Search Results for author:
Found 397 papers, 143 papers with code
Multi-Modal Sarcasm Detection via Cross-Modal Graph Convolutional Network
Then, the descriptions of the objects are served as a bridge to determine the importance of the association between the objects of image modality and the contextual words of text modality, so as to build a cross-modal graph for each multi-modal instance.
BIT-Xiaomi’s System for AutoSimTrans 2022
This system paper describes the BIT-Xiaomi simultaneous translation system for Autosimtrans 2022 simultaneous translation challenge.
融合情感分析的隐式反问句识别模型(Implicit Rhetorical Questions Recognition Model Combined with Sentiment Analysis)
“反问是现代汉语中一种常用的修辞手法, 根据是否含有反问标记可分为显式反问句与隐式反问句。其中隐式反问句表达的情感更为丰富, 表现形式也十分复杂, 对隐式反问句的识别更具挑战性。本文首先扩充了汉语反问句语料库, 语料库规模达到10000余句, 接着针对隐式反问句的特点, 提出了一种融合情感分析的隐式反问句识别模型。模型考虑了句子的语义信息, 上下文信息, 并借助情感分析任务辅助识别隐式反问句。实验结果表明, 本文提出的模型在隐式反问句识别任务上取得了良好的性能。”
Towards Robust Neural Machine Translation with Iterative Scheduled Data-Switch Training
Most existing methods on robust neural machine translation (NMT) construct adversarial examples by injecting noise into authentic examples and indiscriminately exploit two types of examples.
Gait Recognition from a Single Image using a Phase-Aware Gait Cycle Reconstruction Network
Specifically, a phase estimation network is introduced for the input single image, and the gait cycle reconstruction network exploits the estimated phase to mitigate the dependence of an encoded feature on the phase of that single image.
The Xiaomi Text-to-Text Simultaneous Speech Translation System for IWSLT 2022
Our system is built based on the Transformer model with novel techniques borrowed from our recent research work.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
JointCL: A Joint Contrastive Learning Framework for Zero-Shot Stance Detection
In this paper, we propose a joint contrastive learning (JointCL) framework, which consists of stance contrastive learning and target-aware prototypical graph contrastive learning.
Probabilistic Copyright Protection Can Fail for Text-to-Image Generative Models
The booming use of text-to-image generative models has raised concerns about their high risk of producing copyright-infringing content.
EAFP-Med: An Efficient Adaptive Feature Processing Module Based on Prompts for Medical Image Detection
EAFP-Med can efficiently extract lesion features from various medical images based on prompts, enhancing the model's performance.
Soulstyler: Using Large Language Model to Guide Image Style Transfer for Target Object
We introduce a large language model to parse the text and identify stylization goals and specific styles.
Federated Learning via Consensus Mechanism on Heterogeneous Data: A New Perspective on Convergence
We theoretically show that the consensus mechanism can guarantee the convergence of the global objective.
Jailbreaking GPT-4V via Self-Adversarial Attacks with System Prompts
Furthermore, in pursuit of better performance, we also add human modification based on GPT-4's analysis, which further improves the attack success rate to 98. 7\%; 3) We evaluated the effect of modifying system prompts to defend against jailbreaking attacks.
Improving Zero-shot Visual Question Answering via Large Language Models with Reasoning Question Prompts
This results in a set of candidate answers.
Method for Text Entity Linking in Power Distribution Scheduling Oriented to Power Distribution Network Knowledge Graph
The proposed method for linking entities in power distribution dispatch texts to a power distribution network knowledge graph is based on a deep understanding of these networks.
Self-supervised Heterogeneous Graph Variational Autoencoders
Instead of directly reconstructing raw features for attributed nodes, SHAVA generates the initial low-dimensional representation matrix for all the nodes, based on which raw features of attributed nodes are further reconstructed to leverage accurate attributes.
u-LLaVA: Unifying Multi-Modal Tasks via Large Language Model
Firstly, we incorporate the modality alignment module and multi-task modules into LLM.
The NeurIPS 2022 Neural MMO Challenge: A Massively Multiagent Competition with Specialization and Trade
In this paper, we present the results of the NeurIPS-2022 Neural MMO Challenge, which attracted 500 participants and received over 1, 600 submissions.
