we propose a novel Semantic-Driven Generative Adversarial Network (SDGAN) which embeds global structure-level style injection and local class-level knowledge re-weighting.
Partial Video Domain Adaptation with Partial Adversarial Temporal Attentive Network
For videos, such negative transfer could be triggered by both spatial and temporal features, which leads to a more challenging Partial Video Domain Adaptation (PVDA) problem.
Towards Balanced Learning for Instance Recognition
In this work, we carefully revisit the standard training practice of detectors, and find that the detection performance is often limited by the imbalance during the training process, which generally consists in three levels - sample level, feature level, and objective level.
A Hard Label Black-box Adversarial Attack Against Graph Neural Networks
We also evaluate the effectiveness of our attack under two defenses: one is well-designed adversarial graph detector and the other is that the target GNN model itself is equipped with a defense to prevent adversarial graph generation.
QA-Driven Zero-shot Slot Filling with Weak Supervision Pretraining
To address this problem, we introduce QA-driven slot filling (QASF), which extracts slot-filler spans from utterances with a span-based QA model.
A Unified Framework for Biphasic Facial Age Translation with Noisy-Semantic Guided Generative Adversarial Networks
Biphasic facial age translation aims at predicting the appearance of the input face at any age.
Black-box Adversarial Attacks on Commercial Speech Platforms with Minimal Information
For commercial cloud speech APIs, we propose Occam, a decision-only black-box adversarial attack, where only final decisions are available to the adversary.
IFR-Explore: Learning Inter-object Functional Relationships in 3D Indoor Scenes
While most works focus on single-object or agent-object visual functionality and affordances, our work proposes to study a new kind of visual relationship that is also important to perceive and model -- inter-object functional relationships (e. g., a switch on the wall turns on or off the light, a remote control operates the TV).
Biphasic Face Photo-Sketch Synthesis via Semantic-Driven Generative Adversarial Network with Graph Representation Learning
To preserve the generated faces being more structure-coordinated, the IRSG models inter-class structural relations among every facial component by graph representation learning.
Generative Adversarial Network
Graph Representation Learning
+1
CPTAM: Constituency Parse Tree Aggregation Method
This paper adopts the truth discovery idea to aggregate constituency parse trees from different parsers by estimating their reliability in the absence of ground truth.
MOST-Net: A Memory Oriented Style Transfer Network for Face Sketch Synthesis
Furthermore, we design a novel Memory Refinement Loss (MR Loss) for feature alignment in the memory module, which enhances the accuracy of memory slots in an unsupervised manner.
Approximate Nearest Neighbor Search under Neural Similarity Metric for Large-Scale Recommendation
In this paper, we propose a novel method to extend ANN search to arbitrary matching functions, e. g., a deep neural network.
Voice-Face Homogeneity Tells Deepfake
To this end, a voice-face matching method is devised to measure the matching degree of these two.
Towards Benchmarking and Evaluating Deepfake Detection
Deepfake detection automatically recognizes the manipulated medias through the analysis of the difference between manipulated and non-altered videos.
Quantum Deep Learning for Mutant COVID-19 Strain Prediction
New COVID-19 epidemic strains like Delta and Omicron with increased transmissibility and pathogenicity emerge and spread across the whole world rapidly while causing high mortality during the pandemic period.
AnyFace: Free-style Text-to-Face Synthesis and Manipulation
AnyFace can achieve high-quality, high-resolution, and high-diversity face synthesis and manipulation results without any constraints on the number and content of input captions.
Multiple-access relay stations for long-haul fiber-optic radio frequency transfer
We report on the realization of a long-haul radio frequency (RF) transfer scheme by using multiple-access relay stations (MARSs).
Acceleration-guided Acoustic Signal Denoising Framework Based on Learnable Wavelet Transform Applied to Slab Track Condition Monitoring
Therefore, only acoustic sensors (non-intrusive) need to be installed during the application phase, which is convenient and crucial for the condition monitoring of safety-critical infrastructure.
Towards future directions in data-integrative supervised prediction of human aging-related genes
Here, we evaluate whether analyzing a weighted dynamic aging-specific subnetwork inferred from newer GE and PPIN data improves prediction accuracy upon analyzing the best current subnetwork inferred from outdated data.
