Search Results for author:
Found 435 papers, 169 papers with code
Enhanced Multi-Channel Graph Convolutional Network for Aspect Sentiment Triplet Extraction
Specifically, we first define ten types of relations for ASTE task, and then adopt a biaffine attention module to embed these relations as an adjacent tensor between words in a sentence.
Reinforced Counterfactual Data Augmentation for Dual Sentiment Classification
Data augmentation and adversarial perturbation approaches have recently achieved promising results in solving the over-fitting problem in many natural language processing (NLP) tasks including sentiment classification.
Enhanced Representation with Contrastive Loss for Long-Tail Query Classification in e-commerce
To solve the above problem, we propose a novel method that leverages an auxiliary module to enhance the representations of long-tail queries by taking advantage of reliable supervised information of variant frequent queries.
Empowering Embodied Visual Tracking with Visual Foundation Models and Offline RL
We evaluate our tracker on several high-fidelity environments with challenging situations, such as distraction and occlusion.
Scaling Multi-Camera 3D Object Detection through Weak-to-Strong Eliciting
Finally, for MC3D-Det joint training, the elaborate dataset merge strategy is designed to solve the problem of inconsistent camera numbers and camera parameters.
Concept-Attention Whitening for Interpretable Skin Lesion Diagnosis
In the former branch, we train the CNN with a CAW layer inserted to perform skin lesion diagnosis.
QMix: Quality-aware Learning with Mixed Noise for Robust Retinal Disease Diagnosis
Previous noise learning methods mainly considered noise arising from images being mislabeled, i. e. label noise, assuming that all mislabeled images are of high image quality.
MedIAnomaly: A comparative study of anomaly detection in medical images
Anomaly detection (AD) aims at detecting abnormal samples that deviate from the expected normal patterns.
Rethinking Self-training for Semi-supervised Landmark Detection: A Selection-free Approach
Self-training is a simple yet effective method for semi-supervised learning, during which pseudo-label selection plays an important role for handling confirmation bias.
Foundation Model for Advancing Healthcare: Challenges, Opportunities, and Future Directions
To answer these questions, a comprehensive and deep survey of the challenges, opportunities, and future directions of HFMs is presented in this survey.
RS-Mamba for Large Remote Sensing Image Dense Prediction
RSM is specifically designed to capture the global context of remote sensing images with linear complexity, facilitating the effective processing of large VHR images.
Ranked #1 on
Road Segmentation
on Massachusetts Roads Dataset
(F1 metric)
Building change detection for remote sensing images
Change Detection
+1
Cohort-Individual Cooperative Learning for Multimodal Cancer Survival Analysis
In this paper, we propose a Cohort-individual Cooperative Learning (CCL) framework to advance cancer survival analysis by collaborating knowledge decomposition and cohort guidance.
360+x: A Panoptic Multi-modal Scene Understanding Dataset
While many existing datasets focus on scene understanding from a certain perspective (e. g. egocentric or third-person views), our dataset offers a panoptic perspective (i. e. multiple viewpoints with multiple data modalities).
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.
Design as Desired: Utilizing Visual Question Answering for Multimodal Pre-training
In this work, we leverage descriptions in medical reports to design multi-granular question-answer pairs associated with different diseases, which assist the framework in pre-training without requiring extra annotations from experts.
Dia-LLaMA: Towards Large Language Model-driven CT Report Generation
Medical report generation has achieved remarkable advancements yet has still been faced with several challenges.
AC4: Algebraic Computation Checker for Circuit Constraints in ZKPs
ZKP systems have surged attention and held a fundamental role in contemporary cryptography.
Modeling the Label Distributions for Weakly-Supervised Semantic Segmentation
Based on the label distributions, we leverage the GMM to generate high-quality pseudo labels for more reliable supervision.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
Prompt-Guided Adaptive Model Transformation for Whole Slide Image Classification
To address this issue, we propose PAMT, a novel Prompt-guided Adaptive Model Transformation framework that enhances MIL classification performance by seamlessly adapting pre-trained models to the specific characteristics of histopathology data.
Advancing COVID-19 Detection in 3D CT Scans
To make a more accurate diagnosis of COVID-19, we propose a straightforward yet effective model.
Domain Adaptation Using Pseudo Labels for COVID-19 Detection
In response to the need for rapid and accurate COVID-19 diagnosis during the global pandemic, we present a two-stage framework that leverages pseudo labels for domain adaptation to enhance the detection of COVID-19 from CT scans.
Self-Supervised Video Desmoking for Laparoscopic Surgery
On the other hand, in order to enhance the desmoking performance, we further feed the valuable information from PS frame into models, where a masking strategy and a regularization term are presented to avoid trivial solutions.
Zippo: Zipping Color and Transparency Distributions into a Single Diffusion Model
Beyond the superiority of the text-to-image diffusion model in generating high-quality images, recent studies have attempted to uncover its potential for adapting the learned semantic knowledge to visual perception tasks.
3D Human Reconstruction in the Wild with Synthetic Data Using Generative Models
In this work, we show that synthetic data created by generative models is complementary to computer graphics (CG) rendered data for achieving remarkable generalization performance on diverse real-world scenes for 3D human pose and shape estimation (HPS).
