Search Results for author:
Found 33 papers, 9 papers with code
FHDe²Net: Full High Definition Demoireing Network
Frequency aliasing in the digital capture of display screens leads to the moir´e pattern, appearing as stripe-shaped distortions in images.
Identity-aware Dual-constraint Network for Cloth-Changing Person Re-identification
In addition, a Multi-scale Constraint Block (MCB) is designed, which extracts fine-grained identity-related features and effectively transfers cloth-irrelevant knowledge.
AnimatableDreamer: Text-Guided Non-rigid 3D Model Generation and Reconstruction with Canonical Score Distillation
Extensive experiments demonstrate the capability of our method in generating high-flexibility text-guided 3D models from the monocular video, while also showing improved reconstruction performance over existing non-rigid reconstruction methods.
Synthetic IMU Datasets and Protocols Can Simplify Fall Detection Experiments and Optimize Sensor Configuration
The proposed method simplifies fall detection data acquisition experiments, provides novel venue for generating low cost synthetic data in scenario where acquiring data for machine learning is challenging and paves the way for customizing machine learning configurations.
Implementation of The Future of Drug Discovery: QuantumBased Machine Learning Simulation (QMLS)
This paper is based on our first paper, where we pitched the concept of machine learning combined with quantum simulations.
Goal-Conditioned Reinforcement Learning with Disentanglement-based Reachability Planning
In the paper, we propose a goal-conditioned RL algorithm combined with Disentanglement-based Reachability Planning (REPlan) to solve temporally extended tasks.
Communication and Control in Collaborative UAVs: Recent Advances and Future Trends
The recent progress in unmanned aerial vehicles (UAV) technology has significantly advanced UAV-based applications for military, civil, and commercial domains.
s-Adaptive Decoupled Prototype for Few-Shot Object Detection
To provide precise information for the query image, the prototype is decoupled into task-specific ones, which provide tailored guidance for 'where to look' and 'what to look for', respectively.
Hybrid stability augmentation control of multi-rotor UAV in confined space based on adaptive backstepping control
The inspection of a diversion line in an enclosed space requires high system stability and robustness of the UAV controller.
Exploring Effective Information Utilization in Multi-Turn Topic-Driven Conversations
In order to expand the information that PLMs can utilize, we encode topic and dialogue history information using certain prompts with multiple channels of Fusion-in-Decoder (FiD) and explore the influence of three different channel settings.
Zero-shot object goal visual navigation
Object goal visual navigation is a challenging task that aims to guide a robot to find the target object based on its visual observation, and the target is limited to the classes pre-defined in the training stage.
Knowledge-enriched Attention Network with Group-wise Semantic for Visual Storytelling
Third, a unified one-stage story generation model with encoder-decoder structure is proposed to simultaneously train and infer the knowledge-enriched attention network, group-wise semantic module and multi-modal story generation decoder in an end-to-end fashion.
Weakly Supervised Disentangled Representation for Goal-conditioned Reinforcement Learning
In the paper, we propose a skill learning framework DR-GRL that aims to improve the sample efficiency and policy generalization by combining the Disentangled Representation learning and Goal-conditioned visual Reinforcement Learning.
UniDS: A Unified Dialogue System for Chit-Chat and Task-oriented Dialogues
With the advances in deep learning, tremendous progress has been made with chit-chat dialogue systems and task-oriented dialogue systems.
Benchmarking Commonsense Knowledge Base Population with an Effective Evaluation Dataset
Experimental results show that generalizing commonsense reasoning on unseen assertions is inherently a hard task.
Neural Network Surgery: Injecting Data Patterns into Pre-trained Models with Minimal Instance-wise Side Effects
Motivated by neuroscientific evidence and theoretical results, we demonstrate that side effects can be controlled by the number of changed parameters and thus, we propose to conduct \textit{neural network surgery} by only modifying a limited number of parameters.
A Global Past-Future Early Exit Method for Accelerating Inference of Pre-trained Language Models
We first take into consideration all the linguistic information embedded in the past layers and then take a further step to engage the future information which is originally inaccessible for predictions.
Be Careful about Poisoned Word Embeddings: Exploring the Vulnerability of the Embedding Layers in NLP Models
However, in this paper, we find that it is possible to hack the model in a data-free way by modifying one single word embedding vector, with almost no accuracy sacrificed on clean samples.
DISCOS: Bridging the Gap between Discourse Knowledge and Commonsense Knowledge
On the other hand, generation models have the potential to automatically generate more knowledge.
High-Likelihood Area Matters --- Rewarding Near-Correct Predictions Under Imbalanced Distributions
We find in practice that the high-likelihood area contains correct predictions for tail classes and it plays a vital role in learning imbalanced class distributions.
KgPLM: Knowledge-guided Language Model Pre-training via Generative and Discriminative Learning
Recent studies on pre-trained language models have demonstrated their ability to capture factual knowledge and applications in knowledge-aware downstream tasks.
PPKE: Knowledge Representation Learning by Path-based Pre-training
Entities may have complex interactions in a knowledge graph (KG), such as multi-step relationships, which can be viewed as graph contextual information of the entities.
Pretrain-KGE: Learning Knowledge Representation from Pretrained Language Models
Conventional knowledge graph embedding (KGE) often suffers from limited knowledge representation, leading to performance degradation especially on the low-resource problem.
BERT-MK: Integrating Graph Contextualized Knowledge into Pre-trained Language Models
Complex node interactions are common in knowledge graphs (KGs), and these interactions can be considered as contextualized knowledge exists in the topological structure of KGs.
KQA Pro: A Dataset with Explicit Compositional Programs for Complex Question Answering over Knowledge Base
To this end, we introduce KQA Pro, a dataset for Complex KBQA including ~120K diverse natural language questions.
Pretrain-KGEs: Learning Knowledge Representation from Pretrained Models for Knowledge Graph Embeddings
Learning knowledge graph embeddings (KGEs) is an efficient approach to knowledge graph completion.
Integrating Graph Contextualized Knowledge into Pre-trained Language Models
Complex node interactions are common in knowledge graphs, and these interactions also contain rich knowledge information.
Mop Moire Patterns Using MopNet
The complex frequency distribution, imbalanced magnitude in colour channels, and diverse appearance attributes of moire pattern make its removal a challenging problem.
Ranked #5 on
Image Enhancement
on TIP 2018
Convolutional Gated Recurrent Units for Medical Relation Classification
Convolutional neural network (CNN) and recurrent neural network (RNN) models have become the mainstream methods for relation classification.
Classifying medical relations in clinical text via convolutional neural networks
Deep learning research on relation classification has achieved solid performance in the general domain.
De-identification of medical records using conditional random fields and long short-term memory networks
The CEGS N-GRID 2016 Shared Task 1 in Clinical Natural Language Processing focuses on the de-identification of psychiatric evaluation records.
Developing a cardiovascular disease risk factor annotated corpus of Chinese electronic medical records
To the best of our knowledge, this is the first annotated corpus concerning CVD risk factors in CEMRs and the guidelines for capturing CVD risk factor annotations from CEMRs were proposed.
Building a comprehensive syntactic and semantic corpus of Chinese clinical texts
Objective: To build a comprehensive corpus covering syntactic and semantic annotations of Chinese clinical texts with corresponding annotation guidelines and methods as well as to develop tools trained on the annotated corpus, which supplies baselines for research on Chinese texts in the clinical domain.