Efficient Bottom-Up Synthesis for Programs with Local Variables
Our algorithm can reduce the space of programs with local variables.
CBSiMT: Mitigating Hallucination in Simultaneous Machine Translation with Weighted Prefix-to-Prefix Training
Simultaneous machine translation (SiMT) is a challenging task that requires starting translation before the full source sentence is available.
Exploiting Latent Attribute Interaction with Transformer on Heterogeneous Information Networks
In addition, most of them only consider the interactions between nodes while neglecting the high-order information behind the latent interactions among different node features.
Parameter-Agnostic Optimization under Relaxed Smoothness
However, as the assumption is relaxed to the more realistic $(L_0, L_1)$-smoothness, all existing convergence results still necessitate tuning of the stepsize.
Prioritized Propagation in Graph Neural Networks
Specifically, the framework consists of three components: a backbone GNN model, a propagation controller to determine the optimal propagation steps for nodes, and a weight controller to compute the priority scores for nodes.
High-resolution power equipment recognition based on improved self-attention
The current trend of automating inspections at substations has sparked a surge in interest in the field of transformer image recognition.
Retrieval-Augmented Code Generation for Universal Information Extraction
Therefore, in this paper, we propose a universal retrieval-augmented code generation framework based on LLMs, called Code4UIE, for IE tasks.
Resist Label Noise with PGM for Graph Neural Networks
To further leverage the information of clean labels in the noisy label set, we put forward LNP-v2, which incorporates the noisy label set into the Bayesian network to generate clean labels.
Limited Data, Unlimited Potential: A Study on ViTs Augmented by Masked Autoencoders
We explore the appropriate SSL tasks that can be optimized alongside the primary task, the training schemes for these tasks, and the data scale at which they can be most effective.
Multimodal ChatGPT for Medical Applications: an Experimental Study of GPT-4V
In this paper, we critically evaluate the capabilities of the state-of-the-art multimodal large language model, i. e., GPT-4 with Vision (GPT-4V), on Visual Question Answering (VQA) task.
3DCoMPaT$^{++}$: An improved Large-scale 3D Vision Dataset for Compositional Recognition
In this work, we present 3DCoMPaT$^{++}$, a multimodal 2D/3D dataset with 160 million rendered views of more than 10 million stylized 3D shapes carefully annotated at the part-instance level, alongside matching RGB point clouds, 3D textured meshes, depth maps, and segmentation masks.
Enhancing Graph Neural Networks with Structure-Based Prompt
Graph Neural Networks (GNNs) are powerful in learning semantics of graph data.
Label Propagation for Graph Label Noise
In this paper, we study graph label noise in the context of arbitrary heterophily, with the aim of rectifying noisy labels and assigning labels to previously unlabeled nodes.
VR PreM+ : An Immersive Pre-learning Branching Visualization System for Museum Tours
We present VR PreM+, an innovative VR system designed to enhance web exploration beyond traditional computer screens.
Zone Evaluation: Revealing Spatial Bias in Object Detection
A fundamental limitation of object detectors is that they suffer from "spatial bias", and in particular perform less satisfactorily when detecting objects near image borders.
Uncertainty-aware Parameter-Efficient Self-training for Semi-supervised Language Understanding
The recent success of large pre-trained language models (PLMs) heavily hinges on massive labeled data, which typically produces inferior performance in low-resource scenarios.
Prompt Tuning for Multi-View Graph Contrastive Learning
Third, we design a prompting tuning method for our multi-view graph contrastive learning method to bridge the gap between pretexts and downsteam tasks.
Empower Text-Attributed Graphs Learning with Large Language Models (LLMs)
In order to tackle this challenge, we propose a lightweight paradigm called ENG, which adopts a plug-and-play approach to empower text-attributed graphs through node generation using LLMs.
DropMix: Better Graph Contrastive Learning with Harder Negative Samples
Recently, Mixup has been introduced to synthesize hard negative samples in graph contrastive learning (GCL).
Context-aware Session-based Recommendation with Graph Neural Networks
Session-based recommendation (SBR) is a task that aims to predict items based on anonymous sequences of user behaviors in a session.
MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning
Motivated by this, we target to build a unified interface for completing many vision-language tasks including image description, visual question answering, and visual grounding, among others.
