Search Results for author: Fenglin Liu

Found 41 papers, 8 papers with code

Large Language Models in Mental Health Care: a Scoping Review

no code implementations1 Jan 2024 Yining Hua, Fenglin Liu, Kailai Yang, Zehan Li, Yi-han Sheu, Peilin Zhou, Lauren V. Moran, Sophia Ananiadou, Andrew Beam

Objective: The growing use of large language models (LLMs) stimulates a need for a comprehensive review of their applications and outcomes in mental health care contexts.

A Survey of Large Language Models in Medicine: Progress, Application, and Challenge

1 code implementation9 Nov 2023 Hongjian Zhou, Fenglin Liu, Boyang Gu, Xinyu Zou, Jinfa Huang, Jinge Wu, Yiru Li, Sam S. Chen, Peilin Zhou, Junling Liu, Yining Hua, Chengfeng Mao, Chenyu You, Xian Wu, Yefeng Zheng, Lei Clifton, Zheng Li, Jiebo Luo, David A. Clifton

Therefore, this review aims to provide a detailed overview of the development and deployment of LLMs in medicine, including the challenges and opportunities they face.

OSNet & MNetO: Two Types of General Reconstruction Architectures for Linear Computed Tomography in Multi-Scenarios

no code implementations21 Sep 2023 Zhisheng Wang, Zihan Deng, Fenglin Liu, Yixing Huang, Haijun Yu, Junning Cui

The second uses multiple networks to train different directional Hilbert filtering models for DBP images of multiple linear scannings, respectively, and then overlays the reconstructed results, i. e., Multiple Networks Overlaying (MNetO).

MultiCapCLIP: Auto-Encoding Prompts for Zero-Shot Multilingual Visual Captioning

1 code implementation25 Aug 2023 Bang Yang, Fenglin Liu, Xian Wu, YaoWei Wang, Xu sun, Yuexian Zou

To deal with the label shortage problem, we present a simple yet effective zero-shot approach MultiCapCLIP that can generate visual captions for different scenarios and languages without any labeled vision-caption pairs of downstream datasets.

Image Captioning Video Captioning

Multimodal Prompt Learning for Product Title Generation with Extremely Limited Labels

no code implementations5 Jul 2023 Bang Yang, Fenglin Liu, Zheng Li, Qingyu Yin, Chenyu You, Bing Yin, Yuexian Zou

We observe that the core challenges of novel product title generation are the understanding of novel product characteristics and the generation of titles in a novel writing style.

Image Captioning Text Generation

BPF Algorithms for Multiple Source-Translation Computed Tomography Reconstruction

no code implementations30 May 2023 Zhisheng Wang, Haijun Yu, Yixing Huang, Shunli Wang, Song Ni, Zongfeng Li, Fenglin Liu, Junning Cui

Micro-computed tomography (micro-CT) is a widely used state-of-the-art instrument employed to study the morphological structures of objects in various fields.

Translation

ZeroNLG: Aligning and Autoencoding Domains for Zero-Shot Multimodal and Multilingual Natural Language Generation

1 code implementation11 Mar 2023 Bang Yang, Fenglin Liu, Yuexian Zou, Xian Wu, YaoWei Wang, David A. Clifton

We present the results of extensive experiments on twelve NLG tasks, showing that, without using any labeled downstream pairs for training, ZeroNLG generates high-quality and believable outputs and significantly outperforms existing zero-shot methods.

Image Captioning Machine Translation +5

Aligning Source Visual and Target Language Domains for Unpaired Video Captioning

no code implementations22 Nov 2022 Fenglin Liu, Xian Wu, Chenyu You, Shen Ge, Yuexian Zou, Xu sun

To this end, we introduce the unpaired video captioning task aiming to train models without coupled video-caption pairs in target language.

Translation Video Captioning

Expectation-Maximization Contrastive Learning for Compact Video-and-Language Representations

4 code implementations21 Nov 2022 Peng Jin, Jinfa Huang, Fenglin Liu, Xian Wu, Shen Ge, Guoli Song, David A. Clifton, Jie Chen

Most video-and-language representation learning approaches employ contrastive learning, e. g., CLIP, to project the video and text features into a common latent space according to the semantic similarities of text-video pairs.

