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Found 26 papers, 10 papers with code
A Large-scale Annotated Mechanical Components Benchmark for Classification and Retrieval Tasks with Deep Neural Networks
We introduce a large-scale annotated mechanical components benchmark for classification and retrieval tasks named MechanicalComponents Benchmark (MCB): a large-scale dataset of 3D objects of mechanical components.
2023 Low-Power Computer Vision Challenge (LPCVC) Summary
This article describes the 2023 IEEE Low-Power Computer Vision Challenge (LPCVC).
DecisionNCE: Embodied Multimodal Representations via Implicit Preference Learning
Multimodal pretraining has emerged as an effective strategy for the trinity of goals of representation learning in autonomous robots: 1) extracting both local and global task progression information; 2) enforcing temporal consistency of visual representation; 3) capturing trajectory-level language grounding.
Open RL Benchmark: Comprehensive Tracked Experiments for Reinforcement Learning
As a result, it is usually necessary to reproduce the experiments from scratch, which can be time-consuming and error-prone.
Evaluation of General Large Language Models in Contextually Assessing Semantic Concepts Extracted from Adult Critical Care Electronic Health Record Notes
Methods: We investigated the performance of three general LLMs in understanding and processing real-world clinical notes.
Reconsideration on evaluation of machine learning models in continuous monitoring using wearables
This paper explores the challenges in evaluating machine learning (ML) models for continuous health monitoring using wearable devices beyond conventional metrics.
Photoplethysmography based atrial fibrillation detection: an updated review from July 2019
This paper offers a comprehensive review of the latest advancements in PPG-based AF detection, utilizing digital health and artificial intelligence (AI) solutions, within the timeframe spanning from July 2019 to December 2022.
SiamAF: Learning Shared Information from ECG and PPG Signals for Robust Atrial Fibrillation Detection
Previous deep learning models learn from a single modality, either electrocardiogram (ECG) or photoplethysmography (PPG) signals.
A Self-Supervised Algorithm for Denoising Photoplethysmography Signals for Heart Rate Estimation from Wearables
Smart watches and other wearable devices are equipped with photoplethysmography (PPG) sensors for monitoring heart rate and other aspects of cardiovascular health.
Learned Kernels for Sparse, Interpretable, and Efficient Medical Time Series Processing
Results: Our interpretable method achieves greater than 99% of the performance of the state-of-the-art methods on the PPG artifact detection task, and even outperforms the state-of-the-art on a challenging out-of-distribution test set, while using dramatically fewer parameters (2% of the parameters of Segade, and about half of the parameters of Tiny-PPG).
Generative Job Recommendations with Large Language Model
The rapid development of online recruitment services has encouraged the utilization of recommender systems to streamline the job seeking process.
Query-Policy Misalignment in Preference-Based Reinforcement Learning
To unravel this mystery, we identify a long-neglected issue in the query selection schemes of existing PbRL studies: Query-Policy Misalignment.
PROTO: Iterative Policy Regularized Offline-to-Online Reinforcement Learning
Offline-to-online reinforcement learning (RL), by combining the benefits of offline pretraining and online finetuning, promises enhanced sample efficiency and policy performance.
Deep Joint Source-Channel Coding with Iterative Source Error Correction
In this paper, we propose an iterative source error correction (ISEC) decoding scheme for deep-learning-based joint source-channel coding (Deep JSCC).
Mind the Gap: Offline Policy Optimization for Imperfect Rewards
RGM is formulated as a bi-level optimization problem: the upper layer optimizes a reward correction term that performs visitation distribution matching w. r. t.
Learning From Alarms: A Robust Learning Approach for Accurate Photoplethysmography-Based Atrial Fibrillation Detection using Eight Million Samples Labeled with Imprecise Arrhythmia Alarms
To address this challenge, in this study, we propose to leverage AF alarms from bedside patient monitors to label concurrent PPG signals, resulting in the largest PPG-AF dataset so far (8. 5M 30-second records from 24100 patients) and demonstrating a practical approach to build large labeled PPG datasets.
Why Accuracy Is Not Enough: The Need for Consistency in Object Detection
Using this method, we show that the consistency of modern object detectors ranges from 83. 2% to 97. 1% on different video datasets from the Multiple Object Tracking Challenge.
Irrelevant Pixels are Everywhere: Find and Exclude Them for More Efficient Computer Vision
We observe that, given a computer vision task, images often contain pixels that are irrelevant to the task.
Efficient Computer Vision on Edge Devices with Pipeline-Parallel Hierarchical Neural Networks
We design a novel method that creates a parallel inference pipeline for computer vision problems that use hierarchical DNNs.
Log-Spectral Matching GAN: PPG-based Atrial Fibrillation Detection can be Enhanced by GAN-based Data Augmentation with Integration of Spectral Loss
Photoplethysmography (PPG) is a ubiquitous physiological measurement that detects beat-to-beat pulsatile blood volume changes and hence has a potential for monitoring cardiovascular conditions, particularly in ambulatory settings.
IFlyEA: A Chinese Essay Assessment System with Automated Rating, Review Generation, and Recommendation
IFlyEA provides multi-level and multi-dimension analytical modules for essay assessment.
Low-Power Multi-Camera Object Re-Identification using Hierarchical Neural Networks
At each node in the hierarchy, a small DNN identifies a different attribute of the query image.
Combining ResNet and Transformer for Chinese Grammatical Error Diagnosis
Grammatical error diagnosis is an important task in natural language processing.
Hetero-Center Loss for Cross-Modality Person Re-Identification
With the joint supervision of Cross-Entropy (CE) loss and HC loss, the network is trained to achieve two vital objectives, inter-class discrepancy and intra-class cross-modality similarity as much as possible.
Cross-Modality Person Re-identification
Person Re-Identification
Low-Power Computer Vision: Status, Challenges, Opportunities
In addition to mobile phones, many autonomous systems rely on visual data for making decisions and some of these systems have limited energy (such as unmanned aerial vehicles also called drones and mobile robots).
Neural Multitask Learning for Simile Recognition
Simile is a special type of metaphor, where comparators such as like and as are used to compare two objects.
