Search Results for author:
Found 25 papers, 3 papers with code
Gait Recognition from a Single Image using a Phase-Aware Gait Cycle Reconstruction Network
Specifically, a phase estimation network is introduced for the input single image, and the gait cycle reconstruction network exploits the estimated phase to mitigate the dependence of an encoded feature on the phase of that single image.
Towards Extreme Pruning of LLMs with Plug-and-Play Mixed Sparsity
Thus, we propose an efficient method based on the trace of Fisher Information Matrix (FIM) to quantitatively measure and verify the different sensitivities across layers.
SurgRIPE challenge: Benchmark of Surgical Robot Instrument Pose Estimation
Recently, more research has focused on the development of marker-less methods based on deep learning.
In-situ Self-optimization of Quantum Dot Emission for Lasers by Machine-Learning Assisted Epitaxy
In this work, we integrate in-situ reflection high-energy electron diffraction (RHEED) with machine learning (ML) to correlate the surface reconstruction with the photoluminescence (PL) of InAs/GaAs quantum dots (QDs), which serve as the active region of lasers.
SALINA: Towards Sustainable Live Sonar Analytics in Wild Ecosystems
Sonar radar captures visual representations of underwater objects and structures using sound wave reflections, making it essential for exploration, mapping, and continuous surveillance in wild ecosystems.
SemiEpi: Self-driving, Closed-loop Multi-Step Growth of Semiconductor Heterostructures Guided by Machine Learning
The semiconductor industry has prioritized automating repetitive tasks through closed-loop, self-driving experimentation, accelerating the optimization of complex multi-step processes.
MVMoE: Multi-Task Vehicle Routing Solver with Mixture-of-Experts
Learning to solve vehicle routing problems (VRPs) has garnered much attention.
Trustworthy Personalized Bayesian Federated Learning via Posterior Fine-Tune
Performance degradation owing to data heterogeneity and low output interpretability are the most significant challenges faced by federated learning in practical applications.
Out of Distribution (OOD) Detection
Personalized Federated Learning
Disordered hyperuniformity signals functioning and resilience of self-organized vegetation patterns
Here we show that disordered hyperuniformity as a striking class of hidden orders can exist in spatially self-organized vegetation landscapes.
A global product of fine-scale urban building height based on spaceborne lidar
Our results revealed that the estimated method of building height samples based on the GEDI data was effective with 0. 78 of Pearson's r and 3. 67 m of RMSE in comparison to the reference data.
Machine-Learning-Assisted and Real-Time-Feedback-Controlled Growth of InAs/GaAs Quantum Dots
Self-assembled InAs/GaAs quantum dots (QDs) have properties highly valuable for developing various optoelectronic devices such as QD lasers and single photon sources.
Regularizing disparity estimation via multi task learning with structured light reconstruction
Secondly, we \textcolor{black}{explore the use of a multi task learning (MTL) framework for the joint training of structured light and disparity.
Self-Supervised Depth Estimation in Laparoscopic Image using 3D Geometric Consistency
Depth estimation is a crucial step for image-guided intervention in robotic surgery and laparoscopic imaging system.
Attention over Self-attention:Intention-aware Re-ranking with Dynamic Transformer Encoders for Recommendation
We then introduce a dynamic transformer encoder (DTE) to capture user-specific inter-item relationships among item candidates by seamlessly accommodating the learned latent user intentions via IDM.
Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning
Priority dispatching rule (PDR) is widely used for solving real-world Job-shop scheduling problem (JSSP).
GLIMG: Global and Local Item Graphs for Top-N Recommender Systems
We argue that recommendation on global and local graphs outperforms that on a single global graph or multiple local graphs.
COMET: Convolutional Dimension Interaction for Collaborative Filtering
In this paper, we propose a novel representation learning-based model called COMET (COnvolutional diMEnsion inTeraction), which simultaneously models the high-order interaction patterns among historical interactions and embedding dimensions.
3D Human Shape Reconstruction from a Polarization Image
Inspired by the recent advances in human shape estimation from single color images, in this paper, we attempt at estimating human body shapes by leveraging the geometric cues from single polarization images.
Polarization Human Shape and Pose Dataset
Polarization images are known to be able to capture polarized reflected lights that preserve rich geometric cues of an object, which has motivated its recent applications in reconstructing detailed surface normal of the objects of interest.
Discovering Protagonist of Sentiment with Aspect Reconstructed Capsule Network
In this paper, we aim to improve ATSA by discovering the potential aspect terms of the predicted sentiment polarity when the aspect terms of a test sentence are unknown.
Multivariate Regression with Grossly Corrupted Observations: A Robust Approach and its Applications
The implementation of our approach and comparison methods as well as the involved datasets are made publicly available in support of the open-source and reproducible research initiatives.
Learning to Search on Manifolds for 3D Pose Estimation of Articulated Objects
This paper focuses on the challenging problem of 3D pose estimation of a diverse spectrum of articulated objects from single depth images.
Lie-X: Depth Image Based Articulated Object Pose Estimation, Tracking, and Action Recognition on Lie Groups
Pose estimation, tracking, and action recognition of articulated objects from depth images are important and challenging problems, which are normally considered separately.
Hand Action Detection from Ego-centric Depth Sequences with Error-correcting Hough Transform
Detecting hand actions from ego-centric depth sequences is a practically challenging problem, owing mostly to the complex and dexterous nature of hand articulations as well as non-stationary camera motion.
Mouse Pose Estimation From Depth Images
We focus on the challenging problem of efficient mouse 3D pose estimation based on static images, and especially single depth images.