Ranked #2 on Video Retrieval on LSMDC (text-to-video Mean Rank metric)

Contrastive Learning Representation Learning +5

DiMBERT: Learning Vision-Language Grounded Representations with Disentangled Multimodal-Attention

no code implementations28 Oct 2022 Fenglin Liu, Xian Wu, Shen Ge, Xuancheng Ren, Wei Fan, Xu sun, Yuexian Zou

To enhance the correlation between vision and language in disentangled spaces, we introduce the visual concepts to DiMBERT which represent visual information in textual format.

Image Captioning Language Modelling +3

Generating Accurate and Faithful Discharge Instructions: Task, Dataset, and Model

2 code implementations23 Oct 2022 Fenglin Liu, Bang Yang, Chenyu You, Xian Wu, Shen Ge, Zhangdaihong Liu, Xu sun, Yang Yang, David A. Clifton

We build a benchmark clinical dataset and propose the Re3Writer, which imitates the working patterns of physicians to first retrieve related working experience from historical PIs written by physicians, then reason related medical knowledge.

Prophet Attention: Predicting Attention with Future Attention for Image Captioning

no code implementations19 Oct 2022 Fenglin Liu, Xuancheng Ren, Xian Wu, Wei Fan, Yuexian Zou, Xu sun

Especially for image captioning, the attention based models are expected to ground correct image regions with proper generated words.

Image Captioning

Mine yOur owN Anatomy: Revisiting Medical Image Segmentation with Extremely Limited Labels

no code implementations27 Sep 2022 Chenyu You, Weicheng Dai, Fenglin Liu, Yifei Min, Haoran Su, Xiaoran Zhang, Xiaoxiao Li, David A. Clifton, Lawrence Staib, James S. Duncan

Blindly leveraging all pixels in training hence can lead to the data imbalance issues, and cause deteriorated performance; (2) consistency: it remains unclear whether a segmentation model has learned meaningful and yet consistent anatomical features due to the intra-class variations between different anatomical features; and (3) diversity: the intra-slice correlations within the entire dataset have received significantly less attention.

Anatomy Contrastive Learning +4

Competence-based Multimodal Curriculum Learning for Medical Report Generation

no code implementations ACL 2021 Fenglin Liu, Shen Ge, Yuexian Zou, Xian Wu

Medical report generation task, which targets to produce long and coherent descriptions of medical images, has attracted growing research interests recently.

Image Captioning Medical Report Generation

Graph-in-Graph Network for Automatic Gene Ontology Description Generation

no code implementations10 Jun 2022 Fenglin Liu, Bang Yang, Chenyu You, Xian Wu, Shen Ge, Adelaide Woicik, Sheng Wang

This task aims to automatically generate a sentence that describes the function of a GO term belonging to one of the three categories, i. e., molecular function, biological process, and cellular component.

Sentence

End-to-end Spoken Conversational Question Answering: Task, Dataset and Model

no code implementations Findings (NAACL) 2022 Chenyu You, Nuo Chen, Fenglin Liu, Shen Ge, Xian Wu, Yuexian Zou

To evaluate the capacity of SCQA systems in a dialogue-style interaction, we assemble a Spoken Conversational Question Answering (Spoken-CoQA) dataset with more than 40k question-answer pairs from 4k conversations.

Conversational Question Answering Spoken Language Understanding +1

AlignTransformer: Hierarchical Alignment of Visual Regions and Disease Tags for Medical Report Generation

no code implementations18 Mar 2022 Di You, Fenglin Liu, Shen Ge, Xiaoxia Xie, Jing Zhang, Xian Wu

The acquired disease-grounded visual features can better represent the abnormal regions of the input image, which could alleviate data bias problem; 2) MGT module effectively uses the multi-grained features and Transformer framework to generate the long medical report.

