We present SegFormer, a simple, efficient yet powerful semantic segmentation framework which unifies Transformers with lightweight multilayer perception (MLP) decoders.
Ranked #1 on Semantic Segmentation on COCO-Stuff full
In contrast to previous approaches that either lack the ability to generalize to arbitrary identity or fail to preserve attributes like facial expression and gaze direction, our framework is capable of transferring the identity of an arbitrary source face into an arbitrary target face while preserving the attributes of the target face.
Multi-Agent Reinforcement Learning (MARL) has seen revolutionary breakthroughs with its successful application to multi-agent cooperative tasks such as computer games and robot swarms.
We devise a hybrid deep learning approach to solving tabular data problems.
Recently, it has been demonstrated that the performance of a deep convolutional neural network can be effectively improved by embedding an attention module into it.
This paper presents a simple yet effective approach to modeling space-time correspondences in the context of video object segmentation.
Ranked #1 on Semi-Supervised Video Object Segmentation on DAVIS 2017 (val) (using extra training data)
Games are abstractions of the real world, where artificial agents learn to compete and cooperate with other agents.