In recent years, deep models have achieved remarkable success in many vision tasks.
Due to domain shift, deep neural networks (DNNs) usually fail to generalize well on unknown test data in practice.
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.
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.
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.
Subsequently, we design the graph attention transformer layer to transfer this adjacency matrix to adapt to the current domain.
Transformer, an attention-based encoder-decoder model, has already revolutionized the field of natural language processing (NLP).
To this end, we propose a novel mutual learning (ML) strategy for effective and robust multi-modal liver tumor segmentation.
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.
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.
Learning discriminative node features is the key to further improve the performance of graph-based face clustering.
We train the teacher model using Bayesian deep learning to obtain double-uncertainty, i. e. segmentation uncertainty and feature uncertainty.
However, existing methods rely heavily on a black-box controller to search architectures, which suffers from the serious problem of lacking interpretability.
Coronavirus disease 2019 (COVID-19) is a highly contagious virus spreading all around the world.
Prior work in standardized science exams requires support from large text corpus, such as targeted science corpus fromWikipedia or SimpleWikipedia.
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.
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.
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