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Found 530 papers, 189 papers with code
OIE@OIA: an Adaptable and Efficient Open Information Extraction Framework
OIE@OIA follows the methodology of Open Information eXpression (OIX): parsing a sentence to an Open Information Annotation (OIA) Graph and then adapting the OIA graph to different OIE tasks with simple rules.
A Predicate-Function-Argument Annotation of Natural Language for Open-Domain Information eXpression
Based on the same platform of OIX, the OIE strategies are reusable, and people can select a set of strategies to assemble their algorithm for a specific task so that the adaptability may be significantly increased.
NESTFUL: A Benchmark for Evaluating LLMs on Nested Sequences of API Calls
In this paper, we present NESTFUL, a benchmark to evaluate LLMs on nested sequences of API calls, i. e., sequences where the output of one API call is passed as input to a subsequent call.
Malacopula: adversarial automatic speaker verification attacks using a neural-based generalised Hammerstein model
We present Malacopula, a neural-based generalised Hammerstein model designed to introduce adversarial perturbations to spoofed speech utterances so that they better deceive automatic speaker verification (ASV) systems.
ASVspoof 5: Crowdsourced Speech Data, Deepfakes, and Adversarial Attacks at Scale
ASVspoof 5 is the fifth edition in a series of challenges that promote the study of speech spoofing and deepfake attacks, and the design of detection solutions.
An Explainable Non-local Network for COVID-19 Diagnosis
The network is embedded with a nonlocal module to capture global information, while a 3D attention module is embedded to focus on the details of the lesion so that it can directly analyze the 3D lung CT and output the classification results.
MGFs: Masked Gaussian Fields for Meshing Building based on Multi-View Images
Compared to the traditional photogrammetric and NeRF-based solutions, recently, Gaussian fields-based methods have exhibited significant potential in generating surface meshes due to their time-efficient training and detailed 3D information preservation.
Multi-weather Cross-view Geo-localization Using Denoising Diffusion Models
For image restoration, MCGF incorporates a shared encoder and a lightweight restoration module to help the backbone eliminate weather-specific information.
AdvQDet: Detecting Query-Based Adversarial Attacks with Adversarial Contrastive Prompt Tuning
During this process, the queries made to the target model are intermediate adversarial examples crafted at the previous attack step, which share high similarities in the pixel space.
U-MedSAM: Uncertainty-aware MedSAM for Medical Image Segmentation
Medical Image Foundation Models have proven to be powerful tools for mask prediction across various datasets.
DFE-IANet: A Method for Polyp Image Classification Based on Dual-domain Feature Extraction and Interaction Attention
This challenge is mainly attributed to the fact that polyps are similar to other pathologies and have complex features influenced by texture, color, and morphology.
RRAM-Based Bio-Inspired Circuits for Mobile Epileptic Correlation Extraction and Seizure Prediction
Non-invasive mobile electroencephalography (EEG) acquisition systems have been utilized for long-term monitoring of seizures, yet they suffer from limited battery life.
Apple Intelligence Foundation Language Models
We present foundation language models developed to power Apple Intelligence features, including a ~3 billion parameter model designed to run efficiently on devices and a large server-based language model designed for Private Cloud Compute.
A Multimodal Knowledge-enhanced Whole-slide Pathology Foundation Model
To our knowledge, this is the first attempt to incorporate multimodal knowledge at the slide level for enhancing pathology FMs, expanding the modelling context from unimodal to multimodal knowledge and from patch-level to slide-level.
Multi-sentence Video Grounding for Long Video Generation
(iii) We also attempt video morphing and personalized generation methods to improve the subject consistency of long video generation, providing ablation experimental results for the subtasks of long video generation.
The VoicePrivacy 2022 Challenge: Progress and Perspectives in Voice Anonymisation
The VoicePrivacy Challenge promotes the development of voice anonymisation solutions for speech technology.
A Benchmark for Multi-speaker Anonymization
To achieve that, a cascaded system uses speaker diarization to aggregate the speech of each speaker and speaker anonymization to conceal speaker privacy and preserve speech content.
SfM on-the-fly: Get better 3D from What You Capture
In the last twenty years, Structure from Motion (SfM) has been a constant research hotspot in the fields of photogrammetry, computer vision, robotics etc., whereas real-time performance is just a recent topic of growing interest.
