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Found 17 papers, 4 papers with code
BClean: A Bayesian Data Cleaning System
By evaluating on both real-world and synthetic datasets, we demonstrate that BClean is capable of achieving an F-measure of up to 0. 9 in data cleaning, outperforming existing Bayesian methods by 2% and other data cleaning methods by 15%.
Spatial Attention-based Distribution Integration Network for Human Pose Estimation
The RFM incorporates a dilated residual block and attention mechanism to expand receptive fields while enhancing sensitivity to spatial information.
TouchUp-G: Improving Feature Representation through Graph-Centric Finetuning
How can we enhance the node features acquired from Pretrained Models (PMs) to better suit downstream graph learning tasks?
Full-resolution Lung Nodule Segmentation from Chest X-ray Images using Residual Encoder-Decoder Networks
The proposed algorithm achieved excellent generalization results against an external dataset with sensitivity of 77% at a false positive rate of 7. 6.
SpotTarget: Rethinking the Effect of Target Edges for Link Prediction in Graph Neural Networks
Particularly, in prevalent GNN frameworks (e. g., DGL and PyTorch-Geometric), the target edges (i. e., the edges being predicted) consistently exist as message passing edges in the graph during training.
Touch and Go: Learning from Human-Collected Vision and Touch
The ability to associate touch with sight is essential for tasks that require physically interacting with objects in the world.
Weight-based Channel-model Matrix Framework provides a reasonable solution for EEG-based cross-dataset emotion recognition
Cross-dataset emotion recognition as an extremely challenging task in the field of EEG-based affective computing is influenced by many factors, which makes the universal models yield unsatisfactory results.
CAPER: Coarsen, Align, Project, Refine - A General Multilevel Framework for Network Alignment
Network alignment, or the task of finding corresponding nodes in different networks, is an important problem formulation in many application domains.
Debiasing pipeline improves deep learning model generalization for X-ray based lung nodule detection
In stripping chest X-ray images of known confounding variables by lung field segmentation, along with suppression of signal noise from the bone structure we can train a highly accurate deep learning lung nodule detection algorithm with outstanding generalization accuracy of 89% to nodule samples in unseen data.
ContinuityLearner: Geometric Continuity Feature Learning for Lane Segmentation
Lane segmentation is a challenging issue in autonomous driving system designing because lane marks show weak textural consistency due to occlusion or extreme illumination but strong geometric continuity in traffic images, from which general convolution neural networks (CNNs) are not capable of learning semantic objects.
Node Proximity Is All You Need: Unified Structural and Positional Node and Graph Embedding
While most network embedding techniques model the relative positions of nodes in a network, recently there has been significant interest in structural embeddings that model node role equivalences, irrespective of their distances to any specific nodes.
NegatER: Unsupervised Discovery of Negatives in Commonsense Knowledge Bases
Codifying commonsense knowledge in machines is a longstanding goal of artificial intelligence.
Modeling Long-Term and Short-Term Interests with Parallel Attentions for Session-based Recommendation
First, most of the attention-based methods only simply utilize the last clicked item to represent the user's short-term interest ignoring the temporal information and behavior context, which may fail to capture the recent preference of users comprehensively.
MODMA dataset: a Multi-modal Open Dataset for Mental-disorder Analysis
The EEG dataset includes not only data collected using traditional 128-electrodes mounted elastic cap, but also a novel wearable 3-electrode EEG collector for pervasive applications.
Self-Supervised Learning of Depth and Ego-motion with Differentiable Bundle Adjustment
Learning to predict scene depth and camera motion from RGB inputs only is a challenging task.
Structure-Attentioned Memory Network for Monocular Depth Estimation
To this end, we introduce a new Structure-Oriented Memory (SOM) module to learn and memorize the structure-specific information between RGB image domain and the depth domain.
Ranked #38 on
Monocular Depth Estimation
on KITTI Eigen split
Learning Object-specific Distance from a Monocular Image
Environment perception, including object detection and distance estimation, is one of the most crucial tasks for autonomous driving.
