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Found 21 papers, 5 papers with code
SurgVLM: A Large Vision-Language Model and Systematic Evaluation Benchmark for Surgical Intelligence
Foundation models have achieved transformative success across biomedical domains by enabling holistic understanding of multimodal data.
Time Reversal Symmetry for Efficient Robotic Manipulations in Deep Reinforcement Learning
Symmetry is pervasive in robotics and has been widely exploited to improve sample efficiency in deep reinforcement learning (DRL).
Temporal Propagation of Asymmetric Feature Pyramid for Surgical Scene Segmentation
Surgical scene segmentation is crucial for robot-assisted laparoscopic surgery understanding.
Tracking-Aware Deformation Field Estimation for Non-rigid 3D Reconstruction in Robotic Surgeries
Then, the 2D deformation field is smoothly incorporated with a neural implicit reconstruction network to obtain tissue deformation in the 3D space.
ASAP: Learning Generalizable Online Bin Packing via Adaptive Selection After Pruning
To learn these policies, we propose a training scheme based on a meta-learning phase of both policies followed by a finetuning phase of the sole selection policy to rapidly adapt it to a test distribution.
Is Segment Anything Model 2 All You Need for Surgery Video Segmentation? A Systematic Evaluation
Surgery video segmentation is an important topic in the surgical AI field.
Surgical Scene Segmentation by Transformer With Asymmetric Feature Enhancement
Surgical scene segmentation is a fundamental task for robotic-assisted laparoscopic surgery understanding.
State-Novelty Guided Action Persistence in Deep Reinforcement Learning
While a powerful and promising approach, deep reinforcement learning (DRL) still suffers from sample inefficiency, which can be notably improved by resorting to more sophisticated techniques to address the exploration-exploitation dilemma.
Understanding and Reducing the Class-Dependent Effects of Data Augmentation with A Two-Player Game Approach
By rewriting this optimization problem as an adversarial two-player game, we propose a novel multiplicative weight algorithm, for which we prove the convergence.
INViT: A Generalizable Routing Problem Solver with Invariant Nested View Transformer
Recently, deep reinforcement learning has shown promising results for learning fast heuristics to solve routing problems.
Hypergraph-Transformer (HGT) for Interactive Event Prediction in Laparoscopic and Robotic Surgery
Understanding and anticipating intraoperative events and actions is critical for intraoperative assistance and decision-making during minimally invasive surgery.
Drive Anywhere: Generalizable End-to-end Autonomous Driving with Multi-modal Foundation Models
As autonomous driving technology matures, end-to-end methodologies have emerged as a leading strategy, promising seamless integration from perception to control via deep learning.
Infrastructure-based End-to-End Learning and Prevention of Driver Failure
FailureNet observes the poses of vehicles as they approach an intersection and detects whether a failure is present in the autonomy stack, warning cross-traffic of potentially dangerous drivers.
On the Forward Invariance of Neural ODEs
We propose a new method to ensure neural ordinary differential equations (ODEs) satisfy output specifications by using invariance set propagation.
Concept Graph Neural Networks for Surgical Video Understanding
We constantly integrate our knowledge and understanding of the world to enhance our interpretation of what we see.
SUPR-GAN: SUrgical PRediction GAN for Event Anticipation in Laparoscopic and Robotic Surgery
Comprehension of surgical workflow is the foundation upon which artificial intelligence (AI) and machine learning (ML) holds the potential to assist intraoperative decision-making and risk mitigation.
TransCenter: Transformers with Dense Representations for Multiple-Object Tracking
Methodologically, we propose the use of image-related dense detection queries and efficient sparse tracking queries produced by our carefully designed query learning networks (QLN).
Ranked #18 on
Multi-Object Tracking
on MOT20
(MOTA metric, using extra
training data)
Aggregating Long-Term Context for Learning Laparoscopic and Robot-Assisted Surgical Workflows
With the understanding of the complete surgical workflow, the robots are able to assist the surgeons in intra-operative events, such as by giving a warning when the surgeon is entering specific keys or high-risk phases.
How To Train Your Deep Multi-Object Tracker
In this paper, we bridge this gap by proposing a differentiable proxy of MOTA and MOTP, which we combine in a loss function suitable for end-to-end training of deep multi-object trackers.
Ranked #4 on
Multi-Object Tracking
on 2D MOT 2015
Variational Bayesian Inference for Audio-Visual Tracking of Multiple Speakers
We propose a variational inference model which amounts to approximate the joint distribution with a factorized distribution.
A Deep Network for Arousal-Valence Emotion Prediction with Acoustic-Visual Cues
In this paper, we comprehensively describe the methodology of our submissions to the One-Minute Gradual-Emotion Behavior Challenge 2018.