DialCoT Meets PPO: Decomposing and Exploring Reasoning Paths in Smaller Language Models
Chain-of-Thought (CoT) prompting has proven to be effective in enhancing the reasoning capabilities of Large Language Models (LLMs) with at least 100 billion parameters.
ChatRadio-Valuer: A Chat Large Language Model for Generalizable Radiology Report Generation Based on Multi-institution and Multi-system Data
Radiology report generation, as a key step in medical image analysis, is critical to the quantitative analysis of clinically informed decision-making levels.
Uncertainty Quantification in Inverse Models in Hydrology
Our inverse model offers 3\% improvement in R$^2$ for the inverse model (basin characteristic estimation) and 6\% for the forward model (streamflow prediction).
Completing Visual Objects via Bridging Generation and Segmentation
This paper presents a novel approach to object completion, with the primary goal of reconstructing a complete object from its partially visible components.
Corex: Pushing the Boundaries of Complex Reasoning through Multi-Model Collaboration
Large Language Models (LLMs) are evolving at an unprecedented pace and have exhibited considerable capability in the realm of natural language processing (NLP) with world knowledge.
Rethinking Audiovisual Segmentation with Semantic Quantization and Decomposition
Specifically, we propose a semantic decomposition method based on product quantization, where the multi-source semantics can be decomposed and represented by several quantized single-source semantics.
Generating Personalized Insulin Treatments Strategies with Deep Conditional Generative Time Series Models
We propose a novel framework that combines deep generative time series models with decision theory for generating personalized treatment strategies.
MedEdit: Model Editing for Medical Question Answering with External Knowledge Bases
Focusing on medical QA using the MedQA-SMILE dataset, we evaluate the impact of different retrieval models and the number of facts provided to the LLM.
AutoPrep: An Automatic Preprocessing Framework for In-the-Wild Speech Data
To address this issue, we introduce an automatic in-the-wild speech data preprocessing framework (AutoPrep) in this paper, which is designed to enhance speech quality, generate speaker labels, and produce transcriptions automatically.
MediViSTA-SAM: Zero-shot Medical Video Analysis with Spatio-temporal SAM Adaptation
The Segmentation Anything Model (SAM) has attracted considerable attention as a foundational model well-known for its robust generalization capabilities across various downstream tasks.
Asca: less audio data is more insightful
Audio recognition in specialized areas such as birdsong and submarine acoustics faces challenges in large-scale pre-training due to the limitations in available samples imposed by sampling environments and specificity requirements.
Invisible Watermarking for Audio Generation Diffusion Models
Diffusion models have gained prominence in the image domain for their capabilities in data generation and transformation, achieving state-of-the-art performance in various tasks in both image and audio domains.
A Discourse-level Multi-scale Prosodic Model for Fine-grained Emotion Analysis
More interestingly, although we aim at the synthesis effect of the style transfer model, the synthesized speech by the proposed text prosodic analysis model is even better than the style transfer from the original speech in some user evaluation indicators.
PolicyGPT: Automated Analysis of Privacy Policies with Large Language Models
In this study, we investigate a privacy policy text analysis framework PolicyGPT based on the LLM.
RadOnc-GPT: A Large Language Model for Radiation Oncology
This paper presents RadOnc-GPT, a large language model specialized for radiation oncology through advanced tuning methods.
MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Image Segmentation
The Segment Anything Model (SAM), a foundation model for general image segmentation, has demonstrated impressive zero-shot performance across numerous natural image segmentation tasks.
SnakeGAN: A Universal Vocoder Leveraging DDSP Prior Knowledge and Periodic Inductive Bias
However, it is still challenging to train a universal vocoder which can generalize well to out-of-domain (OOD) scenarios, such as unseen speaking styles, non-speech vocalization, singing, and musical pieces.
Quantifying and Attributing the Hallucination of Large Language Models via Association Analysis
Although demonstrating superb performance on various NLP tasks, large language models (LLMs) still suffer from the hallucination problem, which threatens the reliability of LLMs.
FLM-101B: An Open LLM and How to Train It with $100K Budget
We demonstrate that a 101B-parameter LLM with 0. 31T tokens can be trained with a budget of 100K US dollars.
