The adoption of “artificial intelligence (AI) in drug discovery”, where AI is used in the process
of pharmaceutical research and development, is progressing. By using the ability to process …

We introduce a unified framework to jointly model images, text, and human attention traces.
Our work is built on top of the recent Localized Narratives annotation framework, where each …

Feature extraction and matching are irreplaceable parts of a typical visual simultaneous
localization and mapping (VSLAM) algorithm. A variety of different approaches (eg, ORB …

针对移动机器人在大视角运动下光照变化剧烈或遭遇纹理稀疏场景易出现特征点提取困难,
极端角度下特征难以匹配导致对极几何计算误差较大问题, 提出一种融合改进图神经网络的视觉 …

This letter aims to learn a global representation for each point in a random cluster using only
purely local geometric or topological information. Based on this, distributed tags for indoor …

Aimed at the difficulty of feature point extraction in mobile robots with drastic changes in
illumination or sparse texture scenes under large-angle view motion, difficulty in matching …

Deep learning models have achieved great success in a wide range of areas over the past
decade, like image processing, natural language processing, audio recognition and robot …

We propose a framework which makes it feasible to directly train deep neural networks with
respect to popular families of task-specific non-decomposable performance measures such …

In this paper, we propose FOCA (First-Order Context-based Adaptation), a learning
framework to model sets of systems governed by common but unknown laws that …

Many domain specific machine learning tasks require more fine tuning with respect to
nondecomposable metrics to be effective. In many applications such as medical diagnosis …