Non-Adversarial Learning: Vector-Quantized Common Latent Space for Multi-Sequence MRI
We propose a generative model that compresses discrete representations of each sequence to estimate the Gaussian distribution of vector-quantized common (VQC) latent space between multiple sequences.
52B to 1T: Lessons Learned via Tele-FLM Series
Large Language Models (LLMs) represent a significant stride toward Artificial General Intelligence.
Large Language Model Enhanced Knowledge Representation Learning: A Survey
The integration of Large Language Models (LLM) with Knowledge Representation Learning (KRL) signifies a significant advancement in the field of artificial intelligence (AI), enhancing the ability to capture and utilize both structure and textual information.
PM-VIS+: High-Performance Video Instance Segmentation without Video Annotation
This method achieves high video instance segmentation performance without manual video annotations, offering a cost-effective solution and new perspectives for video instance segmentation applications.
Q-DiT: Accurate Post-Training Quantization for Diffusion Transformers
Specifically, when quantizing DiT-XL/2 to W8A8 on ImageNet 256x256, Q-DiT achieves a remarkable reduction in FID by 1. 26 compared to the baseline.
Robustly Optimized Deep Feature Decoupling Network for Fatty Liver Diseases Detection
This can lead to significant differences in recognition accuracy between classes and obvious recognition weaknesses.
Towards Lightweight Graph Neural Network Search with Curriculum Graph Sparsification
To search for optimal lightweight Graph Neural Networks (GNNs), we propose a Lightweight Graph Neural Architecture Search with Graph SparsIfication and Network Pruning (GASSIP) method.
Meta-GCN: A Dynamically Weighted Loss Minimization Method for Dealing with the Data Imbalance in Graph Neural Networks
Although many real-world applications, such as disease prediction, and fault detection suffer from class imbalance, most existing graph-based classification methods ignore the skewness of the distribution of classes; therefore, tend to be biased towards the majority class(es).
Belief Information based Deep Channel Estimation for Massive MIMO Systems
In the next generation wireless communication system, transmission rates should continue to rise to support emerging scenarios, e. g., the immersive communications.
Unifying Unsupervised Graph-Level Anomaly Detection and Out-of-Distribution Detection: A Benchmark
To bridge the gap, in this work, we present a Unified Benchmark for unsupervised Graph-level OOD and anomaly Detection (our method), a comprehensive evaluation framework that unifies GLAD and GLOD under the concept of generalized graph-level OOD detection.
Towards Dynamic Resource Allocation and Client Scheduling in Hierarchical Federated Learning: A Two-Phase Deep Reinforcement Learning Approach
Federated learning (FL) is a viable technique to train a shared machine learning model without sharing data.
Is A Picture Worth A Thousand Words? Delving Into Spatial Reasoning for Vision Language Models
Large language models (LLMs) and vision-language models (VLMs) have demonstrated remarkable performance across a wide range of tasks and domains.
Trustworthy Enhanced Multi-view Multi-modal Alzheimer's Disease Prediction with Brain-wide Imaging Transcriptomics Data
Here, we propose TMM, a trusted multiview multimodal graph attention framework for AD diagnosis, using extensive brain-wide transcriptomics and imaging data.
Efficient Sharpness-Aware Minimization for Molecular Graph Transformer Models
Sharpness-aware minimization (SAM) has received increasing attention in computer vision since it can effectively eliminate the sharp local minima from the training trajectory and mitigate generalization degradation.
D2O: Dynamic Discriminative Operations for Efficient Generative Inference of Large Language Models
Efficient inference in Large Language Models (LLMs) is impeded by the growing memory demands of key-value (KV) caching, especially for longer sequences.
Revisiting and Improving Scoring Fusion for Spoofing-aware Speaker Verification Using Compositional Data Analysis
The outcomes of these findings, namely, the score calibration before fusion, improved linear fusion, and better non-linear fusion, were found to be effective on the SASV challenge database.
An Initial Investigation of Language Adaptation for TTS Systems under Low-resource Scenarios
Self-supervised learning (SSL) representations from massively multilingual models offer a promising solution for low-resource language speech tasks.
Spoof Diarization: "What Spoofed When" in Partially Spoofed Audio
As a pioneering study in spoof diarization, we focus on defining the task, establishing evaluation metrics, and proposing a benchmark model, namely the Countermeasure-Condition Clustering (3C) model.
