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Found 52 papers, 16 papers with code
RetinaRegNet: A Versatile Approach for Retinal Image Registration
The model's effectiveness was demonstrated across three retinal image datasets: color fundus images, fluorescein angiography images, and laser speckle flowgraphy images.
Unleashing the Potential of SAM for Medical Adaptation via Hierarchical Decoding
This paper introduces H-SAM: a prompt-free adaptation of SAM tailored for efficient fine-tuning of medical images via a two-stage hierarchical decoding procedure.
LDTR: Transformer-based Lane Detection with Anchor-chain Representation
Despite recent advances in lane detection methods, scenarios with limited- or no-visual-clue of lanes due to factors such as lighting conditions and occlusion remain challenging and crucial for automated driving.
RSAM-Seg: A SAM-based Approach with Prior Knowledge Integration for Remote Sensing Image Semantic Segmentation
While the direct application of SAM to remote sensing image segmentation tasks does not yield satisfactory results, we propose RSAM-Seg, which stands for Remote Sensing SAM with Semantic Segmentation, as a tailored modification of SAM for the remote sensing field and eliminates the need for manual intervention to provide prompts.
Data-Centric Evolution in Autonomous Driving: A Comprehensive Survey of Big Data System, Data Mining, and Closed-Loop Technologies
There has been a huge bottleneck regarding the upper bound of autonomous driving algorithm performance, a consensus from academia and industry believes that the key to surmount the bottleneck lies in data-centric autonomous driving technology.
CIS-UNet: Multi-Class Segmentation of the Aorta in Computed Tomography Angiography via Context-Aware Shifted Window Self-Attention
Advancements in medical imaging and endovascular grafting have facilitated minimally invasive treatments for aortic diseases.
Unraveling Attacks in Machine Learning-based IoT Ecosystems: A Survey and the Open Libraries Behind Them
Through our comprehensive review and analysis, this paper seeks to contribute to the ongoing discourse on ML-based IoT security, offering valuable insights and practical solutions to secure ML models and data in the rapidly expanding field of artificial intelligence in IoT.
RecRanker: Instruction Tuning Large Language Model as Ranker for Top-k Recommendation
Large Language Models (LLMs) have demonstrated remarkable capabilities and have been extensively deployed across various domains, including recommender systems.
CLOMO: Counterfactual Logical Modification with Large Language Models
In this study, we delve into the realm of counterfactual reasoning capabilities of large language models (LLMs).
DistillCSE: Distilled Contrastive Learning for Sentence Embeddings
This paper proposes the DistillCSE framework, which performs contrastive learning under the self-training paradigm with knowledge distillation.
Consistency-guided Meta-Learning for Bootstrapping Semi-Supervised Medical Image Segmentation
Specifically, our approach first involves training a segmentation model on a small set of clean labeled images to generate initial labels for unlabeled data.
MicroSegNet: A Deep Learning Approach for Prostate Segmentation on Micro-Ultrasound Images
During the training process, MicroSegNet focuses more on regions that are hard to segment (hard regions), characterized by discrepancies between expert and non-expert annotations.
Image Registration of In Vivo Micro-Ultrasound and Ex Vivo Pseudo-Whole Mount Histopathology Images of the Prostate: A Proof-of-Concept Study
Our pipeline begins with the reconstruction of pseudo-whole-mount histopathology images and a 3-dimensional (3D) micro-US volume.
SimCSE++: Improving Contrastive Learning for Sentence Embeddings from Two Perspectives
This paper improves contrastive learning for sentence embeddings from two perspectives: handling dropout noise and addressing feature corruption.
Reconstruct Before Summarize: An Efficient Two-Step Framework for Condensing and Summarizing Meeting Transcripts
Meetings typically involve multiple participants and lengthy conversations, resulting in redundant and trivial content.
Traffic Forecasting on New Roads Using Spatial Contrastive Pre-Training (SCPT)
During inference, the spatial encoder only requires two days of traffic data on the new roads and does not require any re-training.
Message Passing Neural Networks for Traffic Forecasting
A road network, in the context of traffic forecasting, is typically modeled as a graph where the nodes are sensors that measure traffic metrics (such as speed) at that location.