Exchanging-based Multimodal Fusion with Transformer
To solve these issues, in this paper, we propose a novel exchanging-based multimodal fusion model MuSE for text-vision fusion based on Transformer.
Graph Self-Contrast Representation Learning
Further, Since the triplet loss only optimizes the relative distance between the anchor and its positive/negative samples, it is difficult to ensure the absolute distance between the anchor and positive sample.
RigNet++: Efficient Repetitive Image Guided Network for Depth Completion
In the latter branch, we introduce a repetitive guidance module based on dynamic convolution, in which an efficient convolution factorization is proposed to reduce the complexity while modeling high-frequency structures progressively.
StratMed: Relevance Stratification between Biomedical Entities for Sparsity on Medication Recommendation
After that, we design a pyramid-like stratification method based on relevance to strengthen the expressiveness of sparse data.
Listen to Minority: Encrypted Traffic Classification for Class Imbalance with Contrastive Pre-Training
Despite some existing learning-based ETC methods showing promising results, three-fold limitations still remain in real-world network environments, 1) label bias caused by traffic class imbalance, 2) traffic homogeneity caused by component sharing, and 3) training with reliance on sufficient labeled traffic.
Radiology-Llama2: Best-in-Class Large Language Model for Radiology
This paper introduces Radiology-Llama2, a large language model specialized for radiology through a process known as instruction tuning.
Rethinking Mobile AI Ecosystem in the LLM Era
Concurrently, each app contributes a concise, offline fine-tuned "adapter" tailored to distinct downstream tasks.
Federated Fine-tuning of Billion-Sized Language Models across Mobile Devices
Federated Learning (FL), a method to preserve user data privacy, is often employed in fine-tuning LLMs to downstream mobile tasks, an approach known as FedLLM.
Decoding Natural Images from EEG for Object Recognition
This paper presents a self-supervised framework to demonstrate the feasibility of learning image representations from EEG signals, particularly for object recognition.
ADNet: Lane Shape Prediction via Anchor Decomposition
In this paper, we revisit the limitations of anchor-based lane detection methods, which have predominantly focused on fixed anchors that stem from the edges of the image, disregarding their versatility and quality.
AltNeRF: Learning Robust Neural Radiance Field via Alternating Depth-Pose Optimization
Neural Radiance Fields (NeRF) have shown promise in generating realistic novel views from sparse scene images.
No Regularization is Needed: An Efficient and Effective Model for Incomplete Label Distribution Learning
Then, by using the prior of degrees, we design a weighted scheme and verify its effectiveness.
TBIN: Modeling Long Textual Behavior Data for CTR Prediction
Click-through rate (CTR) prediction plays a pivotal role in the success of recommendations.
Heterogeneous Knowledge Fusion: A Novel Approach for Personalized Recommendation via LLM
The analysis and mining of user heterogeneous behavior are of paramount importance in recommendation systems.
Relation-Oriented: Toward Causal Knowledge-Aligned AGI
Observation-Oriented paradigm currently dominates relationship learning models, including AI-based ones, which inherently do not account for relationships with temporally nonlinear effects.
You Can Backdoor Personalized Federated Learning
The resistance of pFL methods with parameter decoupling is attributed to the heterogeneous classifiers between malicious clients and benign counterparts.
UPFL: Unsupervised Personalized Federated Learning towards New Clients
To address this challenge, we extend the adaptive risk minimization technique into the unsupervised personalized federated learning setting and propose our method, FedTTA.
MUSE: Multi-View Contrastive Learning for Heterophilic Graphs
Finally, an alternating training scheme is adopted to ensure that unsupervised node representation learning and information fusion controller can mutually reinforce each other.
RSGPT: A Remote Sensing Vision Language Model and Benchmark
This dataset consists of human-annotated captions and visual question-answer pairs, allowing for a comprehensive assessment of VLMs in the context of RS.
Rethinking Voice-Face Correlation: A Geometry View
Previous works on voice-face matching and voice-guided face synthesis demonstrate strong correlations between voice and face, but mainly rely on coarse semantic cues such as gender, age, and emotion.
The Hidden Dance of Phonemes and Visage: Unveiling the Enigmatic Link between Phonemes and Facial Features
This work unveils the enigmatic link between phonemes and facial features.
