Weakly supervised object localization and detection: A survey
As an emerging and challenging problem in the computer vision community, weakly
supervised object localization and detection plays an important role for developing new …
supervised object localization and detection plays an important role for developing new …
Deep reinforcement learning in computer vision: a comprehensive survey
Deep reinforcement learning augments the reinforcement learning framework and utilizes
the powerful representation of deep neural networks. Recent works have demonstrated the …
the powerful representation of deep neural networks. Recent works have demonstrated the …
A review on machine learning styles in computer vision—techniques and future directions
Computer applications have considerably shifted from single data processing to machine
learning in recent years due to the accessibility and availability of massive volumes of data …
learning in recent years due to the accessibility and availability of massive volumes of data …
On the detection of digital face manipulation
Detecting manipulated facial images and videos is an increasingly important topic in digital
media forensics. As advanced face synthesis and manipulation methods are made …
media forensics. As advanced face synthesis and manipulation methods are made …
Deep reinforcement learning: An overview
Y Li - arXiv preprint arXiv:1701.07274, 2017 - arxiv.org
We give an overview of recent exciting achievements of deep reinforcement learning (RL).
We discuss six core elements, six important mechanisms, and twelve applications. We start …
We discuss six core elements, six important mechanisms, and twelve applications. We start …
A gentle introduction to reinforcement learning and its application in different fields
Due to the recent progress in Deep Neural Networks, Reinforcement Learning (RL) has
become one of the most important and useful technology. It is a learning method where a …
become one of the most important and useful technology. It is a learning method where a …
Runtime neural pruning
In this paper, we propose a Runtime Neural Pruning (RNP) framework which prunes the
deep neural network dynamically at the runtime. Unlike existing neural pruning methods …
deep neural network dynamically at the runtime. Unlike existing neural pruning methods …
Active learning query strategies for classification, regression, and clustering: A survey
Generally, data is available abundantly in unlabeled form, and its annotation requires some
cost. The labeling, as well as learning cost, can be minimized by learning with the minimum …
cost. The labeling, as well as learning cost, can be minimized by learning with the minimum …
Action-decision networks for visual tracking with deep reinforcement learning
This paper proposes a novel tracker which is controlled by sequentially pursuing actions
learned by deep reinforcement learning. In contrast to the existing trackers using deep …
learned by deep reinforcement learning. In contrast to the existing trackers using deep …