CoLeCLIP: Open-Domain Continual Learning via Joint Task Prompt and Vocabulary Learning
Large pre-trained VLMs like CLIP have demonstrated superior zero-shot recognition ability, and a number of recent studies leverage this ability to mitigate catastrophic forgetting in CL, but they focus on closed-set CL in a single domain dataset.
Histo-Genomic Knowledge Distillation For Cancer Prognosis From Histopathology Whole Slide Images
G-HANet is expected to be explored as a useful tool by the research community to address the current bottleneck of insufficient histo-genomic data pairing in the context of cancer prognosis and precision oncology.
XCoOp: Explainable Prompt Learning for Computer-Aided Diagnosis via Concept-guided Context Optimization
Utilizing potent representations of the large vision-language models (VLMs) to accomplish various downstream tasks has attracted increasing attention.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+1
Rethinking Autoencoders for Medical Anomaly Detection from A Theoretical Perspective
To the best of our knowledge, this is the first effort to theoretically clarify the principles and design philosophy of AE for anomaly detection.
Iterative Online Image Synthesis via Diffusion Model for Imbalanced Classification
In this paper, we introduce an Iterative Online Image Synthesis (IOIS) framework to address the class imbalance problem in medical image classification.
MambaMIL: Enhancing Long Sequence Modeling with Sequence Reordering in Computational Pathology
Multiple Instance Learning (MIL) has emerged as a dominant paradigm to extract discriminative feature representations within Whole Slide Images (WSIs) in computational pathology.
Learning with Noisy Foundation Models
Foundation models are usually pre-trained on large-scale datasets and then adapted to downstream tasks through tuning.
Diffusion Models Trained with Large Data Are Transferable Visual Models
We show that, simply initializing image understanding models using a pre-trained UNet (or transformer) of diffusion models, it is possible to achieve remarkable transferable performance on fundamental vision perception tasks using a moderate amount of target data (even synthetic data only), including monocular depth, surface normal, image segmentation, matting, human pose estimation, among virtually many others.
GPT as Psychologist? Preliminary Evaluations for GPT-4V on Visual Affective Computing
In conclusion, this paper provides valuable insights into the potential applications and challenges of MLLMs in human-centric computing.
HistGen: Histopathology Report Generation via Local-Global Feature Encoding and Cross-modal Context Interaction
Histopathology serves as the gold standard in cancer diagnosis, with clinical reports being vital in interpreting and understanding this process, guiding cancer treatment and patient care.
$\text{R}^2$-Bench: Benchmarking the Robustness of Referring Perception Models under Perturbations
Referring perception, which aims at grounding visual objects with multimodal referring guidance, is essential for bridging the gap between humans, who provide instructions, and the environment where intelligent systems perceive.
MolNexTR: A Generalized Deep Learning Model for Molecular Image Recognition
In addition, it employs a series of novel augmentation algorithms to significantly enhance the robustness and performance of the model.
PI-AstroDeconv: A Physics-Informed Unsupervised Learning Method for Astronomical Image Deconvolution
In the imaging process of an astronomical telescope, the deconvolution of its beam or Point Spread Function (PSF) is a crucial task.
Boosting Box-supervised Instance Segmentation with Pseudo Depth
This innovative approach empowers the network to simultaneously predict masks and depth, enhancing its ability to capture nuanced depth-related information during the instance segmentation process.
Data-efficient Event Camera Pre-training via Disentangled Masked Modeling
Our pre-training overcomes the limitations of previous methods, which either sacrifice temporal information by converting event sequences into 2D images for utilizing pre-trained image models or directly employ paired image data for knowledge distillation to enhance the learning of event streams.
A Novel Approach to Industrial Defect Generation through Blended Latent Diffusion Model with Online Adaptation
Effectively addressing the challenge of industrial Anomaly Detection (AD) necessitates an ample supply of defective samples, a constraint often hindered by their scarcity in industrial contexts.
Anatomy-guided fiber trajectory distribution estimation for cranial nerves tractography
Diffusion MRI tractography is an important tool for identifying and analyzing the intracranial course of cranial nerves (CNs).
VoCo: A Simple-yet-Effective Volume Contrastive Learning Framework for 3D Medical Image Analysis
Through this pretext task, VoCo implicitly encodes the contextual position priors into model representations without the guidance of annotations, enabling us to effectively improve the performance of downstream tasks that require high-level semantics.
Structure Guided Large Language Model for SQL Generation
Existing models typically input queries and database schemas into the LLM and rely on the LLM to perform semantic-structure matching and generate structured SQL.
Knowledge-to-SQL: Enhancing SQL Generation with Data Expert LLM
Generating accurate SQL for user queries (text-to-SQL) is a long-standing problem since the generation of the SQL requires comprehending the query and database and retrieving the accurate data from the database accordingly.
Large Language Model Interaction Simulator for Cold-Start Item Recommendation
To address this challenge, we propose an LLM Interaction Simulator (LLM-InS) to model users' behavior patterns based on the content aspect.
Deep Rib Fracture Instance Segmentation and Classification from CT on the RibFrac Challenge
The resulting FracNet+ demonstrates competitive performance in rib fracture detection, which lays a foundation for further research and development in AI-assisted rib fracture detection and diagnosis.
Multi-Behavior Collaborative Filtering with Partial Order Graph Convolutional Networks
POG defines the partial order relation of multiple behaviors and models behavior combinations as weighted edges to merge separate behavior graphs into a joint POG.