Creative Birds: Self-Supervised Single-View 3D Style Transfer
Our focus lies primarily on birds, a popular subject in 3D reconstruction, for which no existing single-view 3D transfer methods have been developed. The method we propose seeks to generate a 3D mesh shape and texture of a bird from two single-view images.
Evaluating Large Language Models for Radiology Natural Language Processing
The rise of large language models (LLMs) has marked a pivotal shift in the field of natural language processing (NLP).
CohortGPT: An Enhanced GPT for Participant Recruitment in Clinical Study
Participant recruitment based on unstructured medical texts such as clinical notes and radiology reports has been a challenging yet important task for the cohort establishment in clinical research.
Forward Laplacian: A New Computational Framework for Neural Network-based Variational Monte Carlo
Neural network-based variational Monte Carlo (NN-VMC) has emerged as a promising cutting-edge technique of ab initio quantum chemistry.
TableGPT: Towards Unifying Tables, Nature Language and Commands into One GPT
Tables are prevalent in real-world databases, requiring significant time and effort for humans to analyze and manipulate.
Exploring Multimodal Approaches for Alzheimer's Disease Detection Using Patient Speech Transcript and Audio Data
As AD impairs the patient's language understanding and expression ability, the speech of AD patients can serve as an indicator of this disease.
Crossway Diffusion: Improving Diffusion-based Visuomotor Policy via Self-supervised Learning
Sequence modeling approaches have shown promising results in robot imitation learning.
Identification of Causal Relationship between Amyloid-beta Accumulation and Alzheimer's Disease Progression via Counterfactual Inference
Alzheimer's disease (AD) is a neurodegenerative disorder that is beginning with amyloidosis, followed by neuronal loss and deterioration in structure, function, and cognition.
SAMAug: Point Prompt Augmentation for Segment Anything Model
Starting with an initial point prompt, SAM produces an initial mask, which is then fed into our proposed SAMAug to generate augmented point prompts.
Review of Large Vision Models and Visual Prompt Engineering
This review aims to summarize the methods employed in the computer vision domain for large vision models and visual prompt engineering, exploring the latest advancements in visual prompt engineering.
Higher-order Graph Attention Network for Stock Selection with Joint Analysis
H-GAT is able to capture higher-order structures and jointly incorporate factors of fundamental analysis with factors of technical analysis.
Privacy-Preserving Community Detection for Locally Distributed Multiple Networks
To remove the bias incurred by RR and the squared network matrices, we develop a two-step bias-adjustment procedure.
Segment Anything Model (SAM) for Radiation Oncology
Given that SAM, a model pre-trained purely on natural images, can handle the delineation of OARs from medical images with clinically acceptable accuracy, these results highlight SAM's robust generalization capabilities with consistent accuracy in automatic segmentation for radiotherapy.
CrossKD: Cross-Head Knowledge Distillation for Dense Object Detection
Such a distillation manner relieves the student's head from receiving contradictory supervision signals from the ground-truth annotations and the teacher's predictions, greatly improving the student's detection performance.
AD-AutoGPT: An Autonomous GPT for Alzheimer's Disease Infodemiology
In this pioneering study, inspired by AutoGPT, the state-of-the-art open-source application based on the GPT-4 large language model, we develop a novel tool called AD-AutoGPT which can conduct data collection, processing, and analysis about complex health narratives of Alzheimer's Disease in an autonomous manner via users' textual prompts.
Lexical Speaker Error Correction: Leveraging Language Models for Speaker Diarization Error Correction
Speaker diarization (SD) is typically used with an automatic speech recognition (ASR) system to ascribe speaker labels to recognized words.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Radiology-GPT: A Large Language Model for Radiology
We introduce Radiology-GPT, a large language model for radiology.
Network Robustness Learning via Graph Transformer
Learning and analysis of network robustness, including controllability robustness and connectivity robustness, is critical for various networked systems against attacks.
Shapley Value on Probabilistic Classifiers
Data valuation has become an increasingly significant discipline in data science due to the economic value of data.
Boosting Language Models Reasoning with Chain-of-Knowledge Prompting
To mitigate this brittleness, we propose a novel Chain-of-Knowledge (CoK) prompting, where we aim at eliciting LLMs to generate explicit pieces of knowledge evidence in the form of structure triple.