CANet: Curved Guide Line Network with Adaptive Decoder for Lane Detection
A lane instance is first responded by the heat-map on the U-shaped curved guide line at global semantic level, thus the corresponding features of each lane are aggregated at the response point.
Ranked #1 on
Lane Detection
on CurveLanes
(Recall metric)
Because Every Sensor Is Unique, so Is Every Pair: Handling Dynamicity in Traffic Forecasting
Further analysis also shows that each pair of sensors also has a unique dynamic.
CLARE: Conservative Model-Based Reward Learning for Offline Inverse Reinforcement Learning
This work aims to tackle a major challenge in offline Inverse Reinforcement Learning (IRL), namely the reward extrapolation error, where the learned reward function may fail to explain the task correctly and misguide the agent in unseen environments due to the intrinsic covariate shift.
Multiple-level Point Embedding for Solving Human Trajectory Imputation with Prediction
Sparsity is a common issue in many trajectory datasets, including human mobility data.
Biomedical image analysis competitions: The state of current participation practice
Of these, 84% were based on standard architectures.
Mutual Information Learned Regressor: an Information-theoretic Viewpoint of Training Regression Systems
We first argue that the MSE minimization approach is equivalent to a conditional entropy learning problem, and then propose a mutual information learning formulation for solving regression problems by using a reparameterization technique.
Integrated Convolutional and Recurrent Neural Networks for Health Risk Prediction using Patient Journey Data with Many Missing Values
Our model can capture both long- and short-term temporal patterns within each patient journey and effectively handle the high degree of missingness in EHR data without any imputation data generation.
Mutual Information Learned Classifiers: an Information-theoretic Viewpoint of Training Deep Learning Classification Systems
Deep learning systems have been reported to acheive state-of-the-art performances in many applications, and one of the keys for achieving this is the existence of well trained classifiers on benchmark datasets which can be used as backbone feature extractors in downstream tasks.
Mutual Information Learned Classifiers: an Information-theoretic Viewpoint of Training Deep Learning Classification Systems
Deep learning systems have been reported to achieve state-of-the-art performances in many applications, and a key is the existence of well trained classifiers on benchmark datasets.
Compound Density Networks for Risk Prediction using Electronic Health Records
We propose an integrated end-to-end approach by utilizing a Compound Density Network (CDNet) that allows the imputation method and prediction model to be tuned together within a single framework.
How Robust is your Fair Model? Exploring the Robustness of Diverse Fairness Strategies
Recent studies have shown that robustness (the ability for a model to perform well on unseen data) plays a significant role in the type of strategy that should be used when approaching a new problem and, hence, measuring the robustness of these strategies has become a fundamental problem.
CoBEVT: Cooperative Bird's Eye View Semantic Segmentation with Sparse Transformers
The extensive experiments on the V2V perception dataset, OPV2V, demonstrate that CoBEVT achieves state-of-the-art performance for cooperative BEV semantic segmentation.
Towards Better Understanding with Uniformity and Explicit Regularization of Embeddings in Embedding-based Neural Topic Models
In this paper, we propose an embedding regularized neural topic model, which applies the specially designed training constraints on word embedding and topic embedding to reduce the optimization space of parameters.
Long-term Spatio-temporal Forecasting via Dynamic Multiple-Graph Attention
To address these issues, we construct new graph models to represent the contextual information of each node and the long-term spatio-temporal data dependency structure.
A Sentence is Worth 128 Pseudo Tokens: A Semantic-Aware Contrastive Learning Framework for Sentence Embeddings
Contrastive learning has shown great potential in unsupervised sentence embedding tasks, e. g., SimCSE.
Measuring disentangled generative spatio-temporal representation
Disentangled representation learning offers useful properties such as dimension reduction and interpretability, which are essential to modern deep learning approaches.
Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning
Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed.