Physics-Informed Neural Networks with Hard Linear Equality Constraints
The incorporation of physics into neural networks can improve generalization and data efficiency.
UniTSyn: A Large-Scale Dataset Capable of Enhancing the Prowess of Large Language Models for Program Testing
The remarkable capability of large language models (LLMs) in generating high-quality code has drawn increasing attention in the software testing community.
A General Framework for Learning from Weak Supervision
Weakly supervised learning generally faces challenges in applicability to various scenarios with diverse weak supervision and in scalability due to the complexity of existing algorithms, thereby hindering the practical deployment.
On Catastrophic Inheritance of Large Foundation Models
Large foundation models (LFMs) are claiming incredible performances.
ExtremeCast: Boosting Extreme Value Prediction for Global Weather Forecast
Data-driven weather forecast based on machine learning (ML) has experienced rapid development and demonstrated superior performance in the global medium-range forecast compared to traditional physics-based dynamical models.
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.
Time Series Supplier Allocation via Deep Black-Litterman Model
Time Series Supplier Allocation (TSSA) poses a complex NP-hard challenge, aimed at refining future order dispatching strategies to satisfy order demands with maximum supply efficiency fully.
Macro Graph Neural Networks for Online Billion-Scale Recommender Systems
Predicting Click-Through Rate (CTR) in billion-scale recommender systems poses a long-standing challenge for Graph Neural Networks (GNNs) due to the overwhelming computational complexity involved in aggregating billions of neighbors.
Deep Joint Source-Channel Coding for Efficient and Reliable Cross-Technology Communication
Cross-technology communication (CTC) is a promising technique that enables direct communications among incompatible wireless technologies without needing hardware modification.
Medical Image Debiasing by Learning Adaptive Agreement from a Biased Council
Specifically, the debiasing model is required to learn adaptive agreement with the biased council by agreeing on the correctly predicted samples and disagreeing on the wrongly predicted samples by the biased council.
Observation-Guided Meteorological Field Downscaling at Station Scale: A Benchmark and a New Method
In this paper, we extend meteorological downscaling to arbitrary scattered station scales, establish a brand new benchmark and dataset, and retrieve meteorological states at any given station location from a coarse-resolution meteorological field.
Codebook-enabled Generative End-to-end Semantic Communication Powered by Transformer
Codebook-based generative semantic communication attracts increasing attention, since only indices are required to be transmitted when the codebook is shared between transmitter and receiver.
SignVTCL: Multi-Modal Continuous Sign Language Recognition Enhanced by Visual-Textual Contrastive Learning
Sign language recognition (SLR) plays a vital role in facilitating communication for the hearing-impaired community.
MDGNN: Multi-Relational Dynamic Graph Neural Network for Comprehensive and Dynamic Stock Investment Prediction
The stock market is a crucial component of the financial system, but predicting the movement of stock prices is challenging due to the dynamic and intricate relations arising from various aspects such as economic indicators, financial reports, global news, and investor sentiment.
Distributed Task-Oriented Communication Networks with Multimodal Semantic Relay and Edge Intelligence
In this article, we present a novel framework, named distributed task-oriented communication networks (DTCN), based on recent advances in multimodal semantic transmission and edge intelligence.
Learning to detect cloud and snow in remote sensing images from noisy labels
This paper is the first to consider the impact of label noise on the detection of clouds and snow in remote sensing images.
MICA: Towards Explainable Skin Lesion Diagnosis via Multi-Level Image-Concept Alignment
Black-box deep learning approaches have showcased significant potential in the realm of medical image analysis.
BoNuS: Boundary Mining for Nuclei Segmentation with Partial Point Labels
To alleviate this problem, in this paper, we propose a weakly-supervised nuclei segmentation method that only requires partial point labels of nuclei.
TAROT: A Hierarchical Framework with Multitask Co-Pretraining on Semi-Structured Data towards Effective Person-Job Fit
Person-job fit is an essential part of online recruitment platforms in serving various downstream applications like Job Search and Candidate Recommendation.
RHOBIN Challenge: Reconstruction of Human Object Interaction
Modeling the interaction between humans and objects has been an emerging research direction in recent years.
DeepPhysiNet: Bridging Deep Learning and Atmospheric Physics for Accurate and Continuous Weather Modeling
Next, we construct hyper-networks based on deep learning methods to directly learn weather patterns from a large amount of meteorological data.
Prototypical Information Bottlenecking and Disentangling for Multimodal Cancer Survival Prediction
Despite advantages of multimodal learning for cancer survival prediction, massive redundancy in multimodal data prevents it from extracting discriminative and compact information: (1) An extensive amount of intra-modal task-unrelated information blurs discriminability, especially for gigapixel whole slide images (WSIs) with many patches in pathology and thousands of pathways in genomic data, leading to an ``intra-modal redundancy" issue.
MOC-RVQ: Multilevel Codebook-assisted Digital Generative Semantic Communication
Vector quantization-based image semantic communication systems have successfully boosted transmission efficiency, but face a challenge with conflicting requirements between codebook design and digital constellation modulation.
Distance Guided Generative Adversarial Network for Explainable Binary Classifications
Experimental results show that DisGAN consistently outperforms the GAN-based augmentation methods with explainable binary classification.
Multimodal Sentiment Analysis with Missing Modality: A Knowledge-Transfer Approach
Multimodal sentiment analysis aims to identify the emotions expressed by individuals through visual, language, and acoustic cues.
