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Found 34 papers, 12 papers with code
PGL at TextGraphs 2020 Shared Task: Explanation Regeneration using Language and Graph Learning Methods
To address this problem, we use a pre-trained language model to recall the top-K relevant explanations for each question.
KPL: Training-Free Medical Knowledge Mining of Vision-Language Models
To tackle these challenges, we introduce the Knowledge Proxy Learning (KPL) to mine knowledge from CLIP.
MedCoT: Medical Chain of Thought via Hierarchical Expert
Artificial intelligence has advanced in Medical Visual Question Answering (Med-VQA), but prevalent research tends to focus on the accuracy of the answers, often overlooking the reasoning paths and interpretability, which are crucial in clinical settings.
PVP: Polar Representation Boost for 3D Semantic Occupancy Prediction
Recently, polar coordinate-based representations have shown promise for 3D perceptual tasks.
Reasons and Solutions for the Decline in Model Performance after Editing
In order to investigate the reasons for the performance decline of the edited model and optimize the editing method, this work explores the underlying reasons from both data and model perspectives.
R-LLaVA: Improving Med-VQA Understanding through Visual Region of Interest
Artificial intelligence has made significant strides in medical visual question answering (Med-VQA), yet prevalent studies often interpret images holistically, overlooking the visual regions of interest that may contain crucial information, potentially aligning with a doctor's prior knowledge that can be incorporated with minimal annotations (e. g., bounding boxes).
SGW-based Multi-Task Learning in Vision Tasks
Multi-task-learning(MTL) is a multi-target optimization task.
MedThink: Explaining Medical Visual Question Answering via Multimodal Decision-Making Rationale
Medical Visual Question Answering (MedVQA), which offers language responses to image-based medical inquiries, represents a challenging task and significant advancement in healthcare.
Enhancing Large Language Models with Pseudo- and Multisource- Knowledge Graphs for Open-ended Question Answering
In summary, our results pave the way for enhancing LLMs by incorporating Pseudo- and Multisource-KGs, particularly in the context of open-ended questions.
Named Entity Driven Zero-Shot Image Manipulation
We introduced StyleEntity a zero-shot image manipulation model that utilizes named entities as proxies during its training phase.
Scalable Geometric Fracture Assembly via Co-creation Space among Assemblers
Therefore, there is a need to develop a scalable framework for geometric fracture assembly without relying on semantic information.
A ChatGPT Aided Explainable Framework for Zero-Shot Medical Image Diagnosis
Recent advances in pretrained vision-language models (VLMs) such as CLIP have shown great performance for zero-shot natural image recognition and exhibit benefits in medical applications.
ToothSegNet: Image Degradation meets Tooth Segmentation in CBCT Images
In computer-assisted orthodontics, three-dimensional tooth models are required for many medical treatments.
JoinBoost: Grow Trees Over Normalized Data Using Only SQL
Although dominant for tabular data, ML libraries that train tree models over normalized databases (e. g., LightGBM, XGBoost) require the data to be denormalized as a single table, materialized, and exported.
Efficient Text-Guided 3D-Aware Portrait Generation with Score Distillation Sampling on Distribution
These elaborated designs enable our model to generate portraits with robust multi-view semantic consistency, eliminating the need for optimization-based methods.
ERNIE 3.0 Tiny: Frustratingly Simple Method to Improve Task-Agnostic Distillation Generalization
Experimental results demonstrate that our method yields a student with much better generalization, significantly outperforms existing baselines, and establishes a new state-of-the-art result on in-domain, out-domain, and low-resource datasets in the setting of task-agnostic distillation.
ERNIE-ViLG 2.0: Improving Text-to-Image Diffusion Model with Knowledge-Enhanced Mixture-of-Denoising-Experts
Recent progress in diffusion models has revolutionized the popular technology of text-to-image generation.
Ranked #12 on
Text-to-Image Generation
on MS COCO
DuETA: Traffic Congestion Propagation Pattern Modeling via Efficient Graph Learning for ETA Prediction at Baidu Maps
In this work, we focus on modeling traffic congestion propagation patterns to improve ETA performance.
