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Found 18 papers, 5 papers with code
Hybrid Representation Learning via Epistemic Graph
In recent years, deep models have achieved remarkable success in many vision tasks.
Learning to Learn Domain-invariant Parameters for Domain Generalization
Due to domain shift, deep neural networks (DNNs) usually fail to generalize well on unknown test data in practice.
SAP-DETR: Bridging the Gap Between Salient Points and Queries-Based Transformer Detector for Fast Model Convergency
To bridge the gap between the reference points of salient queries and Transformer detectors, we propose SAlient Point-based DETR (SAP-DETR) by treating object detection as a transformation from salient points to instance objects.
Decoupled Pyramid Correlation Network for Liver Tumor Segmentation from CT images
In this paper, we propose a Decoupled Pyramid Correlation Network (DPC-Net) that exploits attention mechanisms to fully leverage both low- and high-level features embedded in FCN to segment liver tumor.
Self-Supervised Graph Neural Network for Multi-Source Domain Adaptation
Domain adaptation (DA) tries to tackle the scenarios when the test data does not fully follow the same distribution of the training data, and multi-source domain adaptation (MSDA) is very attractive for real world applications.
Graph Attention Transformer Network for Multi-Label Image Classification
Subsequently, we design the graph attention transformer layer to transfer this adjacency matrix to adapt to the current domain.
A Survey of Visual Transformers
Transformer, an attention-based encoder-decoder model, has already revolutionized the field of natural language processing (NLP).
Modality-aware Mutual Learning for Multi-modal Medical Image Segmentation
To this end, we propose a novel mutual learning (ML) strategy for effective and robust multi-modal liver tumor segmentation.
ACN: Adversarial Co-training Network for Brain Tumor Segmentation with Missing Modalities
Specifically, ACN adopts a novel co-training network, which enables a coupled learning process for both full modality and missing modality to supplement each other's domain and feature representations, and more importantly, to recover the `missing' information of absent modalities.
Trust It or Not: Confidence-Guided Automatic Radiology Report Generation
Experimental results have demonstrated that the proposed method for model uncertainty characterization and estimation can produce more reliable confidence scores for radiology report generation, and the modified loss function, which takes into account the uncertainties, leads to better model performance on two public radiology report datasets.
Orthogonal Subspace Decomposition: A New Perspective of Learning Discriminative Features for Face Clustering
Learning discriminative node features is the key to further improve the performance of graph-based face clustering.
Double-Uncertainty Weighted Method for Semi-supervised Learning
We train the teacher model using Bayesian deep learning to obtain double-uncertainty, i. e. segmentation uncertainty and feature uncertainty.
Disentangled Neural Architecture Search
However, existing methods rely heavily on a black-box controller to search architectures, which suffers from the serious problem of lacking interpretability.
Does Non-COVID19 Lung Lesion Help? Investigating Transferability in COVID-19 CT Image Segmentation
Coronavirus disease 2019 (COVID-19) is a highly contagious virus spreading all around the world.
Challenge Closed-book Science Exam: A Meta-learning Based Question Answering System
Prior work in standardized science exams requires support from large text corpus, such as targeted science corpus fromWikipedia or SimpleWikipedia.
Semantic Feature Attention Network for Liver Tumor Segmentation in Large-scale CT database
In the SFAN, a Semantic Attention Transmission (SAT) module is designed to select discriminative low-level localization details with the guidance of neighboring high-level semantic information.
Efficient Automatic Meta Optimization Search for Few-Shot Learning
NAS automatically generates and evaluates meta-learner's architecture for few-shot learning problems, while the meta-learner uses meta-learning algorithm to optimize its parameters based on the distribution of learning tasks.
Facial Motion Prior Networks for Facial Expression Recognition
Deep learning based facial expression recognition (FER) has received a lot of attention in the past few years.
Ranked #2 on
Facial Expression Recognition (FER)
on MMI
Facial Expression Recognition
Facial Expression Recognition (FER)