Mobility and Cost Aware Inference Accelerating Algorithm for Edge Intelligence
The model segmentation without user mobility has been investigated deeply by previous works.
PDiT: Interleaving Perception and Decision-making Transformers for Deep Reinforcement Learning
Designing better deep networks and better reinforcement learning (RL) algorithms are both important for deep RL.
Mutual Information as Intrinsic Reward of Reinforcement Learning Agents for On-demand Ride Pooling
The emergence of on-demand ride pooling services allows each vehicle to serve multiple passengers at a time, thus increasing drivers' income and enabling passengers to travel at lower prices than taxi/car on-demand services (only one passenger can be assigned to a car at a time like UberX and Lyft).
Time Travelling Pixels: Bitemporal Features Integration with Foundation Model for Remote Sensing Image Change Detection
Change detection, a prominent research area in remote sensing, is pivotal in observing and analyzing surface transformations.
Ranked #6 on
Change Detection
on LEVIR-CD
Professional Network Matters: Connections Empower Person-Job Fit
Online recruitment platforms typically employ Person-Job Fit models in the core service that automatically match suitable job seekers with appropriate job positions.
Towards an end-to-end artificial intelligence driven global weather forecasting system
Initial states are typically generated by traditional data assimilation components, which are computational expensive and time-consuming.
Model-Free Change Point Detection for Mixing Processes
In particular, we provide performance guarantees for the MMD-CUSUM test under $\alpha$, $\beta$, and $\phi$-mixing processes, which significantly expands its utility beyond the i. i. d.
PromptBench: A Unified Library for Evaluation of Large Language Models
The evaluation of large language models (LLMs) is crucial to assess their performance and mitigate potential security risks.
KnowGPT: Knowledge Injection for Large Language Models
Generative Large Language Models (LLMs), such as ChatGPT, offer interactive APIs that can answer common questions at a human-expert level.
Reinforcement Neighborhood Selection for Unsupervised Graph Anomaly Detection
To address these issues, we utilize the advantages of reinforcement learning in adaptively learning in complex environments and propose a novel method that incorporates Reinforcement neighborhood selection for unsupervised graph ANomaly Detection (RAND).
Shapley Values-enabled Progressive Pseudo Bag Augmentation for Whole Slide Image Classification
While most of the conventional MIL methods use attention scores to estimate instance importance scores (IIS) which contribute to the prediction of the slide labels, these often lead to skewed attention distributions and inaccuracies in identifying crucial instances.
GenDeF: Learning Generative Deformation Field for Video Generation
We offer a new perspective on approaching the task of video generation.
Rethinking Multiple Instance Learning for Whole Slide Image Classification: A Bag-Level Classifier is a Good Instance-Level Teacher
Based on this idea, we design Iteratively Coupled Multiple Instance Learning (ICMIL) to couple the embedder and the bag classifier at a low cost.
Learning for Semantic Knowledge Base-Guided Online Feature Transmission in Dynamic Channels
In this context, we address the problem of efficient remote object recognition by optimizing feature transmission between mobile devices and edge servers.
HumanRecon: Neural Reconstruction of Dynamic Human Using Geometric Cues and Physical Priors
These priors ensure the color rendered along rays to be robust to view direction and reduce the inherent ambiguities of density estimated along rays.
Hybrid Precoding and Combining for mmWave Full-Duplex Joint Radar and Communication Systems under Self-Interference
Specifically, we introduce a generalized eigenvalue-based precoder design that considers the downlink user rate, the radar gain, and the SI suppression.
A Parameterized Generative Adversarial Network Using Cyclic Projection for Explainable Medical Image Classification
Although current data augmentation methods are successful to alleviate the data insufficiency, conventional augmentation are primarily intra-domain while advanced generative adversarial networks (GANs) generate images remaining uncertain, particularly in small-scale datasets.
Advancing Transformer Architecture in Long-Context Large Language Models: A Comprehensive Survey
Transformer-based Large Language Models (LLMs) have been applied in diverse areas such as knowledge bases, human interfaces, and dynamic agents, and marking a stride towards achieving Artificial General Intelligence (AGI).
AutoStory: Generating Diverse Storytelling Images with Minimal Human Effort
We empirically find that sparse control conditions, such as bounding boxes, are suitable for layout planning, while dense control conditions, e. g., sketches and keypoints, are suitable for generating high-quality image content.
Knowledge Graph Construction in Power Distribution Networks
In this paper, we propose a method for knowledge graph construction in power distribution networks.
Alleviating Behavior Data Imbalance for Multi-Behavior Graph Collaborative Filtering
To this end, we propose IMGCF, a simple but effective model to alleviate behavior data imbalance for multi-behavior graph collaborative filtering.
Rethinking and Improving Multi-task Learning for End-to-end Speech Translation
Significant improvements in end-to-end speech translation (ST) have been achieved through the application of multi-task learning.
Channel Estimation and Training Design for Active RIS Aided Wireless Communications
To address this issue, we further investigate this ARIS-specific channel estimation problem and propose a least-square (LS) based channel estimator, whose performance can be further improved with the design on ARIS reflection patterns at the channel training phase.
Train Once, Get a Family: State-Adaptive Balances for Offline-to-Online Reinforcement Learning
Existing solutions tackle this problem by imposing a policy constraint on the policy improvement objective in both offline and online learning.
