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Found 39 papers, 10 papers with code
MARMOT: Masked Autoencoder for Modeling Transient Imaging
Our MARMOT is a self-supervised model pretrianed on massive and diverse NLOS transient datasets.
A New Segment Routing method with Swap Node Selection Strategy Based on Deep Reinforcement Learning for Software Defined Network
The existing segment routing (SR) methods need to determine the routing first and then use path segmentation approaches to select swap nodes to form a segment routing path (SRP).
TimeZero: Temporal Video Grounding with Reasoning-Guided LVLM
We introduce TimeZero, a reasoning-guided LVLM designed for the temporal video grounding (TVG) task.
TransiT: Transient Transformer for Non-line-of-sight Videography
High frame rates, for example, can be achieved by reducing either per-point scanning time or scanning density, but at the cost of lowering the information density at individual frames.
EquiBench: Benchmarking Large Language Models' Understanding of Program Semantics via Equivalence Checking
As large language models (LLMs) become integral to code-related tasks, a central question emerges: do LLMs truly understand program execution semantics?
Solving the long-tailed distribution problem by exploiting the synergies and balance of different techniques
While many studies have sought to improve long tail recognition by altering the data distribution in the feature space and adjusting model decision boundaries, research on the synergy and corrective approach among various methods is limited.
Surgical Visual Understanding (SurgVU) Dataset
Owing to recent advances in machine learning and the ability to harvest large amounts of data during robotic-assisted surgeries, surgical data science is ripe for foundational work.
Synthetic Data Generation for Residential Load Patterns via Recurrent GAN and Ensemble Method
Our developed ERGAN can capture diverse load patterns across various households, thereby enhancing the realism and diversity of the synthetic data generated.
Adaptive Resolution Inference (ARI): Energy-Efficient Machine Learning for Internet of Things
This enables most inferences to run with the reduced precision model and only a small fraction requires the full model, so significantly reducing computation and energy while not affecting model performance.
MiranDa: Mimicking the Learning Processes of Human Doctors to Achieve Causal Inference for Medication Recommendation
To enhance therapeutic outcomes from a pharmacological perspective, we propose MiranDa, designed for medication recommendation, which is the first actionable model capable of providing the estimated length of stay in hospitals (ELOS) as counterfactual outcomes that guide clinical practice and model training.
EgoNCE++: Do Egocentric Video-Language Models Really Understand Hand-Object Interactions?
Due to the occurrence of diverse EgoHOIs in the real world, we propose an open-vocabulary benchmark named EgoHOIBench to reveal the diminished performance of current egocentric video-language models (EgoVLM) on fined-grained concepts, indicating that these models still lack a full spectrum of egocentric understanding.
Movie101v2: Improved Movie Narration Benchmark
Automatic movie narration aims to generate 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.
Edit As You Wish: Video Caption Editing with Multi-grained User Control
In this paper, we propose a novel \textbf{V}ideo \textbf{C}aption \textbf{E}diting \textbf{(VCE)} task to automatically revise an existing video description guided by multi-grained user requests.
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)