Inter-slice Super-resolution of Magnetic Resonance Images by Pre-training and Self-supervised Fine-tuning
To reduce slice spacing, deep-learning-based super-resolution techniques are widely investigated.
To what extent can ASV systems naturally defend against spoofing attacks?
The current automatic speaker verification (ASV) task involves making binary decisions on two types of trials: target and non-target.
Attribute-Aware Implicit Modality Alignment for Text Attribute Person Search
Text attribute person search aims to find specific pedestrians through given textual attributes, which is very meaningful in the scene of searching for designated pedestrians through witness descriptions.
Seed-TTS: A Family of High-Quality Versatile Speech Generation Models
Seed-TTS offers superior controllability over various speech attributes such as emotion and is capable of generating highly expressive and diverse speech for speakers in the wild.
Precise Analysis of Covariance Identifiability for Activity Detection in Grant-Free Random Access
We consider the identifiability issue of maximum likelihood based activity detection in massive MIMO based grant-free random access.
AI-Face: A Million-Scale Demographically Annotated AI-Generated Face Dataset and Fairness Benchmark
However, no existing dataset comprehensively encompasses both demographic attributes and diverse generative methods, which hinders the development of fair detectors for AI-generated faces.
Balancing User Preferences by Social Networks: A Condition-Guided Social Recommendation Model for Mitigating Popularity Bias
More specifically, CGSoRec first includes a Condition-Guided Social Denoising Model (CSD) to remove redundant social relations in the social network for capturing users' social preferences with items more precisely.
Causal-Aware Graph Neural Architecture Search under Distribution Shifts
We propose to handle the distribution shifts in the graph architecture search process by discovering and exploiting the causal relationship between graphs and architectures to search for the optimal architectures that can generalize under distribution shifts.
UU-Mamba: Uncertainty-aware U-Mamba for Cardiac Image Segmentation
Biomedical image segmentation is critical for accurate identification and analysis of anatomical structures in medical imaging, particularly in cardiac MRI.
Rethinking Independent Cross-Entropy Loss For Graph-Structured Data
We learn the joint distribution of node and cluster labels conditioned on their representations, and train GNNs with the obtained joint loss.
DisenStudio: Customized Multi-subject Text-to-Video Generation with Disentangled Spatial Control
To tackle the problems, in this paper, we propose DisenStudio, a novel framework that can generate text-guided videos for customized multiple subjects, given few images for each subject.
Unsupervised Multimodal Clustering for Semantics Discovery in Multimodal Utterances
UMC shows remarkable improvements of 2-6\% scores in clustering metrics over state-of-the-art methods, marking the first successful endeavor in this domain.
EPPS: Advanced Polyp Segmentation via Edge Information Injection and Selective Feature Decoupling
Furthermore, we introduce a component called Selective Feature Decoupler (SFD) to suppress the influence of noise and extraneous features on the model.
Combining multiple post-training techniques to achieve most efficient quantized LLMs
Large Language Models (LLMs) have distinguished themselves with outstanding performance in complex language modeling tasks, yet they come with significant computational and storage challenges.
PrivSGP-VR: Differentially Private Variance-Reduced Stochastic Gradient Push with Tight Utility Bounds
In this paper, we propose a differentially private decentralized learning method (termed PrivSGP-VR) which employs stochastic gradient push with variance reduction and guarantees $(\epsilon, \delta)$-differential privacy (DP) for each node.
Tele-FLM Technical Report
Large language models (LLMs) have showcased profound capabilities in language understanding and generation, facilitating a wide array of applications.
Optimizing OOD Detection in Molecular Graphs: A Novel Approach with Diffusion Models
In this work, we propose to detect OOD molecules by adopting an auxiliary diffusion model-based framework, which compares similarities between input molecules and reconstructed graphs.
FedGreen: Carbon-aware Federated Learning with Model Size Adaptation
Federated learning (FL) provides a promising collaborative framework to build a model from distributed clients, and this work investigates the carbon emission of the FL process.
Mechanisms promoting biodiversity in ecosystems
Explaining biodiversity is a central focus in theoretical ecology.
RealTCD: Temporal Causal Discovery from Interventional Data with Large Language Model
In the field of Artificial Intelligence for Information Technology Operations, causal discovery is pivotal for operation and maintenance of graph construction, facilitating downstream industrial tasks such as root cause analysis.