CompeteAI: Understanding the Competition Behaviors in Large Language Model-based Agents
Large language models (LLMs) have been widely used as agents to complete different tasks, such as personal assistance or event planning.
Customising General Large Language Models for Specialised Emotion Recognition Tasks
This indicates the strong transferability and feasibility of LLMs in the field of emotion recognition.
De novo protein design using geometric vector field networks
Most prior encoders rely on atom-wise features, such as angles and distances between atoms, which are not available in this context.
Object-aware Inversion and Reassembly for Image Editing
Subsequently, we propose an additional reassembly step to seamlessly integrate the respective editing results and the non-editing region to obtain the final edited image.
RGM: A Robust Generalizable Matching Model
Finding corresponding pixels within a pair of images is a fundamental computer vision task with various applications.
Towards Intelligent Network Management: Leveraging AI for Network Service Detection
As the complexity and scale of modern computer networks continue to increase, there has emerged an urgent need for precise traffic analysis, which plays a pivotal role in cutting-edge wireless connectivity technologies.
The Program Testing Ability of Large Language Models for Code
In this paper, we explore the ability of LLMs for testing programs/code.
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.
X-Transfer: A Transfer Learning-Based Framework for GAN-Generated Fake Image Detection
Generative adversarial networks (GANs) have remarkably advanced in diverse domains, especially image generation and editing.
Towards Domain-Specific Features Disentanglement for Domain Generalization
Distributional shift between domains poses great challenges to modern machine learning algorithms.
Hoeffding's Inequality for Markov Chains under Generalized Concentrability Condition
This paper studies Hoeffding's inequality for Markov chains under the generalized concentrability condition defined via integral probability metric (IPM).
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.
Unpaired Optical Coherence Tomography Angiography Image Super-Resolution via Frequency-Aware Inverse-Consistency GAN
For optical coherence tomography angiography (OCTA) images, a limited scanning rate leads to a trade-off between field-of-view (FOV) and imaging resolution.
Understanding and Mitigating the Label Noise in Pre-training on Downstream Tasks
This paper aims to understand the nature of noise in pre-training datasets and to mitigate its impact on downstream tasks.
Cross-Modal Translation and Alignment for Survival Analysis
With the rapid advances in high-throughput sequencing technologies, the focus of survival analysis has shifted from examining clinical indicators to incorporating genomic profiles with pathological images.
Enabling Quartile-based Estimated-Mean Gradient Aggregation As Baseline for Federated Image Classifications
Federated Learning (FL) has revolutionized how we train deep neural networks by enabling decentralized collaboration while safeguarding sensitive data and improving model performance.
Soft Merging: A Flexible and Robust Soft Model Merging Approach for Enhanced Neural Network Performance
This merging process not only enhances the model performance by converging to a better local optimum, but also minimizes computational costs, offering an efficient and explicit learning process integrated with stochastic gradient descent.
Multi-view Self-supervised Disentanglement for General Image Denoising
It is understandable as the model is designed to learn paired mapping (e. g. from a noisy image to its clean version).
Ranked #1 on
Denoising
on CBSD68 sigm75
Progressive Feature Adjustment for Semi-supervised Learning from Pretrained Models
As an effective way to alleviate the burden of data annotation, semi-supervised learning (SSL) provides an attractive solution due to its ability to leverage both labeled and unlabeled data to build a predictive model.
Code Representation Pre-training with Complements from Program Executions
The test cases are obtained with the assistance of a customized fuzzer and are only required during pre-training.
DARC: Distribution-Aware Re-Coloring Model for Generalizable Nucleus Segmentation
Second, we propose a new instance normalization method that is robust to the variation in foreground-background ratios.
Using Large Language Models to Automate Category and Trend Analysis of Scientific Articles: An Application in Ophthalmology
Moreover, the extendibility of the model to other scientific fields broadens its impact in facilitating research and trend analysis across diverse disciplines.
Unsupervised Domain Adaptation for Anatomical Landmark Detection
The framework leverages self-training and domain adversarial learning to address the domain gap during adaptation.
PromptMRG: Diagnosis-Driven Prompts for Medical Report Generation
To address these challenges, we propose diagnosis-driven prompts for medical report generation (PromptMRG), a novel framework that aims to improve the diagnostic accuracy of MRG with the guidance of diagnosis-aware prompts.
Diagnosing Infeasible Optimization Problems Using Large Language Models
Decision-making problems can be represented as mathematical optimization models, finding wide applications in fields such as economics, engineering and manufacturing, transportation, and health care.
In-Rack Test Tube Pose Estimation Using RGB-D Data
Accurate robotic manipulation of test tubes in biology and medical industries is becoming increasingly important to address workforce shortages and improve worker safety.
Karma: Adaptive Video Streaming via Causal Sequence Modeling
Unlike direct observation-to-action mapping, Karma recurrently maintains a multi-dimensional time series of observations, returns, and actions as input and employs causal sequence modeling via a decision transformer to determine the next action.
Interpretation on Multi-modal Visual Fusion
In this paper, we present an analytical framework and a novel metric to shed light on the interpretation of the multimodal vision community.
Better Zero-Shot Reasoning with Role-Play Prompting
This highlights its potential to augment the reasoning capabilities of LLMs.
When Monte-Carlo Dropout Meets Multi-Exit: Optimizing Bayesian Neural Networks on FPGA
Bayesian Neural Networks (BayesNNs) have demonstrated their capability of providing calibrated prediction for safety-critical applications such as medical imaging and autonomous driving.