Adaptive Domain Interest Network for Multi-domain Recommendation
Industrial recommender systems usually hold data from multiple business scenarios and are expected to provide recommendation services for these scenarios simultaneously.
Improving Distantly Supervised Relation Extraction by Natural Language Inference
To reduce human annotations for relation extraction (RE) tasks, distantly supervised approaches have been proposed, while struggling with low performance.
Disentangled Graph Contrastive Learning for Review-based Recommendation
In review-based recommendation methods, review data is considered as auxiliary information that can improve the quality of learned user/item or interaction representations for the user rating prediction task.
TPGNN: Learning High-order Information in Dynamic Graphs via Temporal Propagation
We argue that when learning high-order information from temporal graphs, we encounter two challenges, i. e., computational inefficiency and over-smoothing, that cannot be solved by conventional techniques applied on static graphs.
FedDef: Defense Against Gradient Leakage in Federated Learning-based Network Intrusion Detection Systems
Deep learning (DL) methods have been widely applied to anomaly-based network intrusion detection system (NIDS) to detect malicious traffic.
Supervised Contrastive Learning with Tree-Structured Parzen Estimator Bayesian Optimization for Imbalanced Tabular Data
To solve this problem, we propose a Supervised Contrastive Learning (SCL) method with Tree-structured Parzen Estimator (TPE) technique for imbalanced tabular datasets.
GAN-based Facial Attribute Manipulation
Facial Attribute Manipulation (FAM) aims to aesthetically modify a given face image to render desired attributes, which has received significant attention due to its broad practical applications ranging from digital entertainment to biometric forensics.
Monocular BEV Perception of Road Scenes via Front-to-Top View Projection
We propose a front-to-top view projection (FTVP) module, which takes the constraint of cycle consistency between views into account and makes full use of their correlation to strengthen the view transformation and scene understanding.
Semantic-aware One-shot Face Re-enactment with Dense Correspondence Estimation
One-shot face re-enactment is a challenging task due to the identity mismatch between source and driving faces.
Reliable Image Dehazing by NeRF
In order to obtain real shot data in different scenes, we used fog generators, array cameras, mobile phones, underwater cameras and drones to obtain haze data.
Graph Mining for Cybersecurity: A Survey
In recent years, with the proliferation of graph mining techniques, many researchers investigated these techniques for capturing correlations between cyber entities and achieving high performance.
Computational Optics for Mobile Terminals in Mass Production
Correcting the optical aberrations and the manufacturing deviations of cameras is a challenging task.
Rethinking the editing of generative adversarial networks: a method to estimate editing vectors based on dimension reduction
While Generative Adversarial Networks (GANs) have recently found applications in image editing, most previous GAN-based image editing methods require largescale datasets with semantic segmentation annotations for training, only provide high level control, or merely interpolate between different images.
Emotional Talking Head Generation based on Memory-Sharing and Attention-Augmented Networks
Given an audio clip and a reference face image, the goal of the talking head generation is to generate a high-fidelity talking head video.
Relation-Aware Network with Attention-Based Loss for Few-Shot Knowledge Graph Completion
Specifically, to better utilize the plentiful negative samples and alleviate the zero-loss issue, we strategically select relevant negative samples and design an attention-based loss function to further differentiate the importance of each negative sample.
Prediction of COVID-19 Patients' Emergency Room Revisit using Multi-Source Transfer Learning
Results showed that the Multi-DANN models outperformed the Single-DANN models and baseline models in predicting revisits of COVID-19 patients to the ER within 7 days after discharge.
Smart filter aided domain adversarial neural network for fault diagnosis in noisy industrial scenarios
We validate the effectiveness of the proposed SFDANN method based on two fault diagnosis cases: one involving fault diagnosis of bearings in noisy environments and another involving fault diagnosis of slab tracks in a train-track-bridge coupling vibration system, where the transfer task involves transferring from numerical simulations to field measurements.
Exploiting Diverse Feature for Multimodal Sentiment Analysis
Specifically, we achieve the results of 0. 8492 and 0. 8439 for MuSe-Personalisation in terms of arousal and valence CCC.
Revealing the preference for correcting separated aberrations in joint optic-image design
The joint design of the optical system and the downstream algorithm is a challenging and promising task.
