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Found 26 papers, 3 papers with code
Automating Catheterization Labs with Real-Time Perception
For decades, three-dimensional C-arm Cone-Beam Computed Tomography (CBCT) imaging system has been a critical component for complex vascular and nonvascular interventional procedures.
Self-supervised 3D Patient Modeling with Multi-modal Attentive Fusion
3D patient body modeling is critical to the success of automated patient positioning for smart medical scanning and operating rooms.
PBADet: A One-Stage Anchor-Free Approach for Part-Body Association
The detection of human parts (e. g., hands, face) and their correct association with individuals is an essential task, e. g., for ubiquitous human-machine interfaces and action recognition.
Implicit Modeling of Non-rigid Objects with Cross-Category Signals
To effectively capture the interrelation between these entities and ensure precise, collision-free representations, our approach facilitates signaling between category-specific fields to adequately rectify shapes.
IBAFormer: Intra-batch Attention Transformer for Domain Generalized Semantic Segmentation
Domain generalized semantic segmentation (DGSS) is a critical yet challenging task, where the model is trained only on source data without access to any target data.
CMDA: Cross-Modality Domain Adaptation for Nighttime Semantic Segmentation
However, limited by the low dynamic range of conventional cameras, images fail to capture structural details and boundary information in low-light conditions.
Model-based Programming: Redefining the Atomic Unit of Programming for the Deep Learning Era
This paper introduces and explores a new programming paradigm, Model-based Programming, designed to address the challenges inherent in applying deep learning models to real-world applications.
Disguise without Disruption: Utility-Preserving Face De-Identification
We extensively evaluate our method using multiple datasets, demonstrating a higher de-identification rate and superior consistency compared to prior approaches in various downstream tasks.
Exploring Cycle Consistency Learning in Interactive Volume Segmentation
Given a medical volume, a user first segments a slice (or several slices) via the interaction module and then propagates the segmentation(s) to the remaining slices.
Progressive Multi-view Human Mesh Recovery with Self-Supervision
To date, little attention has been given to multi-view 3D human mesh estimation, despite real-life applicability (e. g., motion capture, sport analysis) and robustness to single-view ambiguities.
MAPDP: Cooperative Multi-Agent Reinforcement Learning to Solve Pickup and Delivery Problems
It is of great importance to efficiently provide high-quality solutions of cooperative PDP.
Federated Learning with Privacy-Preserving Ensemble Attention Distillation
We propose a privacy-preserving FL framework leveraging unlabeled public data for one-way offline knowledge distillation in this work.
PREF: Predictability Regularized Neural Motion Fields
We propose to regularize the estimated motion to be predictable.
Self-supervised Human Mesh Recovery with Cross-Representation Alignment
However, on synthetic dense correspondence maps (i. e., IUV) few have been explored since the domain gap between synthetic training data and real testing data is hard to address for 2D dense representation.
PseudoClick: Interactive Image Segmentation with Click Imitation
The goal of click-based interactive image segmentation is to obtain precise object segmentation masks with limited user interaction, i. e., by a minimal number of user clicks.
SMPL-A: Modeling Person-Specific Deformable Anatomy
In order to obtain accurate target location information, clinicians have to either conduct frequent intraoperative scans, resulting in higher exposition of patients to radiations, or adopt proxy procedures (e. g., creating and using custom molds to keep patients in the exact same pose during both preoperative organ scanning and subsequent treatment.
Spatio-Temporal Representation Factorization for Video-based Person Re-Identification
To alleviate these problems, we propose Spatio-Temporal Representation Factorization (STRF), a flexible new computational unit that can be used in conjunction with most existing 3D convolutional neural network architectures for re-ID.
Ranked #2 on
Person Re-Identification
on DukeMTMC-VideoReID
Everybody Is Unique: Towards Unbiased Human Mesh Recovery
Next, we present a simple baseline to address this problem that is scalable and can be easily used in conjunction with existing algorithms to improve their performance.
Ranked #1 on
3D Human Shape Estimation
on SSP-3D
(PVE-T metric)
A Peek Into the Reasoning of Neural Networks: Interpreting with Structural Visual Concepts
Despite substantial progress in applying neural networks (NN) to a wide variety of areas, they still largely suffer from a lack of transparency and interpretability.
Rewriting by Generating: Learn Heuristics for Large-scale Vehicle Routing Problems
The large-scale vehicle routing problem is defined based on the classical VRP with usually more than one thousand customers.
Towards Visually Explaining Similarity Models
We show that the resulting similarity models perform, and can be visually explained, better than the corresponding baseline models trained without these constraints.
Visual Similarity Attention
While there has been substantial progress in learning suitable distance metrics, these techniques in general lack transparency and decision reasoning, i. e., explaining why the input set of images is similar or dissimilar.
Towards Visually Explaining Variational Autoencoders
We present methods to generate visual attention from the learned latent space, and also demonstrate such attention explanations serve more than just explaining VAE predictions.
Re-Identification with Consistent Attentive Siamese Networks
We propose a new deep architecture for person re-identification (re-id).
Measuring the Temporal Behavior of Real-World Person Re-Identification
Designing real-world person re-identification (re-id) systems requires attention to operational aspects not typically considered in academic research.
Deep Regression Bayesian Network and Its Applications
The major difficulty of learning and inference with deep directed models with many latent variables is the intractable inference due to the dependencies among the latent variables and the exponential number of latent variable configurations.