Target before Shooting: Accurate Anomaly Detection and Localization under One Millisecond via Cascade Patch Retrieval
In this framework, the anomaly detection problem is solved via a cascade patch retrieval procedure that retrieves the nearest neighbors for each test image patch in a coarse-to-fine fashion.
Ranked #1 on
Supervised Anomaly Detection
on BTAD
SegPrompt: Boosting Open-world Segmentation via Category-level Prompt Learning
In this work, we propose a novel training mechanism termed SegPrompt that uses category information to improve the model's class-agnostic segmentation ability for both known and unknown categories.
DatasetDM: Synthesizing Data with Perception Annotations Using Diffusion Models
To showcase the power of the proposed approach, we generate datasets with rich dense pixel-wise labels for a wide range of downstream tasks, including semantic segmentation, instance segmentation, and depth estimation.
FrozenRecon: Pose-free 3D Scene Reconstruction with Frozen Depth Models
3D scene reconstruction is a long-standing vision task.
CTVIS: Consistent Training for Online Video Instance Segmentation
The discrimination of instance embeddings plays a vital role in associating instances across time for online video instance segmentation (VIS).
Ranked #2 on
Video Instance Segmentation
on Youtube-VIS 2022 Validation
(using extra training data)
Phase Matching for Out-of-Distribution Generalization
In this paper, we aim to clarify the relationships between Domain Generalization (DG) and the frequency components, and explore the spatial relationships of the phase spectrum.
Collaborative Graph Neural Networks for Attributed Network Embedding
Graph neural networks (GNNs) have shown prominent performance on attributed network embedding.
Improving Transferability of Adversarial Examples via Bayesian Attacks
Our ICLR work advocated for enhancing transferability in adversarial examples by incorporating a Bayesian formulation into model parameters, which effectively emulates the ensemble of infinitely many deep neural networks, while, in this paper, we introduce a novel extension by incorporating the Bayesian formulation into the model input as well, enabling the joint diversification of both the model input and model parameters.
Metric3D: Towards Zero-shot Metric 3D Prediction from A Single Image
State-of-the-art (SOTA) monocular metric depth estimation methods can only handle a single camera model and are unable to perform mixed-data training due to the metric ambiguity.
Ranked #18 on
Monocular Depth Estimation
on NYU-Depth V2
(using extra training data)
Image Captions are Natural Prompts for Text-to-Image Models
With the rapid development of Artificial Intelligence Generated Content (AIGC), it has become common practice in many learning tasks to train or fine-tune large models on synthetic data due to the data-scarcity and privacy leakage problems.
Dense Affinity Matching for Few-Shot Segmentation
Few-Shot Segmentation (FSS) aims to segment the novel class images with a few annotated samples.
Towards Generalizable Diabetic Retinopathy Grading in Unseen Domains
Diabetic Retinopathy (DR) is a common complication of diabetes and a leading cause of blindness worldwide.
A Survey on Evaluation of Large Language Models
Large language models (LLMs) are gaining increasing popularity in both academia and industry, owing to their unprecedented performance in various applications.
Low-Light Enhancement in the Frequency Domain
Hence, in this work, we propose a novel residual recurrent multi-wavelet convolutional neural network R2-MWCNN learned in the frequency domain that can simultaneously increase the image contrast and reduce noise signals well.
RSPrompter: Learning to Prompt for Remote Sensing Instance Segmentation based on Visual Foundation Model
We also propose several ongoing derivatives for instance segmentation tasks, drawing on recent advancements within the SAM community, and compare their performance with RSPrompter.
Deep Omni-supervised Learning for Rib Fracture Detection from Chest Radiology Images
To tackle this challenge, the literature on object detection has witnessed an increase of weakly-supervised and semi-supervised approaches, yet still lacks a unified framework that leverages various forms of fully-labeled, weakly-labeled, and unlabeled data.
Distributed Localization and Tracking Control for Nonholonomic Agents with Time-varying Bearing Formation
This paper studies the bearing-based time-varying formation control problem for unicycle-type agents without bearing rigidity conditions.
Uncertainty Quantification via Spatial-Temporal Tweedie Model for Zero-inflated and Long-tail Travel Demand Prediction
Understanding Origin-Destination (O-D) travel demand is crucial for transportation management.
Advancing Volumetric Medical Image Segmentation via Global-Local Masked Autoencoder
Masked autoencoder (MAE) is a promising self-supervised pre-training technique that can improve the representation learning of a neural network without human intervention.
Multimodal Optimal Transport-based Co-Attention Transformer with Global Structure Consistency for Survival Prediction
Survival prediction is a complicated ordinal regression task that aims to predict the ranking risk of death, which generally benefits from the integration of histology and genomic data.
A Dynamic Feature Interaction Framework for Multi-task Visual Perception
In particular, one feature merge branch is designed for instance-level recognition the other for dense predictions.
PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization
To ensure the reliability of PandaLM, we collect a diverse human-annotated test dataset, where all contexts are generated by humans and labels are aligned with human preferences.
PromptBench: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts
The increasing reliance on Large Language Models (LLMs) across academia and industry necessitates a comprehensive understanding of their robustness to prompts.