FLARE: A New Federated Learning Framework with Adjustable Learning Rates over Resource-Constrained Wireless Networks
Wireless federated learning (WFL) suffers from heterogeneity prevailing in the data distributions, computing powers, and channel conditions of participating devices.
ID-Animator: Zero-Shot Identity-Preserving Human Video Generation
In this study, we present \textbf{ID-Animator}, a zero-shot human-video generation approach that can perform personalized video generation given a single reference facial image without further training.
Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone
We introduce phi-3-mini, a 3. 8 billion parameter language model trained on 3. 3 trillion tokens, whose overall performance, as measured by both academic benchmarks and internal testing, rivals that of models such as Mixtral 8x7B and GPT-3. 5 (e. g., phi-3-mini achieves 69% on MMLU and 8. 38 on MT-bench), despite being small enough to be deployed on a phone.
Ranked #5 on
MMR total
on MRR-Benchmark
(using extra training data)
Texture-aware and Shape-guided Transformer for Sequential DeepFake Detection
In this paper, we propose a novel Texture-aware and Shape-guided Transformer to enhance detection performance.
Robust CLIP-Based Detector for Exposing Diffusion Model-Generated Images
Diffusion models (DMs) have revolutionized image generation, producing high-quality images with applications spanning various fields.
Self-Selected Attention Span for Accelerating Large Language Model Inference
We demonstrate that using this custom CUDA kernel improves the throughput of LLM inference by 28%.
Fast Diffeomorphic Image Registration using Patch based Fully Convolutional Networks
Diffeomorphic image registration is a fundamental step in medical image analysis, owing to its capability to ensure the invertibility of transformations and preservation of topology.
The VoicePrivacy 2024 Challenge Evaluation Plan
The task of the challenge is to develop a voice anonymization system for speech data which conceals the speaker's voice identity while protecting linguistic content and emotional states.
iMD4GC: Incomplete Multimodal Data Integration to Advance Precise Treatment Response Prediction and Survival Analysis for Gastric Cancer
The limited availability of modalities for each patient would cause information loss, adversely affecting predictive accuracy.
Qibo: A Large Language Model for Traditional Chinese Medicine
Large Language Models (LLMs) has made significant progress in a number of professional fields, including medicine, law, and finance.
Exploring the Potential of Large Language Models in Graph Generation
In this paper, we propose LLM4GraphGen to explore the ability of LLMs for graph generation with systematical task designs and extensive experiments.
When Do We Not Need Larger Vision Models?
Our results show that a multi-scale smaller model has comparable learning capacity to a larger model, and pre-training smaller models with S$^2$ can match or even exceed the advantage of larger models.
MIntRec2.0: A Large-scale Benchmark Dataset for Multimodal Intent Recognition and Out-of-scope Detection in Conversations
We believe that MIntRec2. 0 will serve as a valuable resource, providing a pioneering foundation for research in human-machine conversational interactions, and significantly facilitating related applications.
Robust Light-Weight Facial Affective Behavior Recognition with CLIP
Human affective behavior analysis aims to delve into human expressions and behaviors to deepen our understanding of human emotions.
Robust COVID-19 Detection in CT Images with CLIP
In the realm of medical imaging, particularly for COVID-19 detection, deep learning models face substantial challenges such as the necessity for extensive computational resources, the paucity of well-annotated datasets, and a significant amount of unlabeled data.
SVD-LLM: Truncation-aware Singular Value Decomposition for Large Language Model Compression
The advancements in Large Language Models (LLMs) have been hindered by their substantial sizes, which necessitate LLM compression methods for practical deployment.
Optimizing Latent Graph Representations of Surgical Scenes for Zero-Shot Domain Transfer
Purpose: Advances in deep learning have resulted in effective models for surgical video analysis; however, these models often fail to generalize across medical centers due to domain shift caused by variations in surgical workflow, camera setups, and patient demographics.
UAV-Enabled Asynchronous Federated Learning
To exploit unprecedented data generation in mobile edge networks, federated learning (FL) has emerged as a promising alternative to the conventional centralized machine learning (ML).
