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Found 16 papers, 5 papers with code
dFLMoE: Decentralized Federated Learning via Mixture of Experts for Medical Data Analysis
This centralized approach would integrate the knowledge from each client into a centralized server, and the knowledge would be already undermined during the centralized integration before it reaches back to each client.
Neural Finite-State Machines for Surgical Phase Recognition
Surgical phase recognition (SPR) is crucial for applications in workflow optimization, performance evaluation, and real-time intervention guidance.
DaRePlane: Direction-aware Representations for Dynamic Scene Reconstruction
Numerous recent approaches to modeling and re-rendering dynamic scenes leverage plane-based explicit representations, addressing slow training times associated with models like neural radiance fields (NeRF) and Gaussian splatting (GS).
Divide and Fuse: Body Part Mesh Recovery from Partially Visible Human Images
Traditional top-down methods, relying on whole-body parametric models like SMPL, falter when only a small part of the human is visible, as they require visibility of most of the human body for accurate mesh reconstruction.
AnatoMask: Enhancing Medical Image Segmentation with Reconstruction-guided Self-masking
Due to the scarcity of labeled data, self-supervised learning (SSL) has gained much attention in 3D medical image segmentation, by extracting semantic representations from unlabeled data.
MH-pFLGB: Model Heterogeneous personalized Federated Learning via Global Bypass for Medical Image Analysis
In the evolving application of medical artificial intelligence, federated learning is notable for its ability to protect training data privacy.
pFLFE: Cross-silo Personalized Federated Learning via Feature Enhancement on Medical Image Segmentation
In medical image segmentation, personalized cross-silo federated learning (FL) is becoming popular for utilizing varied data across healthcare settings to overcome data scarcity and privacy concerns.
MH-pFLID: Model Heterogeneous personalized Federated Learning via Injection and Distillation for Medical Data Analysis
Federated learning is widely used in medical applications for training global models without needing local data access.
FSC: Few-point Shape Completion
While previous studies have demonstrated successful 3D object shape completion with a sufficient number of points, they often fail in scenarios when a few points, e. g. tens of points, are observed.
DaReNeRF: Direction-aware Representation for Dynamic Scenes
However, the straightforward decomposition of 4D dynamic scenes into multiple 2D plane-based representations proves insufficient for re-rendering high-fidelity scenes with complex motions.
Spectrum AUC Difference (SAUCD): Human-aligned 3D Shape Evaluation
Then, we calculate the Area Under the Curve (AUC) difference between the two spectrums, so that each frequency band that captures either the overall or detailed shape is equitably considered.
Towards Generic Image Manipulation Detection with Weakly-Supervised Self-Consistency Learning
To improve the generalization ability, we propose weakly-supervised self-consistency learning (WSCL) to leverage the weakly annotated images.
Language-guided Human Motion Synthesis with Atomic Actions
In this paper, we propose ATOM (ATomic mOtion Modeling) to mitigate this problem, by decomposing actions into atomic actions, and employing a curriculum learning strategy to learn atomic action composition.
High Fidelity 3D Hand Shape Reconstruction via Scalable Graph Frequency Decomposition
To capture high-frequency personalized details, we transform the 3D mesh into the frequency domain, and propose a novel frequency decomposition loss to supervise each frequency component.
Learning Dynamical Human-Joint Affinity for 3D Pose Estimation in Videos
Graph Convolution Network (GCN) has been successfully used for 3D human pose estimation in videos.
Ranked #11 on
3D Human Pose Estimation
on HumanEva-I
PC-HMR: Pose Calibration for 3D Human Mesh Recovery from 2D Images/Videos
By coupling advanced 3D pose estimators and HMR in a serial or parallel manner, these two frameworks can effectively correct human mesh with guidance of a concise pose calibration module.
Ranked #4 on
3D Human Pose Estimation
on Surreal
