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Found 27 papers, 9 papers with code
RLCAD: Reinforcement Learning Training Gym for Revolution Involved CAD Command Sequence Generation
A CAD command sequence is a typical parametric design paradigm in 3D CAD systems where a model is constructed by overlaying 2D sketches with operations such as extrusion, revolution, and Boolean operations.
Deep Self-Cleansing for Medical Image Segmentation with Noisy Labels
To achieve this, we devise a gaussian mixture model-based label filtering module that distinguishes noisy labels from clean labels.
HSVLT: Hierarchical Scale-Aware Vision-Language Transformer for Multi-Label Image Classification
Moreover, given the potential variance in object size and appearance within a single image, attention to features of different scales can help to discover possible objects in the image.
Going Beyond Multi-Task Dense Prediction with Synergy Embedding Models
Multi-task visual scene understanding aims to leverage the relationships among a set of correlated tasks which are solved simultaneously by embedding them within a uni- fied network.
Rethinking Multiple Instance Learning for Whole Slide Image Classification: A Bag-Level Classifier is a Good Instance-Level Teacher
Based on this idea, we design Iteratively Coupled Multiple Instance Learning (ICMIL) to couple the embedder and the bag classifier at a low cost.
Image-Based Virtual Try-On: A Survey
Image-based virtual try-on aims to synthesize a naturally dressed person image with a clothing image, which revolutionizes online shopping and inspires related topics within image generation, showing both research significance and commercial potential.
CTSN: Predicting Cloth Deformation for Skeleton-based Characters with a Two-stream Skinning Network
We present a novel learning method to predict the cloth deformation for skeleton-based characters with a two-stream network.
Tailored Multi-Organ Segmentation with Model Adaptation and Ensemble
Multi-organ segmentation, which identifies and separates different organs in medical images, is a fundamental task in medical image analysis.
Iteratively Coupled Multiple Instance Learning from Instance to Bag Classifier for Whole Slide Image Classification
In ICMIL, we use category information in the bag-level classifier to guide the patch-level fine-tuning of the patch feature extractor.
StyleIPSB: Identity-Preserving Semantic Basis of StyleGAN for High Fidelity Face Swapping
StyleIPSB gives us a novel tool for high-fidelity face swapping, and we propose a three-stage framework for face swapping with StyleIPSB.
SemiCVT: Semi-Supervised Convolutional Vision Transformer for Semantic Segmentation
Semi-supervised learning improves data efficiency of deep models by leveraging unlabeled samples to alleviate the reliance on a large set of labeled samples.
Super-Resolution Based Patch-Free 3D Image Segmentation with High-Frequency Guidance
The framework contains two sub-tasks, of which semantic segmentation is the main task and super resolution is an auxiliary task aiding in rebuilding the high frequency information from the LR input.
ScaleFormer: Revisiting the Transformer-based Backbones from a Scale-wise Perspective for Medical Image Segmentation
(2) A simple and effective spatial-aware inter-scale transformer is designed to interact among consensual regions in multiple scales, which can highlight the cross-scale dependency and resolve the complex scale variations.
Efficient and Accurate Hyperspectral Pansharpening Using 3D VolumeNet and 2.5D Texture Transfer
Different from the texture transfer processing of RGB image, we use HR PAN images as the reference images and perform texture transfer for each frequency band of MS images, which is named 2. 5D texture transfer.
Attention-based Cross-Layer Domain Alignment for Unsupervised Domain Adaptation
To further boost model adaptation performance, we propose a novel method called Attention-based Cross-layer Domain Alignment (ACDA), which captures the semantic relationship between the source and target domains across model layers and calibrates each level of semantic information automatically through a dynamic attention mechanism.
N-Cloth: Predicting 3D Cloth Deformation with Mesh-Based Networks
We present a novel mesh-based learning approach (N-Cloth) for plausible 3D cloth deformation prediction.
Sphere Face Model:A 3D Morphable Model with Hypersphere Manifold Latent Space
However, the shape parameters of traditional 3DMMs satisfy the multivariate Gaussian distribution while the identity embeddings satisfy the hypersphere distribution, and this conflict makes it challenging for face reconstruction models to preserve the faithfulness and the shape consistency simultaneously.
Mixed Transformer U-Net For Medical Image Segmentation
Therefore, Vision Transformers have emerged as alternative segmentation structures recently, for their innate ability of capturing long-range correlations through Self-Attention (SA).
Multi-phase Liver Tumor Segmentation with Spatial Aggregation and Uncertain Region Inpainting
In this work, we propose a novel LiTS method to adequately aggregate multi-phase information and refine uncertain region segmentation.
Reconstructing Recognizable 3D Face Shapes based on 3D Morphable Models
We compare our method with existing methods in terms of the reconstruction error, visual distinguishability, and face recognition accuracy of the shape parameters.
Graph-based Pyramid Global Context Reasoning with a Saliency-aware Projection for COVID-19 Lung Infections Segmentation
Our Graph- PGCR module is plug-and-play, which can be integrated into any architecture to improve its performance.
PA-ResSeg: A Phase Attention Residual Network for Liver Tumor Segmentation from Multi-phase CT Images
In this paper, we propose a phase attention residual network (PA-ResSeg) to model multi-phase features for accurate liver tumor segmentation, in which a phase attention (PA) is newly proposed to additionally exploit the images of arterial (ART) phase to facilitate the segmentation of portal venous (PV) phase.
Graph-BAS3Net: Boundary-Aware Semi-Supervised Segmentation Network With Bilateral Graph Convolution
Semi-supervised learning (SSL) algorithms have attracted much attentions in medical image segmentation by leveraging unlabeled data, which challenge in acquiring massive pixel-wise annotated samples.
Interactive Deep Refinement Network for Medical Image Segmentation
Deep learning techniques have successfully been employed in numerous computer vision tasks including image segmentation.
UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation
UNet, which is one of deep learning networks with an encoder-decoder architecture, is widely used in medical image segmentation.
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Multispectral Pedestrian Detection via Simultaneous Detection and Segmentation
To narrow this gap, we propose a network fusion architecture, which consists of a multispectral proposal network to generate pedestrian proposals, and a subsequent multispectral classification network to distinguish pedestrian instances from hard negatives.
Illumination-aware Faster R-CNN for Robust Multispectral Pedestrian Detection
Multispectral images of color-thermal pairs have shown more effective than a single color channel for pedestrian detection, especially under challenging illumination conditions.
