2019/05/29 by Baoxiang Wang, Wang, Baoxiang
Computer Science · Mathematics · #Advanced Neural Network Applications #Adversarial Robustness in Machine Learning #Algorithm #Artificial Intelligence (cs.AI) #Artificial intelligence #Binary number #Clutter #Computer science #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Image (mathematics) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine learning #Mathematics #Mechanism (biology) #Pattern recognition (psychology) #Process (computing) #Salient #cs.AI #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1905.13551
International Joint Conference on Artificial Intelligence (IJCAI) 2019
openalex publication_date 2019/05/29 · arxiv created 2019/06/03 · arxiv updated 2019/06/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
Binary determination of the presence of objects is one of the problems where humans perform extraordinarily better than computer vision systems, in terms of both speed and preciseness. One of the possible reasons is that humans can skip most of the clutter and attend only on salient regions. Recurrent attention models (RAM) are the first computational models to imitate the way humans process images via the REINFORCE algorithm. Despite that RAM is originally designed for image recognition, we extend it and present recurrent existence determination, an attention-based mechanism to solve the existence determination. Our algorithm employs a novel k-maximum aggregation layer and a new reward mechanism to address the issue of delayed rewards, which would have caused the instability of the training process. The experimental analysis demonstrates significant efficiency and accuracy improvement over existing approaches, on both synthetic and real-world datasets.