Search Results for author:
Found 22 papers, 11 papers with code
RSUD20K: A Dataset for Road Scene Understanding In Autonomous Driving
Road scene understanding is crucial in autonomous driving, enabling machines to perceive the visual environment.
Adaptive spectral graph wavelets for collaborative filtering
Collaborative filtering is a popular approach in recommender systems, whose objective is to provide personalized item suggestions to potential users based on their purchase or browsing history.
Classification of developmental and brain disorders via graph convolutional aggregation
While graph convolution based methods have become the de-facto standard for graph representation learning, their applications to disease prediction tasks remain quite limited, particularly in the classification of neurodevelopmental and neurodegenerative brain disorders.
Learning to recognize occluded and small objects with partial inputs
Recognizing multiple objects in an image is challenging due to occlusions, and becomes even more so when the objects are small.
Spatio-temporal MLP-graph network for 3D human pose estimation
Graph convolutional networks and their variants have shown significant promise in 3D human pose estimation.
A Graph Encoder-Decoder Network for Unsupervised Anomaly Detection
However, most existing graph pooling strategies rely on an assignment matrix obtained by employing a GNN layer, which is characterized by trainable parameters, often leading to significant computational complexity and a lack of interpretability in the pooling process.
Iterative Graph Filtering Network for 3D Human Pose Estimation
Furthermore, we conduct ablation studies to analyze the contributions of different components of our model architecture and show that the skip connection and adjacency modulation help improve the model performance.
Regular Splitting Graph Network for 3D Human Pose Estimation
In human pose estimation methods based on graph convolutional architectures, the human skeleton is usually modeled as an undirected graph whose nodes are body joints and edges are connections between neighboring joints.
Ranked #40 on
3D Human Pose Estimation
on MPI-INF-3DHP
(AUC metric)
Fill in Fabrics: Body-Aware Self-Supervised Inpainting for Image-Based Virtual Try-On
Previous virtual try-on methods usually focus on aligning a clothing item with a person, limiting their ability to exploit the complex pose, shape and skin color of the person, as well as the overall structure of the clothing, which is vital to photo-realistic virtual try-on.
Masked Supervised Learning for Semantic Segmentation
Self-attention is of vital importance in semantic segmentation as it enables modeling of long-range context, which translates into improved performance.
Ranked #97 on
Semantic Segmentation
on NYU Depth v2
Higher-Order Implicit Fairing Networks for 3D Human Pose Estimation
Estimating a 3D human pose has proven to be a challenging task, primarily because of the complexity of the human body joints, occlusions, and variability in lighting conditions.
Ranked #217 on
3D Human Pose Estimation
on Human3.6M
STAR: Noisy Semi-Supervised Transfer Learning for Visual Classification
However, recent SSL methods rely on unlabeled image data at a scale of billions to work well.
Sharp U-Net: Depthwise Convolutional Network for Biomedical Image Segmentation
The U-Net architecture, built upon the fully convolutional network, has proven to be effective in biomedical image segmentation.
Synthetic COVID-19 Chest X-ray Dataset for Computer-Aided Diagnosis
We introduce a new dataset called Synthetic COVID-19 Chest X-ray Dataset for training machine learning models.
Ridge Regression Neural Network for Pediatric Bone Age Assessment
In this paper, we introduce a unified deep learning framework for bone age assessment using instance segmentation and ridge regression.
A Federated Learning Approach to Anomaly Detection in Smart Buildings
These devices sense the environment and generate multivariate temporal data of paramount importance for detecting anomalies and improving the prediction of energy usage in smart buildings.
Synthesis of COVID-19 Chest X-rays using Unpaired Image-to-Image Translation
Second, we show how our image synthesis method can serve as a data anonymization tool by achieving comparable detection performance when trained only on synthetic data.
Anisotropic Graph Convolutional Network for Semi-supervised Learning
Graph convolutional networks learn effective node embeddings that have proven to be useful in achieving high-accuracy prediction results in semi-supervised learning tasks, such as node classification.
Graph Fairing Convolutional Networks for Anomaly Detection
The proposed layerwise propagation rule of our model is theoretically motivated by the concept of implicit fairing in geometry processing, and comprises a graph convolution module for aggregating information from immediate node neighbors and a skip connection module for combining layer-wise neighborhood representations.
Semi-supervised Anomaly Detection
Supervised Anomaly Detection
Melanoma Detection using Adversarial Training and Deep Transfer Learning
In the first stage, we leverage the inter-class variation of the data distribution for the task of conditional image synthesis by learning the inter-class mapping and synthesizing under-represented class samples from the over-represented ones using unpaired image-to-image translation.
Shape retrieval of non-rigid 3d human models
In addition, further participants have also taken part, and we provide extra analysis of the retrieval results.
A Multicomponent Approach to Nonrigid Registration of Diffusion Tensor Images
We propose a nonrigid registration approach for diffusion tensor images using a multicomponent information-theoretic measure.