Abstraction and Refinement: Towards Scalable and Exact Verification of Neural Networks
Our approach is orthogonal to and can be integrated with many existing verification techniques.
ERNIE-Search: Bridging Cross-Encoder with Dual-Encoder via Self On-the-fly Distillation for Dense Passage Retrieval
Our method 1) introduces a self on-the-fly distillation method that can effectively distill late interaction (i. e., ColBERT) to vanilla dual-encoder, and 2) incorporates a cascade distillation process to further improve the performance with a cross-encoder teacher.
ERNIE-SPARSE: Learning Hierarchical Efficient Transformer Through Regularized Self-Attention
We argue that two factors, information bottleneck sensitivity and inconsistency between different attention topologies, could affect the performance of the Sparse Transformer.
AI-enabled Automatic Multimodal Fusion of Cone-Beam CT and Intraoral Scans for Intelligent 3D Tooth-Bone Reconstruction and Clinical Applications
Here, we present a Deep Dental Multimodal Analysis (DDMA) framework consisting of a CBCT segmentation model, an intraoral scan (IOS) segmentation model (the most accurate digital dental model), and a fusion model to generate 3D fused crown-root-bone structures with high fidelity and accurate occlusal and dentition information.
ERNIE 3.0 Titan: Exploring Larger-scale Knowledge Enhanced Pre-training for Language Understanding and Generation
A unified framework named ERNIE 3. 0 was recently proposed for pre-training large-scale knowledge enhanced models and trained a model with 10 billion parameters.
ERNIE-SPARSE: Robust Efficient Transformer Through Hierarchically Unifying Isolated Information
The first factor is information bottleneck sensitivity, which is caused by the key feature of Sparse Transformer — only a small number of global tokens can attend to all other tokens.
Alpha at SemEval-2021 Task 6: Transformer Based Propaganda Classification
This paper describes our system participated in Task 6 of SemEval-2021: the task focuses on multimodal propaganda technique classification and it aims to classify given image and text into 22 classes.
abcbpc at SemEval-2021 Task 7: ERNIE-based Multi-task Model for Detecting and Rating Humor and Offense
This paper describes our system participated in Task 7 of SemEval-2021: Detecting and Rating Humor and Offense.
ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation
We trained the model with 10 billion parameters on a 4TB corpus consisting of plain texts and a large-scale knowledge graph.
Pre-trained Language Model for Web-scale Retrieval in Baidu Search
More importantly, we present a practical system workflow for deploying the model in web-scale retrieval.
ERNIE-Tiny : A Progressive Distillation Framework for Pretrained Transformer Compression
Specifically, the first stage, General Distillation, performs distillation with guidance from pretrained teacher, gerenal data and latent distillation loss.
Pose Discrepancy Spatial Transformer Based Feature Disentangling for Partial Aspect Angles SAR Target Recognition
This letter presents a novel framework termed DistSTN for the task of synthetic aperture radar (SAR) automatic target recognition (ATR).
Galileo at SemEval-2020 Task 12: Multi-lingual Learning for Offensive Language Identification using Pre-trained Language Models
In Sub-task A - Offensive Language Identification, we ranked first in terms of average F1 scores in all languages.
ERNIE at SemEval-2020 Task 10: Learning Word Emphasis Selection by Pre-trained Language Model
This paper describes the system designed by ERNIE Team which achieved the first place in SemEval-2020 Task 10: Emphasis Selection For Written Text in Visual Media.
kk2018 at SemEval-2020 Task 9: Adversarial Training for Code-Mixing Sentiment Classification
Code switching is a linguistic phenomenon that may occur within a multilingual setting where speakers share more than one language.
OleNet at SemEval-2019 Task 9: BERT based Multi-Perspective Models for Suggestion Mining
This paper describes our system partici- pated in Task 9 of SemEval-2019: the task is focused on suggestion mining and it aims to classify given sentences into sug- gestion and non-suggestion classes in do- main specific and cross domain training setting respectively.