Artificial General Intelligence for Medical Imaging
In this review, we explore the potential applications of Artificial General Intelligence (AGI) models in healthcare, focusing on foundational Large Language Models (LLMs), Large Vision Models, and Large Multimodal Models.
Variable Radiance Field for Real-Life Category-Specifc Reconstruction from Single Image
Our key contributions are: (1) We parameterize the geometry and appearance of the object using a multi-scale global feature extractor, which avoids frequent point-wise feature retrieval and camera dependency.
Fine-Grained Visual Prompting
Vision-Language Models (VLMs), such as CLIP, have demonstrated impressive zero-shot transfer capabilities in image-level visual perception.
Modeling Dual Period-Varying Preferences for Takeaway Recommendation
Different from traditional recommendation, takeaway recommendation faces two main challenges: (1) Dual Interaction-Aware Preference Modeling.
PaintSeg: Training-free Segmentation via Painting
The paper introduces PaintSeg, a new unsupervised method for segmenting objects without any training.
Exploring Better Text Image Translation with Multimodal Codebook
Text image translation (TIT) aims to translate the source texts embedded in the image to target translations, which has a wide range of applications and thus has important research value.
BiomedGPT: A Unified and Generalist Biomedical Generative Pre-trained Transformer for Vision, Language, and Multimodal Tasks
In this paper, we introduce a unified and generalist Biomedical Generative Pre-trained Transformer (BiomedGPT) model, which leverages self-supervision on large and diverse datasets to accept multi-modal inputs and perform a range of downstream tasks.
Ranked #1 on
Text Summarization
on MeQSum
TransCoder: Towards Unified Transferable Code Representation Learning Inspired by Human Skills
To tackle the issue, in this paper, we present TransCoder, a unified Transferable fine-tuning strategy for Code representation learning.
Is Synthetic Data From Diffusion Models Ready for Knowledge Distillation?
Diffusion models have recently achieved astonishing performance in generating high-fidelity photo-realistic images.
StereoVAE: A lightweight stereo matching system through embedded GPUs
We present a lightweight system for stereo matching through embedded GPUs.
S$^3$HQA: A Three-Stage Approach for Multi-hop Text-Table Hybrid Question Answering
In this paper, we propose a three-stage TextTableQA framework S3HQA, which comprises of retriever, selector, and reasoner.
When Gradient Descent Meets Derivative-Free Optimization: A Match Made in Black-Box Scenario
In this paper, we introduce gradient descent into black-box tuning scenario through knowledge distillation.
Make Prompt-based Black-Box Tuning Colorful: Boosting Model Generalization from Three Orthogonal Perspectives
Large language models (LLMs) have shown increasing power on various natural language processing (NLP) tasks.
SPP-CNN: An Efficient Framework for Network Robustness Prediction
This paper addresses the robustness of a network to sustain its connectivity and controllability against malicious attacks.
Dual Intent Enhanced Graph Neural Network for Session-based New Item Recommendation
We design a dual-intent network to learn user intent from an attention mechanism and the distribution of historical data respectively, which can simulate users' decision-making process in interacting with a new item.
Vision-Language Models in Remote Sensing: Current Progress and Future Trends
This makes them better suited for tasks that require both visual and textual understanding, such as image captioning, text-based image retrieval, and visual question answering.
ChatGraph: Interpretable Text Classification by Converting ChatGPT Knowledge to Graphs
To tackle these limitations, we propose a novel framework that leverages the power of ChatGPT for specific tasks, such as text classification, while improving its interpretability.
FreeLM: Fine-Tuning-Free Language Model
FreeLM outperforms large models e. g., GPT-3 and InstructGPT, on a range of language understanding tasks in experiments.
Instruction-ViT: Multi-Modal Prompts for Instruction Learning in ViT
Prompts have been proven to play a crucial role in large language models, and in recent years, vision models have also been using prompts to improve scalability for multiple downstream tasks.
Prompt Engineering for Healthcare: Methodologies and Applications
This review will introduce the latest advances in prompt engineering in the field of natural language processing (NLP) for the medical domain.
Asymptotic Behaviors and Phase Transitions in Projected Stochastic Approximation: A Jump Diffusion Approach
Additionally, we propose the Debiased LPSA (DLPSA) as a practical application of our jump diffusion approximation result.
