Joint Image-Visual Grounding of Temporal Memory Networks with Data-Adaptive Layerwise Regularization – A large number of tasks in robotics, including object pose estimation and tracking, require a human-occluded task. To tackle the challenge of capturing user-reported high-level pose accurately, we propose an end-to-end deep reinforcement learning system that simultaneously learns to recognize user-reported high-level pose and predict their intentions from a human-occluded model. In this work, we build a system that uses a novel learning strategy to learn how to perform various tasks, and how to predict an end-to-end human-occluded prediction based on a learned knowledge base. As a result, we significantly simplify tasks performed by humans and inferring end-to-end human-occluded trajectories from our end-to-end deep learning network. The results of experiments show that our end-to-end reinforcement learning system achieves state-of-the-art results when the user intent is not reported by the human models.
Neural autofocus is a very challenging task due to its inherent difficulty in capturing depth information from both 3D and 4D images. Such a problem has attracted a lot of attention in vision research, especially research on 3D and 4D object recognition. The task has been well-studied in different fields, mainly in the supervised setting, which can be seen as a form of data-driven learning. Nevertheless, a lot of previous work in this area is in the supervised domain. In this paper, we propose and study an end-to-end 3D autofocus system that can learn a depth information from 3D images. Experiments indicate that our system outperforms previous models in terms of the accuracy of retrieval, and even in the deep domain.
Joint Image-Visual Grounding of Temporal Memory Networks with Data-Adaptive Layerwise Regularization
Learning Deep Convolutional Features With Random Weights for Endoscopic Capsule Endoscopic ImagingNeural autofocus is a very challenging task due to its inherent difficulty in capturing depth information from both 3D and 4D images. Such a problem has attracted a lot of attention in vision research, especially research on 3D and 4D object recognition. The task has been well-studied in different fields, mainly in the supervised setting, which can be seen as a form of data-driven learning. Nevertheless, a lot of previous work in this area is in the supervised domain. In this paper, we propose and study an end-to-end 3D autofocus system that can learn a depth information from 3D images. Experiments indicate that our system outperforms previous models in terms of the accuracy of retrieval, and even in the deep domain.