Search Results for author:
Found 28 papers, 5 papers with code
Movie101v2: Improved Movie Narration Benchmark
Automatic movie narration targets at creating video-aligned plot descriptions to assist visually impaired audiences.
Weak Convergence Analysis of Online Neural Actor-Critic Algorithms
Then, using a Poisson equation, we prove that the fluctuations of the model updates around the limit distribution due to the randomly-arriving data samples vanish as the number of parameter updates $\rightarrow \infty$.
An Efficient Sparse Inference Software Accelerator for Transformer-based Language Models on CPUs
We apply our sparse accelerator on widely-used Transformer-based language models including Bert-Mini, DistilBERT, Bert-Base, and BERT-Large.
Concurrent Classifier Error Detection (CCED) in Large Scale Machine Learning Systems
In this paper, we introduce Concurrent Classifier Error Detection (CCED), a scheme to implement CED in ML systems using a concurrent ML classifier to detect errors.
Movie101: A New Movie Understanding Benchmark
Closer to real scenarios, the Movie Clip Narrating (MCN) task in our benchmark asks models to generate role-aware narration paragraphs for complete movie clips where no actors are speaking.
Surgical tool classification and localization: results and methods from the MICCAI 2022 SurgToolLoc challenge
Unfortunately, obtaining the annotations needed to train machine learning models to identify and localize surgical tools is a difficult task.
Uncertainty-aware Self-supervised Learning for Cross-domain Technical Skill Assessment in Robot-assisted Surgery
In this paper, we propose a novel approach for skill assessment by transferring domain knowledge from labeled kinematic data to unlabeled data.
Automatic Detection of Out-of-body Frames in Surgical Videos for Privacy Protection Using Self-supervised Learning and Minimal Labels
Endoscopic video recordings are widely used in minimally invasive robot-assisted surgery, but when the endoscope is outside the patient's body, it can capture irrelevant segments that may contain sensitive information.
CholecTriplet2022: Show me a tool and tell me the triplet -- an endoscopic vision challenge for surgical action triplet detection
This paper presents the CholecTriplet2022 challenge, which extends surgical action triplet modeling from recognition to detection.
Ranked #1 on
Action Triplet Detection
on CholecT50 (Challenge)
Objective Surgical Skills Assessment and Tool Localization: Results from the MICCAI 2021 SimSurgSkill Challenge
Using this publicly available dataset and results as a springboard, future work may enable more efficient training of surgeons with advances in surgical data science.
A Forward Propagation Algorithm for Online Optimization of Nonlinear Stochastic Differential Equations
We then re-write the algorithm using the PDE solution, which allows us to characterize the parameter evolution around the direction of steepest descent.
Continuous-time stochastic gradient descent for optimizing over the stationary distribution of stochastic differential equations
The gradient estimate is simultaneously updated using forward propagation of the SDE state derivatives, asymptotically converging to the direction of steepest descent.
Surgical Visual Domain Adaptation: Results from the MICCAI 2020 SurgVisDom Challenge
Surgical data science is revolutionizing minimally invasive surgery by enabling context-aware applications.
SparseDNN: Fast Sparse Deep Learning Inference on CPUs
While we find mature support for quantized neural networks in production frameworks such as OpenVINO and MNN, support for pruned sparse neural networks is still lacking.
SparseRT: Accelerating Unstructured Sparsity on GPUs for Deep Learning Inference
In recent years, there has been a flurry of research in deep neural network pruning and compression.
Structured Pruning of Large Language Models
Large language models have recently achieved state of the art performance across a wide variety of natural language tasks.
Accelerated CNN Training Through Gradient Approximation
In this work, we explore three alternative methods to approximate gradients, with an efficient GPU kernel implementation for one of them.
Transferrable Operative Difficulty Assessment in Robot-assisted Teleoperation: A Domain Adaptation Approach
Providing an accurate and efficient assessment of operative difficulty is important for designing robot-assisted teleoperation interfaces that are easy and natural for human operators to use.
SATR-DL: Improving Surgical Skill Assessment and Task Recognition in Robot-assisted Surgery with Deep Neural Networks
Purpose: This paper focuses on an automated analysis of surgical motion profiles for objective skill assessment and task recognition in robot-assisted surgery.
Deep Learning with Convolutional Neural Network for Objective Skill Evaluation in Robot-assisted Surgery
We propose an analytical deep learning framework for skill assessment in surgical training.
A Generative Restricted Boltzmann Machine Based Method for High-Dimensional Motion Data Modeling
A learning method is then proposed to perform efficient learning for the proposed model.
A Hierarchical Probabilistic Model for Facial Feature Detection
Facial feature detection from facial images has attracted great attention in the field of computer vision.
Structured Feature Selection
Then we apply structured feature selection to two applications: 1) We introduce a new method that enables STMB to scale up and show the competitive performance of our algorithms on large-scale image classification tasks.
Classifier Learning With Hidden Information
Experimental results on different applications demonstrate the effectiveness of the proposed methods for exploiting hidden information and their superior performance to existing methods.
Capturing Complex Spatio-temporal Relations among Facial Muscles for Facial Expression Recognition
Spatial-temporal relations among facial muscles carry crucial information about facial expressions yet have not been thoroughly exploited.
Facial Expression Recognition
Facial Expression Recognition (FER)