Spectral Invariant Learning for Dynamic Graphs under Distribution Shifts
In this paper, we discover that there exist cases with distribution shifts unobservable in the time domain while observable in the spectral domain, and propose to study distribution shifts on dynamic graphs in the spectral domain for the first time.
Unsupervised Graph Neural Architecture Search with Disentangled Self-supervision
To address the challenge, we propose a novel Disentangled Self-supervised Graph Neural Architecture Search (DSGAS) model, which is able to discover the optimal architectures capturing various latent graph factors in a self-supervised fashion based on unlabeled graph data.
MEIT: Multi-Modal Electrocardiogram Instruction Tuning on Large Language Models for Report Generation
Electrocardiogram (ECG) is the primary non-invasive diagnostic tool for monitoring cardiac conditions and is crucial in assisting clinicians.
Parameterized quantum comb and simpler circuits for reversing unknown qubit-unitary operations
Quantum comb is an essential tool for characterizing complex quantum protocols in quantum information processing.
Preserving Fairness Generalization in Deepfake Detection
The existing method for addressing this problem is providing a fair loss function.
Neural Radiance Fields in Medical Imaging: Challenges and Next Steps
Neural Radiance Fields (NeRF), as a pioneering technique in computer vision, offer great potential to revolutionize medical imaging by synthesizing three-dimensional representations from the projected two-dimensional image data.
Two-stage Cytopathological Image Synthesis for Augmenting Cervical Abnormality Screening
In the first Global Image Generation stage, a Normal Image Generator is designed to generate cytopathological images full of normal cervical cells.
HyCubE: Efficient Knowledge Hypergraph 3D Circular Convolutional Embedding
It is desirable and challenging for knowledge hypergraph embedding to reach a trade-off between model effectiveness and efficiency.
Rethinking Propagation for Unsupervised Graph Domain Adaptation
Motivated by our empirical analysis, we reevaluate the role of GNNs in graph domain adaptation and uncover the pivotal role of the propagation process in GNNs for adapting to different graph domains.
Failure Analysis in Next-Generation Critical Cellular Communication Infrastructures
The advent of communication technologies marks a transformative phase in critical infrastructure construction, where the meticulous analysis of failures becomes paramount in achieving the fundamental objectives of continuity, security, and availability.
Revisiting VAE for Unsupervised Time Series Anomaly Detection: A Frequency Perspective
To ensure an accurate AD, FCVAE exploits an innovative approach to concurrently integrate both the global and local frequency features into the condition of Conditional Variational Autoencoder (CVAE) to significantly increase the accuracy of reconstructing the normal data.
Artificial Intelligence in Image-based Cardiovascular Disease Analysis: A Comprehensive Survey and Future Outlook
Our review encompasses these modalities, giving a broad perspective on the diverse imaging techniques integrated with AI for CVD analysis.
Masked Conditional Diffusion Model for Enhancing Deepfake Detection
this paper present we put a new insight into diffusion model-based data augmentation, and propose a Masked Conditional Diffusion Model (MCDM) for enhancing deepfake detection.
Uncertainty-Aware Explainable Recommendation with Large Language Models
Providing explanations within the recommendation system would boost user satisfaction and foster trust, especially by elaborating on the reasons for selecting recommended items tailored to the user.
Active Generation Network of Human Skeleton for Action Recognition
To solve those problems, We propose a novel active generative network (AGN), which can adaptively learn various action categories by motion style transfer to generate new actions when the data for a particular action is only a single sample or few samples.
Detecting Multimedia Generated by Large AI Models: A Survey
The rapid advancement of Large AI Models (LAIMs), particularly diffusion models and large language models, has marked a new era where AI-generated multimedia is increasingly integrated into various aspects of daily life.
Efficient Image Super-Resolution via Symmetric Visual Attention Network
An important development direction in the Single-Image Super-Resolution (SISR) algorithms is to improve the efficiency of the algorithms.
Ranked #65 on
Image Super-Resolution
on Set14 - 4x upscaling
To deform or not: treatment-aware longitudinal registration for breast DCE-MRI during neoadjuvant chemotherapy via unsupervised keypoints detection
We use a clinical dataset with 1630 MRI scans from 314 patients treated with NAC.
TeleChat Technical Report
Subsequently, the model undergoes fine-tuning to align with human preferences, following a detailed methodology that we describe.