Descriptive Image Captioning +1

Audio-Oriented Multimodal Machine Comprehension: Task, Dataset and Model

no code implementations4 Jul 2021 Zhiqi Huang, Fenglin Liu, Xian Wu, Shen Ge, Helin Wang, Wei Fan, Yuexian Zou

As a result, the proposed approach can handle various tasks including: Audio-Oriented Multimodal Machine Comprehension, Machine Reading Comprehension and Machine Listening Comprehension, in a single model, making fair comparisons possible between our model and the existing unimodal MC models.

Knowledge Distillation Machine Reading Comprehension

Exploring Semantic Relationships for Unpaired Image Captioning

no code implementations20 Jun 2021 Fenglin Liu, Meng Gao, Tianhao Zhang, Yuexian Zou

To further improve the quality of captions generated by the model, we propose the Semantic Relationship Explorer, which explores the relationships between semantic concepts for better understanding of the image.

Image Captioning Sentence

Exploring and Distilling Posterior and Prior Knowledge for Radiology Report Generation

no code implementations CVPR 2021 Fenglin Liu, Xian Wu, Shen Ge, Wei Fan, Yuexian Zou

In detail, PoKE explores the posterior knowledge, which provides explicit abnormal visual regions to alleviate visual data bias; PrKE explores the prior knowledge from the prior medical knowledge graph (medical knowledge) and prior radiology reports (working experience) to alleviate textual data bias.

Contrastive Attention for Automatic Chest X-ray Report Generation

no code implementations Findings (ACL) 2021 Fenglin Liu, Changchang Yin, Xian Wu, Shen Ge, Ping Zhang, Yuexian Zou, Xu sun

In addition, according to the analysis, the CA model can help existing models better attend to the abnormal regions and provide more accurate descriptions which are crucial for an interpretable diagnosis.

Rethinking Skip Connection with Layer Normalization in Transformers and ResNets

no code implementations15 May 2021 Fenglin Liu, Xuancheng Ren, Zhiyuan Zhang, Xu sun, Yuexian Zou

In this work, we investigate how the scale factors in the effectiveness of the skip connection and reveal that a trivial adjustment of the scale will lead to spurious gradient exploding or vanishing in line with the deepness of the models, which could be addressed by normalization, in particular, layer normalization, which induces consistent improvements over the plain skip connection.

Image Classification Machine Translation +1

Adaptive Bi-directional Attention: Exploring Multi-Granularity Representations for Machine Reading Comprehension

no code implementations20 Dec 2020 Nuo Chen, Fenglin Liu, Chenyu You, Peilin Zhou, Yuexian Zou

To predict the answer, it is common practice to employ a predictor to draw information only from the final encoder layer which generates the \textit{coarse-grained} representations of the source sequences, i. e., passage and question.

Machine Reading Comprehension

Prophet Attention: Predicting Attention with Future Attention

no code implementations NeurIPS 2020 Fenglin Liu, Xuancheng Ren, Xian Wu, Shen Ge, Wei Fan, Yuexian Zou, Xu sun

Especially for image captioning, the attention based models are expected to ground correct image regions with proper generated words.

Image Captioning

Rethinking Skip Connection with Layer Normalization

no code implementations COLING 2020 Fenglin Liu, Xuancheng Ren, Zhiyuan Zhang, Xu sun, Yuexian Zou

In this work, we investigate how the scale factors in the effectiveness of the skip connection and reveal that a trivial adjustment of the scale will lead to spurious gradient exploding or vanishing in line with the deepness of the models, which could by addressed by normalization, in particular, layer normalization, which induces consistent improvements over the plain skip connection.

Image Classification Machine Translation +1

Federated Learning for Spoken Language Understanding

no code implementations COLING 2020 Zhiqi Huang, Fenglin Liu, Yuexian Zou

To this end, we propose a federated learning framework, which could unify various types of datasets as well as tasks to learn and fuse various types of knowledge, i. e., text representations, from different datasets and tasks, without the sharing of downstream task data.

