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Found 20 papers, 5 papers with code
Unsupervised Self-Driving Attention Prediction via Uncertainty Mining and Knowledge Embedding
Predicting attention regions of interest is an important yet challenging task for self-driving systems.
Interdisciplinary Discovery of Nanomaterials Based on Convolutional Neural Networks
The material science literature contains up-to-date and comprehensive scientific knowledge of materials.
Communication-Efficient Topologies for Decentralized Learning with $O(1)$ Consensus Rate
In the proposed family, EquiStatic has a degree of $\Theta(\ln(n))$, where $n$ is the network size, and a series of time-dependent one-peer topologies, EquiDyn, has a constant degree of 1.
Causal Inference via Nonlinear Variable Decorrelation for Healthcare Applications
Causal inference and model interpretability research are gaining increasing attention, especially in the domains of healthcare and bioinformatics.
Remote Medication Status Prediction for Individuals with Parkinson's Disease using Time-series Data from Smartphones
Medication for neurological diseases such as the Parkinson's disease usually happens remotely at home, away from hospitals.
Product Information Browsing Support System Using Analytic Hierarchy Process
Large-scale e-commerce sites can collect and analyze a large number of user preferences and behaviors, and thus can recommend highly trusted products to users.
Learning Bias-Invariant Representation by Cross-Sample Mutual Information Minimization
We propose to remove the bias information misused by the target task with a cross-sample adversarial debiasing (CSAD) method.
Scalable Semi-supervised Landmark Localization for X-ray Images using Few-shot Deep Adaptive Graph
It first trains a DAG model on the labeled data and then fine-tunes the pre-trained model on the unlabeled data with a teacher-student SSL mechanism.
ConTNet: Why not use convolution and transformer at the same time?
It is also worth pointing that, given identical strong data augmentations, the performance improvement of ConTNet is more remarkable than that of ResNet.
Contour Transformer Network for One-shot Segmentation of Anatomical Structures
In this work, we present Contour Transformer Network (CTN), a one-shot anatomy segmentation method with a naturally built-in human-in-the-loop mechanism.
Blind signal decomposition of various word embeddings based on join and individual variance explained
We found that by mapping different word embeddings into the joint component, sentiment performance can be greatly improved for the original word embeddings with lower performance.
Predicting Parkinson's Disease with Multimodal Irregularly Collected Longitudinal Smartphone Data
Parkinsons Disease is a neurological disorder and prevalent in elderly people.
Learning to Segment Anatomical Structures Accurately from One Exemplar
Accurate segmentation of critical anatomical structures is at the core of medical image analysis.
Anatomy-Aware Siamese Network: Exploiting Semantic Asymmetry for Accurate Pelvic Fracture Detection in X-ray Images
A new contrastive feature learning component in our Siamese network is designed to optimize the deep image features being more salient corresponding to the underlying semantic asymmetries (caused by pelvic fracture occurrences).
Alleviating the Incompatibility between Cross Entropy Loss and Episode Training for Few-shot Skin Disease Classification
Inspired by the recent success of Few-Shot Learning (FSL) in natural image classification, we propose to apply FSL to skin disease identification to address the extreme scarcity of training sample problem.
Structured Landmark Detection via Topology-Adapting Deep Graph Learning
Image landmark detection aims to automatically identify the locations of predefined fiducial points.
Ranked #4 on
Face Alignment
on COFW-68
Unsupervised Learning of Landmarks based on Inter-Intra Subject Consistencies
To recover from the transformed images back to the original subject, the landmark detector is forced to learn spatial locations that contain the consistent semantic meanings both for the paired intra-subject images and between the paired inter-subject images.
Uncovering Download Fraud Activities in Mobile App Markets
To the best of our knowledge, this is the first work that investigates the download fraud problem in mobile App markets.
Patch Transformer for Multi-tagging Whole Slide Histopathology Images
Automated whole slide image (WSI) tagging has become a growing demand due to the increasing volume and diversity of WSIs collected nowadays in histopathology.
Attentive Relational Networks for Mapping Images to Scene Graphs
Scene graph generation refers to the task of automatically mapping an image into a semantic structural graph, which requires correctly labeling each extracted object and their interaction relationships.
