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Found 20 papers, 7 papers with code
Point Tracking in Surgery--The 2024 Surgical Tattoos in Infrared (STIR) Challenge
Understanding tissue motion in surgery is crucial to enable applications in downstream tasks such as segmentation, 3D reconstruction, virtual tissue landmarking, autonomous probe-based scanning, and subtask autonomy.
Usefulness of LLMs as an Author Checklist Assistant for Scientific Papers: NeurIPS'24 Experiment
Survey responses and analysis of re-submissions indicate that authors made substantive revisions to their submissions in response to specific feedback from the LLM.
FAIR Universe HiggsML Uncertainty Challenge Competition
The FAIR Universe -- HiggsML Uncertainty Challenge focuses on measuring the physics properties of elementary particles with imperfect simulators due to differences in modelling systematic errors.
SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge
Surgical data science has seen rapid advancement due to the excellent performance of end-to-end deep neural networks (DNNs) for surgical video analysis.
Depth Estimation using Weighted-loss and Transfer Learning
The optimized loss function is a combination of weighted losses to which enhance robustness and generalization: Mean Absolute Error (MAE), Edge Loss and Structural Similarity Index (SSIM).
Beyond the Known: Adversarial Autoencoders in Novelty Detection
The first is that we compute the novelty probability by linearizing the manifold that holds the structure of the inlier distribution.
Synthesizing CTA Image Data for Type-B Aortic Dissection using Stable Diffusion Models
It has been shown that Cardiac CTA images can be successfully generated using using Text to Image (T2I) stable diffusion model.
Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification
We introduce Meta-Album, an image classification meta-dataset designed to facilitate few-shot learning, transfer learning, meta-learning, among other tasks.
NeurIPS'22 Cross-Domain MetaDL competition: Design and baseline results
We present the design and baseline results for a new challenge in the ChaLearn meta-learning series, accepted at NeurIPS'22, focusing on "cross-domain" meta-learning.
Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification
Although deep neural networks are capable of achieving performance superior to humans on various tasks, they are notorious for requiring large amounts of data and computing resources, restricting their success to domains where such resources are available.
Neural Network Architectures for Location Estimation in the Internet of Things
Artificial Intelligence (AI) solutions for wireless location estimation are likely to prevail in many real-world scenarios.
Survey on Reliable Deep Learning-Based Person Re-Identification Models: Are We There Yet?
Intelligent video-surveillance (IVS) is currently an active research field in computer vision and machine learning and provides useful tools for surveillance operators and forensic video investigators.
2018 Robotic Scene Segmentation Challenge
In 2015 we began a sub-challenge at the EndoVis workshop at MICCAI in Munich using endoscope images of ex-vivo tissue with automatically generated annotations from robot forward kinematics and instrument CAD models.
Graph Convolutional Networks: analysis, improvements and results
In this context, we enhanced two of the existing Graph convolutional network models by proposing four enhancements.
Automatic Emotion Recognition (AER) System based on Two-Level Ensemble of Lightweight Deep CNN Models
A DL model involves a large number of learnable parameters, and its training needs a large dataset of EEG signals, which is difficult to acquire for AER problem.
A Virtual Testbed for Critical Incident Investigation with Autonomous Remote Aerial Vehicle Surveying, Artificial Intelligence, and Decision Support
Autonomous robotics and artificial intelligence techniques can be used to support human personnel in the event of critical incidents.
Survey on Deep Learning Techniques for Person Re-Identification Task
To this aim, many techniques have been proposed to increase the performance of PReID.
A Virtual Environment with Multi-Robot Navigation, Analytics, and Decision Support for Critical Incident Investigation
Because of the rare and high-risk nature of these events, realistic simulations can support the development and evaluation of AI-based tools.
An Automated System for Epilepsy Detection using EEG Brain Signals based on Deep Learning Approach
Epilepsy is a neurological disorder and for its detection, encephalography (EEG) is a commonly used clinical approach.
About Pyramid Structure in Convolutional Neural Networks
However, their model designing still requires attention to reduce number of learnable parameters, with no meaningful reduction in performance.