Cross-Lingual Paraphrase Identification
Machine Translation
+5
Efficient Anomaly Detection with Budget Annotation Using Semi-Supervised Residual Transformer
Thus the sliding transformer can attain even higher accuracy with much less annotation labor.
Ranked #1 on
Anomaly Detection
on MVTec AD
(Segmentation AUROC metric)
Retrieval-Enhanced Visual Prompt Learning for Few-shot Classification
Prompt learning has become a popular approach for adapting large vision-language models, such as CLIP, to downstream tasks.
Few-Shot Speaker Identification Using Lightweight Prototypical Network with Feature Grouping and Interaction
In the proposed embedding module, audio feature of each speech sample is split into several low-dimensional feature subsets that are transformed by a recurrent convolutional block in parallel.
Medication Recommendation via Domain Knowledge Informed Deep Learning
However, most of them neglect incorporating domain knowledge according to the clinical manifestations in the EHR of the patient.
Machine learning with tree tensor networks, CP rank constraints, and tensor dropout
As suggested in [arXiv:2205. 15296] in the context of quantum many-body physics, computation costs can be further substantially reduced by imposing constraints on the canonical polyadic (CP) rank of the tensors in such networks.
Scale-aware Super-resolution Network with Dual Affinity Learning for Lesion Segmentation from Medical Images
To guide the segmentation branch to learn from richer high-resolution features, we propose a feature affinity module and a scale affinity module to enhance the multi-task learning of the dual branches.
Learning Conditional Attributes for Compositional Zero-Shot Learning
Compositional Zero-Shot Learning (CZSL) aims to train models to recognize novel compositional concepts based on learned concepts such as attribute-object combinations.
Ranked #1 on
Compositional Zero-Shot Learning
on MIT-States
LoRAPrune: Pruning Meets Low-Rank Parameter-Efficient Fine-Tuning
This is due to their utilization of unstructured pruning on LPMs, impeding the merging of LoRA weights, or their dependence on the gradients of pre-trained weights to guide pruning, which can impose significant memory overhead.
Continuous Cross-resolution Remote Sensing Image Change Detection
Toward continuous cross-resolution CD, we propose scale-invariant learning to enforce the model consistently predicting HR results given synthesized samples of varying resolution differences.
Understanding Programs by Exploiting (Fuzzing) Test Cases
The effectiveness of the proposed method is verified on two program understanding tasks including code clone detection and code classification, and it outperforms current state-of-the-arts by large margins.
Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching
In this work, we present Matcher, a novel perception paradigm that utilizes off-the-shelf vision foundation models to address various perception tasks.
Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations
In this paper, we introduce imprecise label learning (ILL), a framework for the unification of learning with various imprecise label configurations.
Ranked #1 on
Learning with noisy labels
on mini WebVision 1.0
Multi-channel Integrated Recommendation with Exposure Constraints
Integrated recommendation, which aims at jointly recommending heterogeneous items from different channels in a main feed, has been widely applied to various online platforms.
Multi-factor Sequential Re-ranking with Perception-Aware Diversification
Feed recommendation systems, which recommend a sequence of items for users to browse and interact with, have gained significant popularity in practical applications.
Maybe Only 0.5% Data is Needed: A Preliminary Exploration of Low Training Data Instruction Tuning
Instruction tuning for large language models (LLMs) has gained attention from researchers due to its ability to unlock the potential of LLMs in following instructions.
Diffusion Models for Imperceptible and Transferable Adversarial Attack
Many existing adversarial attacks generate $L_p$-norm perturbations on image RGB space.
Reference-based OCT Angiogram Super-resolution with Learnable Texture Generation
The key difference between the proposed method and traditional RefSR models is that the textures used during inference are generated by the LTG instead of being searched from a single reference image.
PromptRank: Unsupervised Keyphrase Extraction Using Prompt
This demonstrates the great potential of using prompt for unsupervised keyphrase extraction.
Ranked #1 on
Keyphrase Extraction
on NUS
Revolutionizing Agrifood Systems with Artificial Intelligence: A Survey
With the world population rapidly increasing, transforming our agrifood systems to be more productive, efficient, safe, and sustainable is crucial to mitigate potential food shortages.
Rethinking Boundary Detection in Deep Learning Models for Medical Image Segmentation
Medical image segmentation is a fundamental task in the community of medical image analysis.
Improving Adversarial Transferability via Intermediate-level Perturbation Decay
In particular, the proposed method, named intermediate-level perturbation decay (ILPD), encourages the intermediate-level perturbation to be in an effective adversarial direction and to possess a great magnitude simultaneously.
Harnessing the Power of Text-image Contrastive Models for Automatic Detection of Online Misinformation
As growing usage of social media websites in the recent decades, the amount of news articles spreading online rapidly, resulting in an unprecedented scale of potentially fraudulent information.
CrossFusion: Interleaving Cross-modal Complementation for Noise-resistant 3D Object Detection
The combination of LiDAR and camera modalities is proven to be necessary and typical for 3D object detection according to recent studies.
Learning to Fuse Monocular and Multi-view Cues for Multi-frame Depth Estimation in Dynamic Scenes
To let the geometric perception learned from multi-view cues in static areas propagate to the monocular representation in dynamic areas and let monocular cues enhance the representation of multi-view cost volume, we propose a cross-cue fusion (CCF) module, which includes the cross-cue attention (CCA) to encode the spatially non-local relative intra-relations from each source to enhance the representation of the other.