Differentiate ChatGPT-generated and Human-written Medical Texts
We focus on analyzing the differences between medical texts written by human experts and generated by ChatGPT, and designing machine learning workflows to effectively detect and differentiate medical texts generated by ChatGPT.
ChatABL: Abductive Learning via Natural Language Interaction with ChatGPT
The proposed method uses the strengths of LLMs' understanding and logical reasoning to correct the incomplete logical facts for optimizing the performance of perceptual module, by summarizing and reorganizing reasoning rules represented in natural language format.
MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models
Our work, for the first time, uncovers that properly aligning the visual features with an advanced large language model can possess numerous advanced multi-modal abilities demonstrated by GPT-4, such as detailed image description generation and website creation from hand-drawn drafts.
Ranked #13 on
Visual Question Answering (VQA)
on InfiMM-Eval
Exploring the Trade-Offs: Unified Large Language Models vs Local Fine-Tuned Models for Highly-Specific Radiology NLI Task
To this end, in this study, we evaluate the performance of ChatGPT/GPT-4 on a radiology NLI task and compare it to other models fine-tuned specifically on task-related data samples.
ImpressionGPT: An Iterative Optimizing Framework for Radiology Report Summarization with ChatGPT
The 'Impression' section of a radiology report is a critical basis for communication between radiologists and other physicians, and it is typically written by radiologists based on the 'Findings' section.
Video ChatCaptioner: Towards Enriched Spatiotemporal Descriptions
Video captioning aims to convey dynamic scenes from videos using natural language, facilitating the understanding of spatiotemporal information within our environment.
MoStGAN-V: Video Generation with Temporal Motion Styles
Video generation remains a challenging task due to spatiotemporal complexity and the requirement of synthesizing diverse motions with temporal consistency.
Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Models
This paper presents a comprehensive survey of ChatGPT-related (GPT-3. 5 and GPT-4) research, state-of-the-art large language models (LLM) from the GPT series, and their prospective applications across diverse domains.
Evaluating Large Language Models on a Highly-specialized Topic, Radiation Oncology Physics
We present the first study to investigate Large Language Models (LLMs) in answering radiation oncology physics questions.
When Brain-inspired AI Meets AGI
Artificial General Intelligence (AGI) has been a long-standing goal of humanity, with the aim of creating machines capable of performing any intellectual task that humans can do.
On de novo Bridging Paired-end RNA-seq Data
Methods have been proposed to bridge paired-end reads in the presence of reference genome (called reference-based bridging), but the algorithms are far away from scaling for de novo bridging as the underlying compacted de Bruijn graph(cdBG) used in the latter task often contains millions of vertices and edges.
A Survey of Historical Learning: Learning Models with Learning History
The various types of elements, deposited in the training history, are a large amount of wealth for improving learning deep models.
DeID-GPT: Zero-shot Medical Text De-Identification by GPT-4
The digitization of healthcare has facilitated the sharing and re-using of medical data but has also raised concerns about confidentiality and privacy.
Large Selective Kernel Network for Remote Sensing Object Detection
To the best of our knowledge, this is the first time that large and selective kernel mechanisms have been explored in the field of remote sensing object detection.
Ranked #1 on
Semantic Segmentation
on UAVid
Digital staining in optical microscopy using deep learning -- a review
Until recently, conventional biochemical staining had the undisputed status as well-established benchmark for most biomedical problems related to clinical diagnostics, fundamental research and biotechnology.
Sufficient Control of Complex Networks
In this paper, we propose to study on sufficient control of complex networks which is to control a sufficiently large portion of the network, where only the quantity of controllable nodes matters.
Variance-aware robust reinforcement learning with linear function approximation under heavy-tailed rewards
Building upon AdaOFUL, we propose VARA for linear MDPs, which achieves a tighter variance-aware regret bound of $\widetilde{O}(d\sqrt{HG^*K})$.
Non-aligned supervision for Real Image Dehazing
In particular, we explore a non-alignment setting by utilizing a clear reference image that is not aligned with the hazy input image to supervise the dehazing network through a multi-scale reference loss that compares the features of the two images.
Rethinking the Reasonability of the Test Set for Simultaneous Machine Translation
Simultaneous machine translation (SimulMT) models start translation before the end of the source sentence, making the translation monotonically aligned with the source sentence.