Bayesian Intrinsic Groupwise Image Registration: Unsupervised Disentanglement of Anatomy and Geometry
This article presents a general Bayesian learning framework for multi-modal groupwise registration on medical images.
IoT in the Era of Generative AI: Vision and Challenges
Advancements in Generative AI hold immense promise to push Internet of Things (IoT) to the next level.
Continuous Optical Zooming: A Benchmark for Arbitrary-Scale Image Super-Resolution in Real World
The extensive experiments demonstrate the superior performance of the arbitrary-scale SR models trained on the COZ dataset compared to models trained on simulated data.
Molecular Data Programming: Towards Molecule Pseudo-labeling with Systematic Weak Supervision
The premise for the great advancement of molecular machine learning is dependent on a considerable amount of labeled data.
Grounding-Prompter: Prompting LLM with Multimodal Information for Temporal Sentence Grounding in Long Videos
To tackle these challenges, in this work we propose a Grounding-Prompter method, which is capable of conducting TSG in long videos through prompting LLM with multimodal information.
PokeMQA: Programmable knowledge editing for Multi-hop Question Answering
Multi-hop question answering (MQA) is one of the challenging tasks to evaluate machine's comprehension and reasoning abilities, where large language models (LLMs) have widely achieved the human-comparable performance.
LLM4VG: Large Language Models Evaluation for Video Grounding
Recently, researchers have attempted to investigate the capability of LLMs in handling videos and proposed several video LLM models.
In2SET: Intra-Inter Similarity Exploiting Transformer for Dual-Camera Compressive Hyperspectral Imaging
Dual-Camera Compressed Hyperspectral Imaging (DCCHI) offers the capability to reconstruct 3D Hyperspectral Image (HSI) by fusing compressive and Panchromatic (PAN) image, which has shown great potential for snapshot hyperspectral imaging in practice.
ConvD: Attention Enhanced Dynamic Convolutional Embeddings for Knowledge Graph Completion
In this paper, we propose a novel dynamic convolutional embedding model ConvD for knowledge graph completion, which directly reshapes the relation embeddings into multiple internal convolution kernels to improve the external convolution kernels of the traditional convolutional embedding model.
Detection and Mitigation of Position Spoofing Attacks on Cooperative UAV Swarm Formations
Detecting spoofing attacks on the positions of unmanned aerial vehicles (UAVs) within a swarm is challenging.
Efficient Large Language Models: A Survey
We hope our survey can serve as a valuable resource to help researchers and practitioners gain a systematic understanding of efficient LLMs research and inspire them to contribute to this important and exciting field.
Virtual Quantum Markov Chains
In this work, we propose the concept of virtual quantum Markov chains (VQMCs), focusing on scenarios where subsystems retain classical information about global systems from measurement statistics.
VTimeLLM: Empower LLM to Grasp Video Moments
Large language models (LLMs) have shown remarkable text understanding capabilities, which have been extended as Video LLMs to handle video data for comprehending visual details.
Out-of-Distribution Generalized Dynamic Graph Neural Network for Human Albumin Prediction
We first model human albumin prediction as a dynamic graph regression problem to model the dynamics and patient relationship.
A Generic Stochastic Hybrid Car-following Model Based on Approximate Bayesian Computation
However, the CF behavior of human drivers is highly stochastic and nonlinear.
OFDMA-F$^2$L: Federated Learning With Flexible Aggregation Over an OFDMA Air Interface
Federated learning (FL) can suffer from a communication bottleneck when deployed in mobile networks, limiting participating clients and deterring FL convergence.
Out-of-Distribution Generalized Dynamic Graph Neural Network with Disentangled Intervention and Invariance Promotion
In this paper, we propose Disentangled Intervention-based Dynamic graph Attention networks with Invariance Promotion (I-DIDA) to handle spatio-temporal distribution shifts in dynamic graphs by discovering and utilizing invariant patterns, i. e., structures and features whose predictive abilities are stable across distribution shifts.
Self-organized biodiversity in biotic resource systems
What determines biodiversity in nature is a prominent issue in ecology, especially in biotic resource systems that are typically devoid of cross-feeding.
Adversarial Prompt Tuning for Vision-Language Models
With the rapid advancement of multimodal learning, pre-trained Vision-Language Models (VLMs) such as CLIP have demonstrated remarkable capacities in bridging the gap between visual and language modalities.