Scale Federated Learning for Label Set Mismatch in Medical Image Classification
Federated learning (FL) has been introduced to the healthcare domain as a decentralized learning paradigm that allows multiple parties to train a model collaboratively without privacy leakage.
The Second Monocular Depth Estimation Challenge
This paper discusses the results for the second edition of the Monocular Depth Estimation Challenge (MDEC).
Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions
Breast cancer has reached the highest incidence rate worldwide among all malignancies since 2020.
HNeRV: A Hybrid Neural Representation for Videos
Such embedding largely limits the regression capacity and internal generalization for video interpolation.
Ranked #3 on
Video Reconstruction
on UVG
DRAC: Diabetic Retinopathy Analysis Challenge with Ultra-Wide Optical Coherence Tomography Angiography Images
Computer-assisted automatic analysis of diabetic retinopathy (DR) is of great importance in reducing the risks of vision loss and even blindness.
Exploring Vision-Language Models for Imbalanced Learning
However, their performance on imbalanced dataset is relatively poor, where the distribution of classes in the training dataset is skewed, leading to poor performance in predicting minority classes.
Learning Robust Medical Image Segmentation from Multi-source Annotations
Learning segmentation networks from multi-source annotations remains a challenge due to the uncertainties brought by the variance of annotations and the quality of images.
Recover Triggered States: Protect Model Against Backdoor Attack in Reinforcement Learning
This paper proposes the Recovery Triggered States (RTS) method, a novel approach that effectively protects the victim agents from backdoor attacks.
Zero-Shot Video Editing Using Off-The-Shelf Image Diffusion Models
Our vid2vid-zero leverages off-the-shelf image diffusion models, and doesn't require training on any video.
Iteratively Coupled Multiple Instance Learning from Instance to Bag Classifier for Whole Slide Image Classification
In ICMIL, we use category information in the bag-level classifier to guide the patch-level fine-tuning of the patch feature extractor.
Image Quality-aware Diagnosis via Meta-knowledge Co-embedding
Medical images usually suffer from image degradation in clinical practice, leading to decreased performance of deep learning-based models.
DoNet: Deep De-overlapping Network for Cytology Instance Segmentation
Cell instance segmentation in cytology images has significant importance for biology analysis and cancer screening, while remains challenging due to 1) the extensive overlapping translucent cell clusters that cause the ambiguous boundaries, and 2) the confusion of mimics and debris as nuclei.
Adversarial Attack and Defense for Medical Image Analysis: Methods and Applications
In this exposition, we present a comprehensive survey on recent advances in adversarial attack and defense for medical image analysis with a novel taxonomy in terms of the application scenario.
Few Shot Medical Image Segmentation with Cross Attention Transformer
Our proposed network mines the correlations between the support image and query image, limiting them to focus only on useful foreground information and boosting the representation capacity of both the support prototype and query features.
Towards Scalable Neural Representation for Diverse Videos
Implicit neural representations (INR) have gained increasing attention in representing 3D scenes and images, and have been recently applied to encode videos (e. g., NeRV, E-NeRV).
Two-level Graph Network for Few-Shot Class-Incremental Learning
Class-level graph network aims to mitigate the semantic conflict between prototype features of new classes and old classes.
Boosting Convolution with Efficient MLP-Permutation for Volumetric Medical Image Segmentation
Recently, the advent of vision Transformer (ViT) has brought substantial advancements in 3D dataset benchmarks, particularly in 3D volumetric medical image segmentation (Vol-MedSeg).
Exploring Visual Prompts for Whole Slide Image Classification with Multiple Instance Learning
Multiple instance learning (MIL) has emerged as a popular method for classifying histopathology whole slide images (WSIs).
Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directions
Thus, label-efficient deep learning methods are developed to make comprehensive use of the labeled data as well as the abundance of unlabeled and weak-labeled data.
Implicit Ray-Transformers for Multi-view Remote Sensing Image Segmentation
In the first stage, we optimize a neural field to encode the color and 3D structure of the remote sensing scene based on multi-view images.
A Monkey Swing Counting Algorithm Based on Object Detection
This paper focuses on proposing a deep learning-based monkey swing counting algorithm.
Traj-MAE: Masked Autoencoders for Trajectory Prediction
Specifically, our Traj-MAE employs diverse masking strategies to pre-train the trajectory encoder and map encoder, allowing for the capture of social and temporal information among agents while leveraging the effect of environment from multiple granularities.
Multi-Content Interaction Network for Few-Shot Segmentation
Few-Shot Segmentation (FSS) is challenging for limited support images and large intra-class appearance discrepancies.
Knowledge Transfer via Multi-Head Feature Adaptation for Whole Slide Image Classification
Transferring prior knowledge from a source domain to the same or similar target domain can greatly enhance the performance of models on the target domain.
Parallel Computing Based Solution for Reliability-Constrained Distribution Network Planning
The main goal of distribution network (DN) expansion planning is essentially to achieve minimal investment constrained with specified reliability requirements.
On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective
In this paper, we conduct a thorough evaluation of the robustness of ChatGPT from the adversarial and out-of-distribution (OOD) perspective.
Hierarchical Cross-modal Transformer for RGB-D Salient Object Detection
Most of existing RGB-D salient object detection (SOD) methods follow the CNN-based paradigm, which is unable to model long-range dependencies across space and modalities due to the natural locality of CNNs.