HopFIR: Hop-wise GraphFormer with Intragroup Joint Refinement for 3D Human Pose Estimation
The HGF module groups the joints by k-hop neighbors and applies a hopwise transformer-like attention mechanism to these groups to discover latent joint synergies.
Ranked #138 on
3D Human Pose Estimation
on Human3.6M
Pushing One Pair of Labels Apart Each Time in Multi-Label Learning: From Single Positive to Full Labels
In Multi-Label Learning (MLL), it is extremely challenging to accurately annotate every appearing object due to expensive costs and limited knowledge.
GradMA: A Gradient-Memory-based Accelerated Federated Learning with Alleviated Catastrophic Forgetting
Federated Learning (FL) has emerged as a de facto machine learning area and received rapid increasing research interests from the community.
AugGPT: Leveraging ChatGPT for Text Data Augmentation
Text data augmentation is an effective strategy for overcoming the challenge of limited sample sizes in many natural language processing (NLP) tasks.
Statistical Analysis of Karcher Means for Random Restricted PSD Matrices
Non-asymptotic statistical analysis is often missing for modern geometry-aware machine learning algorithms due to the possibly intricate non-linear manifold structure.
MM-SFENet: Multi-scale Multi-task Localization and Classification of Bladder Cancer in MRI with Spatial Feature Encoder Network
Conclusions: The experimental result demonstrates the effectiveness of the proposed MM-SFENet on the localization and classification of bladder cancer.
Mask-guided BERT for Few Shot Text Classification
The main challenge of FSL is the difficulty of training robust models on small amounts of samples, which frequently leads to overfitting.
Online Statistical Inference for Nonlinear Stochastic Approximation with Markovian Data
We study the statistical inference of nonlinear stochastic approximation algorithms utilizing a single trajectory of Markovian data.
Meta-Learning Triplet Network with Adaptive Margins for Few-Shot Named Entity Recognition
We design an improved triplet network to map samples and prototype vectors into a low-dimensional space that is easier to be classified and propose an adaptive margin for each entity type.
Meta-Learning Siamese Network for Few-Shot Text Classification
Few-shot learning has been used to tackle the problem of label scarcity in text classification, of which meta-learning based methods have shown to be effective, such as the prototypical networks (PROTO).
Structure Flow-Guided Network for Real Depth Super-Resolution
Specifically, our framework consists of a cross-modality flow-guided upsampling network (CFUNet) and a flow-enhanced pyramid edge attention network (PEANet).
Recurrent Structure Attention Guidance for Depth Super-Resolution
Second, instead of the coarse concatenation guidance, we propose a recurrent structure attention block, which iteratively utilizes the latest depth estimation and the image features to jointly select clear patterns and boundaries, aiming at providing refined guidance for accurate depth recovery.
Decision-Making Context Interaction Network for Click-Through Rate Prediction
Click-through rate (CTR) prediction is crucial in recommendation and online advertising systems.
SeeGera: Self-supervised Semi-implicit Graph Variational Auto-encoders with Masking
In this paper, to comprehensively enhance the performance of generative graph SSL against other GCL models on both unsupervised and supervised learning tasks, we propose the SeeGera model, which is based on the family of self-supervised variational graph auto-encoder (VGAE).
Towards Spatial Equilibrium Object Detection
In this paper, we study the spatial disequilibrium problem of modern object detectors and propose to quantify this ``spatial bias'' by measuring the detection performance over zones.
DLGSANet: Lightweight Dynamic Local and Global Self-Attention Networks for Image Super-Resolution
We develop a hybrid dynamic-Transformer block(HDTB) that integrates the MHDLSA and SparseGSA for both local and global feature exploration.
FishNet: A Large-scale Dataset and Benchmark for Fish Recognition, Detection, and Functional Trait Prediction
Aquatic species are essential components of the world's ecosystem, and the preservation of aquatic biodiversity is crucial for maintaining proper ecosystem functioning.
Robust Referring Video Object Segmentation with Cyclic Structural Consensus
Our model achieves state-of-the-art performance on R-VOS benchmarks, Ref-DAVIS17 and Ref-Youtube-VOS, and also our RRYTVOS dataset.
Referring Video Object Segmentation
Semantic Segmentation
+1