Memory-Constrained Semantic Segmentation for Ultra-High Resolution UAV Imagery
Within the domain of UAV imagery analysis, the segmentation of ultra-high resolution images emerges as a substantial and intricate challenge, especially when grappling with the constraints imposed by GPU memory-restricted computational devices.
Self-supervision meets kernel graph neural models: From architecture to augmentations
Recently, the idea of designing neural models on graphs using the theory of graph kernels has emerged as a more transparent as well as sometimes more expressive alternative to MPNNs known as kernel graph neural networks (KGNNs).
DetectGPT-SC: Improving Detection of Text Generated by Large Language Models through Self-Consistency with Masked Predictions
Using this observation, we subsequently proposed a new method for AI-generated texts detection based on self-consistency with masked predictions to determine whether a text is generated by LLMs.
Robust Representation Learning for Unified Online Top-K Recommendation
The robust representation learning employs domain adversarial learning and multi-view wasserstein distribution learning to learn robust representations.
An Empirical Study of Using ChatGPT for Fact Verification Task
(2) What are different prompts performance using ChatGPT for fact verification tasks?
Towards Robust and Accurate Visual Prompting
Visual prompting, an efficient method for transfer learning, has shown its potential in vision tasks.
CoRec: An Easy Approach for Coordination Recognition
In this paper, we observe and address the challenges of the coordination recognition task.
ActiveClean: Generating Line-Level Vulnerability Data via Active Learning
We demonstrated that using our cleaned data, LineVul, a SOTA line-level vulnerability detection tool, detected 70 more vulnerable lines and 18 more vulnerable functions, and improved Top 10 accuracy from 66% to 73%.
Gemini: A Family of Highly Capable Multimodal Models
This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding.
Ranked #1 on
Multi-task Language Understanding
on MMLU
(using extra training data)
Produce Once, Utilize Twice for Anomaly Detection
Equipped with the above modules, POUTA is endowed with the ability to provide a more precise anomaly location than the prior arts.
Ranked #16 on
Anomaly Detection
on MVTec AD
MIVC: Multiple Instance Visual Component for Visual-Language Models
Vision-language models have been widely explored across a wide range of tasks and achieve satisfactory performance.
Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems
Based on this, we propose a comprehensive taxonomy, which systematically analyzes potential risks associated with each module of an LLM system and discusses the corresponding mitigation strategies.
Re-Examine Distantly Supervised NER: A New Benchmark and a Simple Approach
This paper delves into Named Entity Recognition (NER) under the framework of Distant Supervision (DS-NER), where the main challenge lies in the compromised quality of labels due to inherent errors such as false positives, false negatives, and positive type errors.
Defending Against Data Reconstruction Attacks in Federated Learning: An Information Theory Approach
According to the channel model, we propose algorithms to constrain the information transmitted in a single round of local training.
AceMap: Knowledge Discovery through Academic Graph
The exponential growth of scientific literature requires effective management and extraction of valuable insights.
Deep Contrastive Multi-view Clustering under Semantic Feature Guidance
To mitigate the interference of view-private information, specific view and fusion view semantic features are learned by cluster-level contrastive learning and concatenated to measure the semantic similarity of instances.
Pencil: Private and Extensible Collaborative Learning without the Non-Colluding Assumption
In this paper, we present Pencil, the first private training framework for collaborative learning that simultaneously offers data privacy, model privacy, and extensibility to multiple data providers, without relying on the non-colluding assumption.
VideoBadminton: A Video Dataset for Badminton Action Recognition
In this paper, we introduce the VideoBadminton dataset derived from high-quality badminton footage.
Has Approximate Machine Unlearning been evaluated properly? From Auditing to Side Effects
The growing concerns surrounding data privacy and security have underscored the critical necessity for machine unlearning--aimed at fully removing data lineage from machine learning models.
A Distributionally Robust Model Predictive Control for Static and Dynamic Uncertainties in Smart Grids
The distinctive contribution of this paper lies in its holistic approach to both static and dynamic uncertainties in smart grids.
Empirical Analysis for Unsupervised Universal Dependency Parse Tree Aggregation
Dependency parsing is an essential task in NLP, and the quality of dependency parsers is crucial for many downstream tasks.
A parameter-free clustering algorithm for missing datasets
Missing datasets, in which some objects have missing values in certain dimensions, are prevalent in the Real-world.
