Tencent (China)

Graph neural networks: A review of methods and applications

Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers

Most recent semantic segmentation methods adopt a fully-convolutional network (FCN) with an encoder-decoder architecture. The encoder progressively reduces the spatial resolutio...

CosFace: Large Margin Cosine Loss for Deep Face Recognition

Face recognition has made extraordinary progress owing to the advancement of deep convolutional neural networks (CNNs). The central task of face recognition, including face veri...

ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties

Abstract Because undesirable pharmacokinetics and toxicity of candidate compounds are the main reasons for the failure of drug development, it has been widely recognized that ab...

ICNet for Real-Time Semantic Segmentation on High-Resolution Images

SGN: Sequential Grouping Networks for Instance Segmentation

In this paper, we propose Sequential Grouping Networks (SGN) to tackle the problem of object instance segmentation. SGNs employ a sequence of neural networks, each solving a sub...

InteractionGraphNet: A Novel and Efficient Deep Graph Representation Learning Framework for Accurate Protein–Ligand Interaction Predictions

Accurate quantification of protein-ligand interactions remains a key challenge to structure-based drug design. However, traditional machine learning (ML)-based methods based on ...

AP-Loss for Accurate One-Stage Object Detection

One-stage object detectors are trained by optimizing classification-loss and localization-loss simultaneously, with the former suffering much from extreme foreground-background ...