Search Results for author:
Found 49 papers, 20 papers with code
Unsupervised Adverbial Identification in Modern Chinese Literature
In Chinese, the derivation may be marked either with the standard adverbial marker DI, or the non-standard marker DE.
Weakly Supervised Semantic Segmentation with Boundary Exploration
Weakly supervised semantic segmentation with image-level labels has attracted a lot of attention recently because these labels are already available in most datasets.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
Leveraging Bidding Graphs for Advertiser-Aware Relevance Modeling in Sponsored Search
As the fundamental basis of sponsored search, relevance modeling has attracted increasing attention due to the tremendous practical value.
Towards Real-Time Neural Video Codec for Cross-Platform Application Using Calibration Information
First, to solve the problem of inconsistency of codec caused by the uncertainty of floating point calculations across platforms, we design a calibration transmitting system to guarantee the consistent quantization of entropy parameters between the encoding and decoding stages.
VIGC: Visual Instruction Generation and Correction
A practical solution to this problem would be to utilize the available multimodal large language models (MLLMs) to generate instruction data for vision-language tasks.
Dynamic Low-Rank Instance Adaptation for Universal Neural Image Compression
We thus introduce a dynamic gating network on top of the low-rank adaptation method, in order to decide which decoder layer should employ adaptation.
IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models
Despite the simplicity of our method, an IP-Adapter with only 22M parameters can achieve comparable or even better performance to a fully fine-tuned image prompt model.
RLTF: Reinforcement Learning from Unit Test Feedback
To address these issues, we proposed RLTF, i. e., Reinforcement Learning from Unit Test Feedback, a novel online RL framework with unit test feedback of multi-granularity for refining code LLMs.
HeadSculpt: Crafting 3D Head Avatars with Text
Specifically, we first equip the diffusion model with 3D awareness by leveraging landmark-based control and a learned textual embedding representing the back view appearance of heads, enabling 3D-consistent head avatar generations.
Achieving Counterfactual Fairness for Anomaly Detection
Ensuring fairness in anomaly detection models has received much attention recently as many anomaly detection applications involve human beings.
FAME-ViL: Multi-Tasking Vision-Language Model for Heterogeneous Fashion Tasks
In the fashion domain, there exists a variety of vision-and-language (V+L) tasks, including cross-modal retrieval, text-guided image retrieval, multi-modal classification, and image captioning.
Slate-Aware Ranking for Recommendation
Therefore, the ranking stage is still essential for most applications to provide high-quality candidate set for the re-ranking stage.
Federated contrastive learning models for prostate cancer diagnosis and Gleason grading
Finally, FCL brings a robust, accurate, low-cost AI training model to biomedical research, effectively protecting medical data privacy.
Cross-center Early Sepsis Recognition by Medical Knowledge Guided Collaborative Learning for Data-scarce Hospitals
In this paper, for a common disease in ICU patients, sepsis, we propose a novel cross-center collaborative learning framework guided by medical knowledge, SofaNet, to achieve early recognition of this disease.
Vertical Federated Knowledge Transfer via Representation Distillation for Healthcare Collaboration Networks
In order to improve the information-sharing capability and innovation of various healthcare-related institutions, and then to establish a next-generation open medical collaboration network, we propose a unified framework for vertical federated knowledge transfer mechanism (VFedTrans) based on a novel cross-hospital representation distillation component.
Artificial intelligence for diagnosing and predicting survival of patients with renal cell carcinoma: Retrospective multi-center study
The OSrisk for the prediction of 5-year survival status achieved AUC of 0. 784 (0. 746-0. 819) in the TCGA cohort, which was further verified in the independent General cohort and the CPTAC cohort, with AUC of 0. 774 (0. 723-0. 820) and 0. 702 (0. 632-0. 765), respectively.
How To Prevent the Poor Performance Clients for Personalized Federated Learning?
Intuitively, these poor clients may come from biased universal information shared with others.
A Unified Knowledge Graph Augmentation Service for Boosting Domain-specific NLP Tasks
We propose KnowledgeDA, a unified domain language model development service to enhance the task-specific training procedure with domain knowledge graphs.
On Root Cause Localization and Anomaly Mitigation through Causal Inference
After that, we further propose an anomaly mitigation approach that aims to recommend mitigation actions on abnormal features to revert the abnormal outcomes such that the counterfactuals guided by the causal mechanism are normal.
RLogist: Fast Observation Strategy on Whole-slide Images with Deep Reinforcement Learning
Most of the existing methods rely on a multiple instance learning framework that requires densely sampling local patches at high magnification.
Resolution enhancement of NMR by decoupling with low-rank Hankel model
Nuclear magnetic resonance (NMR) spectroscopy has become a formidable tool for biochemistry and medicine.
Adap DP-FL: Differentially Private Federated Learning with Adaptive Noise
The heterogeneity and convergence of training parameters were simply not considered.
LiCamGait: Gait Recognition in the Wild by Using LiDAR and Camera Multi-modal Visual Sensors
LiDAR can capture accurate depth information in large-scale scenarios without the effect of light conditions, and the captured point cloud contains gait-related 3D geometric properties and dynamic motion characteristics.
RAPO: An Adaptive Ranking Paradigm for Bilingual Lexicon Induction
Bilingual lexicon induction induces the word translations by aligning independently trained word embeddings in two languages.
