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Found 16 papers, 9 papers with code
Rediscovering BCE Loss for Uniform Classification
We also propose the uniform classification accuracy as a metric to measure the model's performance in uniform classification.
Decoder-Only Image Registration
For this, we propose a novel network architecture, termed LessNet in this paper, which contains only a learnable decoder, while entirely omitting the utilization of a learnable encoder.
Fourier-Net+: Leveraging Band-Limited Representation for Efficient 3D Medical Image Registration
Instead of directly predicting a full-resolution displacement field, our Fourier-Net learns a low-dimensional representation of the displacement field in the band-limited Fourier domain which our model-driven decoder converts to a full-resolution displacement field in the spatial domain.
UniFace: Unified Cross-Entropy Loss for Deep Face Recognition
To bridge this gap, we design a UCE (Unified Cross-Entropy) loss for face recognition model training, which is built on the vital constraint that all the positive sample-to-class similarities shall be larger than the negative ones.
Category-Level 6D Object Pose Estimation with Flexible Vector-Based Rotation Representation
The proposed rotation representation has two major advantages: 1) decoupled characteristic that makes the rotation estimation easier; 2) flexible length and rotated angle of the vectors allow us to find a more suitable vector representation for specific pose estimation task.
Fourier-Net: Fast Image Registration with Band-limited Deformation
Specifically, instead of our Fourier-Net learning to output a full-resolution displacement field in the spatial domain, we learn its low-dimensional representation in a band-limited Fourier domain.
Ranked #3 on
Medical Image Registration
on OASIS
(val dsc metric)
GRATIS: Deep Learning Graph Representation with Task-specific Topology and Multi-dimensional Edge Features
Our framework is effective, robust and flexible, and is a plug-and-play module that can be combined with different backbones and Graph Neural Networks (GNNs) to generate a task-specific graph representation from various graph and non-graph data.
U-Net vs Transformer: Is U-Net Outdated in Medical Image Registration?
On the public 3D IXI brain dataset for atlas-based registration, we show that the performance of the vanilla U-Net is already comparable with that of state-of-the-art transformer-based networks (such as TransMorph), and that the proposed LKU-Net outperforms TransMorph by using only 1. 12% of its parameters and 10. 8% of its mult-adds operations.
Structure Unbiased Adversarial Model for Medical Image Segmentation
It consists of a spatial transformation block followed by an intensity distribution rendering module.
Nesterov Accelerated ADMM for Fast Diffeomorphic Image Registration
Recent developments in stochastic approaches based on deep learning have achieved sub-second runtimes for DiffIR with competitive registration accuracy, offering a fast alternative to conventional iterative methods.
Learning a Model-Driven Variational Network for Deformable Image Registration
We then propose two neural layers (i. e. warping layer and intensity consistency layer) to model the analytical solution and a residual U-Net to formulate the denoising problem (i. e. generalized denoising layer).
FS-Net: Fast Shape-based Network for Category-Level 6D Object Pose Estimation with Decoupled Rotation Mechanism
In this paper, we focus on category-level 6D pose and size estimation from monocular RGB-D image.
Ranked #7 on
6D Pose Estimation using RGBD
on REAL275
Geometry Constrained Weakly Supervised Object Localization
The detector predicts the object location defined by a set of coefficients describing a geometric shape (i. e. ellipse or rectangle), which is geometrically constrained by the mask produced by the generator.
G2L-Net: Global to Local Network for Real-time 6D Pose Estimation with Embedding Vector Features
Third, via the predicted segmentation and translation, we transfer the fine object point cloud into a local canonical coordinate, in which we train a rotation localization network to estimate initial object rotation.
Texture Deformation Based Generative Adversarial Networks for Face Editing
We also evaluate our approach qualitatively and quantitatively on facial attribute and facial expression synthesis.
Skin Lesion Classification using Class Activation Map
We proposed a two stage framework with only one network to analyze skin lesion images, we firstly trained a convolutional network to classify these images, and cropped the import regions which the network has the maximum activation value.
