We address the problem of estimating a high quality dense depth map from a single RGB input image. We start out with a baseline encoder-decoder convolutional neural network architecture and pose the question of how the global processing of information can help improve overall depth estimation. To this end, we propose a transformerbased architecture block that divides the depth range into bins whose center value is estimated adaptively per image. The final depth values are estimated as linear combinations of the bin centers. We call our new building block AdaBins. Our results show a decisive improvement over the state-ofthe-art on several popular depth datasets across all metrics. We also validate the effectiveness of the proposed block with an ablation study and provide the code and corresponding pre-trained weights of the new state-of-the-art model 1 .

尽管神经网络表现出具有非凡的语言内容的非凡能力，但捕获与说话者对话角色有关的上下文信息是一个开放的研究领域。在这项工作中，我们通过黑手党的游戏分析了说话者角色对语言使用的影响，其中参与者被分配了诚实或欺骗性的角色。除了构建一个框架以收集黑手党游戏记录数据集外，我们还证明了角色不同的玩家所产生的语言差异。我们确认，分类模型能够将欺骗性玩家排名为仅根据语言的使用而对诚实的玩家排名更可疑。此外，我们表明，有关两个辅助任务的培训模型优于基于BERT的标准文本分类方法。我们还提出了使用训练有素的模型来识别区分玩家角色的功能的方法，这些功能可在黑手党游戏中用于帮助玩家。