MeLo: Low-rank Adaptation is Better than Fine-tuning for Medical Image Diagnosis
By fixing the weight of ViT models and only adding small low-rank plug-ins, we achieve competitive results on various diagnosis tasks across different imaging modalities using only a few trainable parameters.
Uni-COAL: A Unified Framework for Cross-Modality Synthesis and Super-Resolution of MR Images
Cross-modality synthesis (CMS), super-resolution (SR), and their combination (CMSR) have been extensively studied for magnetic resonance imaging (MRI).
UMedNeRF: Uncertainty-aware Single View Volumetric Rendering for Medical Neural Radiance Fields
In the field of clinical medicine, computed tomography (CT) is an effective medical imaging modality for the diagnosis of various pathologies.
Post-training Quantization for Text-to-Image Diffusion Models with Progressive Calibration and Activation Relaxing
In this paper, we propose a novel post-training quantization method PCR (Progressive Calibration and Relaxing) for text-to-image diffusion models, which consists of a progressive calibration strategy that considers the accumulated quantization error across timesteps, and an activation relaxing strategy that improves the performance with negligible cost.
Lightweight Diffusion Models with Distillation-Based Block Neural Architecture Search
When retraining the searched architecture, we adopt a dynamic joint loss to maintain the consistency between supernet training and subnet retraining, which also provides informative objectives for each block and shortens the paths of gradient propagation.
3D Pose Estimation of Tomato Peduncle Nodes using Deep Keypoint Detection and Point Cloud
A 21 comprehensive evaluation was conducted in a commercial greenhouse to gain insight into the 22 performance of different parts of the method.
Dissecting the Runtime Performance of the Training, Fine-tuning, and Inference of Large Language Models
For end users, our benchmark and findings help better understand different optimization techniques, training and inference frameworks, together with hardware platforms in choosing configurations for deploying LLMs.
VideoDreamer: Customized Multi-Subject Text-to-Video Generation with Disen-Mix Finetuning
The video generator is further customized for the given multiple subjects by the proposed Disen-Mix Finetuning and Human-in-the-Loop Re-finetuning strategy, which can tackle the attribute binding problem of multi-subject generation.
A Systematic Review for Transformer-based Long-term Series Forecasting
Various variants have enabled transformer architecture to effectively handle long-term time series forecasting (LTSF) tasks.
Towards Generalized Multi-stage Clustering: Multi-view Self-distillation
MVC aims at exploring common semantics and pseudo-labels from multiple views and clustering in a self-supervised manner.
Hierarchical Mutual Information Analysis: Towards Multi-view Clustering in The Wild
Multi-view clustering (MVC) can explore common semantics from unsupervised views generated by different sources, and thus has been extensively used in applications of practical computer vision.
Disentangled Representation Learning with Large Language Models for Text-Attributed Graphs
Text-attributed graphs (TAGs) are prevalent on the web and research over TAGs such as citation networks, e-commerce networks and social networks has attracted considerable attention in the web community.
LLM4DyG: Can Large Language Models Solve Spatial-Temporal Problems on Dynamic Graphs?
Our main observations are: 1) LLMs have preliminary spatial-temporal understanding abilities on dynamic graphs, 2) Dynamic graph tasks show increasing difficulties for LLMs as the graph size and density increase, while not sensitive to the time span and data generation mechanism, 3) the proposed DST2 prompting method can help to improve LLMs' spatial-temporal understanding abilities on dynamic graphs for most tasks.
Self-triggered Consensus Control of Multi-agent Systems from Data
In the presence of external disturbances, a model-based STC scheme is put forth for $\mathcal{H}_{\infty}$-consensus of MASs, serving as a baseline for the data-driven STC.
Provable Advantage of Parameterized Quantum Circuit in Function Approximation
To achieve this, we utilize techniques from quantum signal processing and linear combinations of unitaries to construct PQCs that implement multivariate polynomials.
Decentralized Federated Learning via MIMO Over-the-Air Computation: Consensus Analysis and Performance Optimization
We conduct a general convergence analysis to quantitatively capture the influence of aggregation weight and communication error on the MIMO OA-DFL performance in \emph{ad hoc} networks.
RAC-BERT: Character Radical Enhanced BERT for Ancient Chinese
The results show that our model significantly outperforms the state-of-the-art models on most tasks.