DeepNoise: Signal and Noise Disentanglement based on Classifying Fluorescent Microscopy Images via Deep Learning
The high-content image-based assay is commonly leveraged for identifying the phenotypic impact of genetic perturbations in biology field.
Safety and Performance, Why not Both? Bi-Objective Optimized Model Compression toward AI Software Deployment
By simulating the attack mechanism as the safety test, SafeCompress can automatically compress a big model to a small one following the dynamic sparse training paradigm.
NMR spectrum reconstruction as a pattern recognition problem
A new deep neural network based on the WaveNet architecture (WNN) is presented, which is designed to grasp specific patterns in the NMR spectra.
Large-Scale Product Retrieval with Weakly Supervised Representation Learning
Large-scale weakly supervised product retrieval is a practically useful yet computationally challenging problem.
FashionViL: Fashion-Focused Vision-and-Language Representation Learning
We thus propose a Multi-View Contrastive Learning task for pulling closer the visual representation of one image to the compositional multimodal representation of another image+text.
Large-Scale Privacy-Preserving Network Embedding against Private Link Inference Attacks
Basically, we propose to perturb the original network by adding or removing links, and expect the embedding generated on the perturbed network can leak little information about private links but hold high utility for various downstream tasks.
Deep learning-based approach to reveal tumor mutational burden status from whole slide images across multiple cancer types
MC-TMB algorithm also exhibited good generalization on the external validation cohort with an AUC of 0. 732 (0. 683-0. 761), and better performance when compared to other methods.
UIGR: Unified Interactive Garment Retrieval
In this paper, we propose a Unified Interactive Garment Retrieval (UIGR) framework to unify TGR and VCR.
Node-Aligned Graph Convolutional Network for Whole-Slide Image Representation and Classification
In this paper, we propose a hierarchical global-to-local clustering strategy to build a Node-Aligned GCN (NAGCN) to represent WSI with rich local structural information as well as global distribution.
Data Valuation for Vertical Federated Learning: An Information-Theoretic Approach
Federated learning (FL) is a promising machine learning paradigm that enables cross-party data collaboration for real-world AI applications in a privacy-preserving and law-regulated way.
HyObscure: Hybrid Obscuring for Privacy-Preserving Data Publishing
We then design a novel hybrid protection mechanism called HyObscure, to cross-iteratively optimize the generalization and obfuscation operations for maximum privacy protection under a certain utility guarantee.
Label Hierarchy Transition: Modeling Class Hierarchies to Enhance Deep Classifiers
In this paper, we propose Label Hierarchy Transition, a unified probabilistic framework based on deep learning, to address hierarchical classification.
CoDiM: Learning with Noisy Labels via Contrastive Semi-Supervised Learning
Labels are costly and sometimes unreliable.
Ranked #5 on
Image Classification
on mini WebVision 1.0
Text-Based Person Search with Limited Data
Firstly, to fully utilize the existing small-scale benchmarking datasets for more discriminative feature learning, we introduce a cross-modal momentum contrastive learning framework to enrich the training data for a given mini-batch.
Ranked #7 on
Text based Person Retrieval
on CUHK-PEDES
(using extra training data)
Label Confusion Learning to Enhance Text Classification Models
LCM can learn label confusion to capture semantic overlap among labels by calculating the similarity between instances and labels during training and generate a better label distribution to replace the original one-hot label vector, thus improving the final classification performance.
Federated Crowdsensing: Framework and Challenges
In particular, we first propose a federated crowdsensing framework, which analyzes the privacy concerns of each crowdsensing stage (i. e., task creation, task assignment, task execution, and data aggregation) and discuss how federated learning techniques may take effect.
Microscope Based HER2 Scoring System
Recently, artificial intelligence (AI) has been used in various disease diagnosis to improve diagnostic accuracy and reliability, but the interpretation of diagnosis results is still an open problem.
IEEE 802.11be-Wi-Fi 7: New Challenges and Opportunities
Due to the related stringent requirements, supporting these applications over wireless local area network (WLAN) is far beyond the capabilities of the new WLAN standard -- IEEE 802. 11ax.
Covidex: Neural Ranking Models and Keyword Search Infrastructure for the COVID-19 Open Research Dataset
We present Covidex, a search engine that exploits the latest neural ranking models to provide information access to the COVID-19 Open Research Dataset curated by the Allen Institute for AI.
Semi-Supervised Deep Learning Using Improved Unsupervised Discriminant Projection
Deep learning demands a huge amount of well-labeled data to train the network parameters.
CreditPrint: Credit Investigation via Geographic Footprints by Deep Learning
Credit investigation is critical for financial services.
SIMPLE: Statistical Inference on Membership Profiles in Large Networks
Both tests are of the Hotelling-type statistics based on the rows of empirical eigenvectors or their ratios, whose asymptotic covariance matrices are very challenging to derive and estimate.
Multi-Perspective Neural Architecture for Recommendation System
In one stage, the user and item are represented from multiple perspectives and in each perspective, the representations of user and item put attentions to each other.
Automatic Liver Lesion Segmentation Using A Deep Convolutional Neural Network Method
Liver lesion segmentation is an important step for liver cancer diagnosis, treatment planning and treatment evaluation.
MR-based synthetic CT generation using a deep convolutional neural network method
Applying a trained model to generate a complete sCT volume for each new patient MR image only took 9 s, which was much faster than the atlas‐based approach.