Bridging the gap between prostate radiology and pathology through machine learning
Our experiments show that (1) radiologist labels and models trained with them can miss cancers, or underestimate cancer extent, (2) digital pathologist labels and models trained with them have high concordance with pathologist labels, and (3) models trained with digital pathologist labels achieve the best performance in prostate cancer detection in two different cohorts with different disease distributions, irrespective of the model architecture used.
Spatio-temporal Disentangled representation learning for mobility prediction
In this work, we propose a VAE-based architecture for learning the disentangled representation from real spatio-temporal data for mobility forecasting.
Weakly Supervised Registration of Prostate MRI and Histopathology Images
Cancer labels achieved by image registration can be used to improve radiologists' interpretation of MRI by training deep learning models for early detection of prostate cancer.
Geodesic Density Regression for Correcting 4DCT Pulmonary Respiratory Motion Artifacts
Correspondences are estimated by accounting for the local tissue density change associated with air entering and leaving the lungs, and using binary artifact masks to exclude regions with artifacts from image regression.
MoParkeR : Multi-objective Parking Recommendation
Existing parking recommendation solutions mainly focus on finding and suggesting parking spaces based on the unoccupied options only.
Predicting Flight Delay with Spatio-Temporal Trajectory Convolutional Network and Airport Situational Awareness Map
To model and forecast flight delays accurately, it is crucial to harness various vehicle trajectory and contextual sensor data on airport tarmac areas.
3D Registration of pre-surgical prostate MRI and histopathology images via super-resolution volume reconstruction
Improvement in cancer detection on MRI is essential to reducing this variability and maximizing the clinical utility of MRI.
Generative Adversarial Networks for Spatio-temporal Data: A Survey
Generative Adversarial Networks (GANs) have shown remarkable success in producing realistic-looking images in the computer vision area.
Low-Rank Reorganization via Proportional Hazards Non-negative Matrix Factorization Unveils Survival Associated Gene Clusters
Towards the goal of precision health and cancer treatments, the proposed algorithm can help understand and interpret high-dimensional heterogeneous genomics data with accurate identification of survival-associated gene clusters.
CorrSigNet: Learning CORRelated Prostate Cancer SIGnatures from Radiology and Pathology Images for Improved Computer Aided Diagnosis
First, the model learns MRI signatures of cancer that are correlated with corresponding histopathology features using Common Representation Learning.
G-CREWE: Graph CompREssion With Embedding for Network Alignment
In this paper, we propose a framework, called G-CREWE (Graph CompREssion With Embedding) to solve the network alignment problem.
FADACS: A Few-shot Adversarial Domain Adaptation Architecture for Context-Aware Parking Availability Sensing
We quantify the strength of our proposed framework in different cases and compare it to the existing data-driven approaches.
BiERU: Bidirectional Emotional Recurrent Unit for Conversational Sentiment Analysis
Sentiment analysis in conversations has gained increasing attention in recent years for the growing amount of applications it can serve, e. g., sentiment analysis, recommender systems, and human-robot interaction.
Ranked #39 on
Emotion Recognition in Conversation
on IEMOCAP
Adaptive Feature Selection Guided Deep Forest for COVID-19 Classification with Chest CT
We evaluated our proposed AFS-DF on COVID-19 dataset with 1495 patients of COVID-19 and 1027 patients of community acquired pneumonia (CAP).
Transfer Learning for Thermal Comfort Prediction in Multiple Cities
The main purpose of HVAC, maintaining appropriate thermal comfort, is crucial for the best utilisation of energy usage.
Towards Fair Cross-Domain Adaptation via Generative Learning
Domain Adaptation (DA) targets at adapting a model trained over the well-labeled source domain to the unlabeled target domain lying in different distributions.
COLTRANE: ConvolutiOnaL TRAjectory NEtwork for Deep Map Inference
This paper introduces COLTRANE, ConvolutiOnaL TRAjectory NEtwork, a novel deep map inference framework which operates on GPS trajectories collected in various environments.
Approximating Optimisation Solutions for Travelling Officer Problem with Customised Deep Learning Network
Deep learning has been extended to a number of new domains with critical success, though some traditional orienteering problems such as the Travelling Salesman Problem (TSP) and its variants are not commonly solved using such techniques.