Intent Detection slot-filling +4

Towards Data Distillation for End-to-end Spoken Conversational Question Answering

no code implementations18 Oct 2020 Chenyu You, Nuo Chen, Fenglin Liu, Dongchao Yang, Yuexian Zou

In spoken question answering, QA systems are designed to answer questions from contiguous text spans within the related speech transcripts.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

PIN: A Novel Parallel Interactive Network for Spoken Language Understanding

no code implementations28 Sep 2020 Peilin Zhou, Zhiqi Huang, Fenglin Liu, Yuexian Zou

However, we noted that, so far, the efforts to obtain better performance by supporting bidirectional and explicit information exchange between ID and SF are not well studied. In addition, few studies attempt to capture the local context information to enhance the performance of SF.

Intent Detection Language Modelling +3

Rethinking and Improving Natural Language Generation with Layer-Wise Multi-View Decoding

no code implementations16 May 2020 Fenglin Liu, Xuancheng Ren, Guangxiang Zhao, Chenyu You, Xuewei Ma, Xian Wu, Xu sun

While it is common practice to draw information from only the last encoder layer, recent work has proposed to use representations from different encoder layers for diversified levels of information.

Abstractive Text Summarization Image Captioning +5

Exploring and Distilling Cross-Modal Information for Image Captioning

no code implementations28 Feb 2020 Fenglin Liu, Xuancheng Ren, Yuanxin Liu, Kai Lei, Xu sun

Recently, attention-based encoder-decoder models have been used extensively in image captioning.

Attribute Image Captioning

Non-Autoregressive Coarse-to-Fine Video Captioning

1 code implementation27 Nov 2019 Bang Yang, Yuexian Zou, Fenglin Liu, Can Zhang

However, mainstream video captioning methods suffer from slow inference speed due to the sequential manner of autoregressive decoding, and prefer generating generic descriptions due to the insufficient training of visual words (e. g., nouns and verbs) and inadequate decoding paradigm.

Sentence Video Captioning

Self-Adaptive Scaling for Learnable Residual Structure

no code implementations CONLL 2019 Fenglin Liu, Meng Gao, Yuanxin Liu, Kai Lei

Residual has been widely applied to build deep neural networks with enhanced feature propagation and improved accuracy.

Image Captioning Image Classification +2

DLIMD: Dictionary Learning based Image-domain Material Decomposition for spectral CT

no code implementations6 May 2019 Weiwen Wu, Haijun Yu, Peijun Chen, Fulin Luo, Fenglin Liu, Qian Wang, Yining Zhu, Yanbo Zhang, Jian Feng, Hengyong Yu

Second, we employ the direct inversion (DI) method to obtain initial material decomposition results, and a set of image patches are extracted from the mode-1 unfolding of normalized material image tensor to train a united dictionary by the K-SVD technique.

Computed Tomography (CT) Dictionary Learning +1

Block Matching Frame based Material Reconstruction for Spectral CT

no code implementations22 Oct 2018 Weiwen Wu, Qian Wang, Fenglin Liu, Yining Zhu, Hengyong Yu

Spectral computed tomography (CT) has a great potential in material identification and decomposition.

Computed Tomography (CT)

Non-local Low-rank Cube-based Tensor Factorization for Spectral CT Reconstruction

no code implementations24 Jul 2018 Weiwen Wu, Fenglin Liu, Yanbo Zhang, Qian Wang, Hengyong Yu

Then, as a new regularizer, Kronecker-Basis-Representation (KBR) tensor factorization is employed into a basic spectral CT reconstruction model to enhance the ability of extracting image features and protecting spatial edges, generating the non-local low-rank cube-based tensor factorization (NLCTF) method.

Clustering Computed Tomography (CT)

Low-dose spectral CT reconstruction using L0 image gradient and tensor dictionary

no code implementations13 Dec 2017 Weiwen Wu, Yanbo Zhang, Qian Wang, Fenglin Liu, Peijun Chen, Hengyong Yu

The L0TDL method inherits the advantages of tensor dictionary learning (TDL) by employing the similarity of spectral CT images.

Computed Tomography (CT) Dictionary Learning +1

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