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Found 31 papers, 9 papers with code
Multi-Agent Reinforcement Learning for Decentralized Reservoir Management via Murmuration Intelligence
Conventional centralized water management systems face critical limitations from computational complexity and uncertainty propagation.
SHADE-AD: An LLM-Based Framework for Synthesizing Activity Data of Alzheimer's Patients
Alzheimer's Disease (AD) has become an increasingly critical global health concern, which necessitates effective monitoring solutions in smart health applications.
Patrol Security Game: Defending Against Adversary with Freedom in Attack Timing, Location, and Duration
Our objective is to devise a patrolling schedule with an infinite time horizon that minimizes the attacker's payoff.
A Unified Deep Semantic Expansion Framework for Domain-Generalized Person Re-identification
Leveraging on this analysis, we propose Unified Deep Semantic Expansion, our novel framework that unifies implicit and explicit semantic feature expansion techniques in a single framework to mitigate this early over-fitting and achieve a new state-of-the-art (SOTA) in all DG-ReID benchmarks.
Diverse Deep Feature Ensemble Learning for Omni-Domain Generalized Person Re-identification
To the best of our knowledge, our work is one of few to consider omni-domain generalization in Person ReID, and we advance the study of applying feature ensembles in Person ReID.
Aligned Divergent Pathways for Omni-Domain Generalized Person Re-Identification
Furthermore, given training data from the target domain, it should perform at least as well as state-of-the-art (SOTA) fully supervised Person ReID methods.
Person Re-Identification
Single-Source Domain Generalization
CtRNet-X: Camera-to-Robot Pose Estimation in Real-world Conditions Using a Single Camera
Camera-to-robot calibration is crucial for vision-based robot control and requires effort to make it accurate.
EyeCLIP: A visual-language foundation model for multi-modal ophthalmic image analysis
Early detection of eye diseases like glaucoma, macular degeneration, and diabetic retinopathy is crucial for preventing vision loss.
SensEmo: Enabling Affective Learning through Real-time Emotion Recognition with Smartwatches
More importantly, SensEmo assists students to achieve better online learning outcomes, e. g., an average of 40. 0% higher grades in quizzes, over the traditional learning without student emotional feedback.
Color Space Learning for Cross-Color Person Re-Identification
The primary color profile of the same identity is assumed to remain consistent in typical Person Re-identification (Person ReID) tasks.
HemoSet: The First Blood Segmentation Dataset for Automation of Hemostasis Management
The first step in automation of hemostasis management is detection of blood in the surgical field.
BASED: Bundle-Adjusting Surgical Endoscopic Dynamic Video Reconstruction using Neural Radiance Fields
We demonstrate this approach on endoscopic surgical scenes from robotic surgery.
SuPerPM: A Large Deformation-Robust Surgical Perception Framework Based on Deep Point Matching Learned from Physical Constrained Simulation Data
Thus, for tuning the learning model, we gather endoscopic data of soft tissue being manipulated by a surgical robot and then establish correspondences between point clouds at different time points to serve as ground truth.
AnyOKP: One-Shot and Instance-Aware Object Keypoint Extraction with Pretrained ViT
Towards flexible object-centric visual perception, we propose a one-shot instance-aware object keypoint (OKP) extraction approach, AnyOKP, which leverages the powerful representation ability of pretrained vision transformer (ViT), and can obtain keypoints on multiple object instances of arbitrary category after learning from a support image.
BAA-NGP: Bundle-Adjusting Accelerated Neural Graphics Primitives
Implicit neural representations have become pivotal in robotic perception, enabling robots to comprehend 3D environments from 2D images.
ORRN: An ODE-based Recursive Registration Network for Deformable Respiratory Motion Estimation with Lung 4DCT Images
Recent Deep Learning methods have shown promising accuracy and speedup for registering a pair of medical images.
An Ensemble Learning Approach for Exercise Detection in Type 1 Diabetes Patients
Artificial pancreas (AP) systems have been developed as a solution for type 1 diabetic patients to mimic the behavior of the pancreas and regulate blood glucose levels.
SemHint-MD: Learning from Noisy Semantic Labels for Self-Supervised Monocular Depth Estimation
Without ground truth supervision, self-supervised depth estimation can be trapped in a local minimum due to the gradient-locality issue of the photometric loss.
CarFi: Rider Localization Using Wi-Fi CSI
With the rise of hailing services, people are increasingly relying on shared mobility (e. g., Uber, Lyft) drivers to pick up for transportation.
Biomedical image analysis competitions: The state of current participation practice
Of these, 84% were based on standard architectures.
Semantic-SuPer: A Semantic-aware Surgical Perception Framework for Endoscopic Tissue Identification, Reconstruction, and Tracking
In this paper, we present a novel, comprehensive surgical perception framework, Semantic-SuPer, that integrates geometric and semantic information to facilitate data association, 3D reconstruction, and tracking of endoscopic scenes, benefiting downstream tasks like surgical navigation.
Adversarial Pairwise Reverse Attention for Camera Performance Imbalance in Person Re-identification: New Dataset and Metrics
Existing evaluation metrics for Person Re-Identification (Person ReID) models focus on system-wide performance.
Reducing Annotating Load: Active Learning with Synthetic Images in Surgical Instrument Segmentation
Motivated by alleviating this workload, we propose a general embeddable method to decrease the usage of labeled real images, using active generated synthetic images.
Multi-Domain Adversarial Feature Generalization for Person Re-Identification
To make Person Re-ID systems more practical and scalable, several cross-dataset domain adaptation methods have been proposed, which achieve high performance without the labeled data from the target domain.
Multi-frame Feature Aggregation for Real-time Instrument Segmentation in Endoscopic Video
However, the high computation cost may limit the application of deep models to time-sensitive tasks such as online surgical video analysis for robotic-assisted surgery.
LC-GAN: Image-to-image Translation Based on Generative Adversarial Network for Endoscopic Images
For live image segmentation, we first translate the live images to fake-cadaveric images with LC-GAN and then perform segmentation on the fake-cadaveric images with models trained on the real cadaveric dataset.
MPC-guided Imitation Learning of Neural Network Policies for the Artificial Pancreas
Even though model predictive control (MPC) is currently the main algorithm for insulin control in the artificial pancreas (AP), it usually requires complex online optimizations, which are infeasible for resource-constrained medical devices.
Towards Better Surgical Instrument Segmentation in Endoscopic Vision: Multi-Angle Feature Aggregation and Contour Supervision
Accurate and real-time surgical instrument segmentation is important in the endoscopic vision of robot-assisted surgery, and significant challenges are posed by frequent instrument-tissue contacts and continuous change of observation perspective.
Generalization Bounds for Convolutional Neural Networks
Convolutional neural networks (CNNs) have achieved breakthrough performances in a wide range of applications including image classification, semantic segmentation, and object detection.
Homogeneous Feature Transfer and Heterogeneous Location Fine-tuning for Cross-City Property Appraisal Framework
Most existing real estate appraisal methods focus on building accuracy and reliable models from a given dataset but pay little attention to the extensibility of their trained model.
Multi-task Mid-level Feature Alignment Network for Unsupervised Cross-Dataset Person Re-Identification
Most existing person re-identification (Re-ID) approaches follow a supervised learning framework, in which a large number of labelled matching pairs are required for training.
