A Web video retrieval method using hierarchical structure of Web video groups

In this paper, we propose a Web video retrieval method that uses hierarchical structure of Web video groups. Existing retrieval systems require users to input suitable queries that identify the desired contents in order to accurately retrieve Web videos; however, the proposed method enables retrieval of the desired Web videos even if users cannot input the suitable queries. Specifically, we first select representative Web videos from a target video dataset by using link relationships between Web videos obtained via metadata “related videos” and heterogeneous video features. Furthermore, by using the representative Web videos, we construct a network whose nodes and edges respectively correspond to Web videos and links between these Web videos. Then Web video groups, i.e., Web video sets with similar topics are hierarchically extracted based on strongly connected components, edge betweenness and modularity. By exhibiting the obtained hierarchical structure of Web video groups, users can easily grasp the overview of many Web videos. Consequently, even if users cannot write suitable queries that identify the desired contents, it becomes feasible to accurately retrieve the desired Web videos by selecting Web video groups according to the hierarchical structure. Experimental results on actual Web videos verify the effectiveness of our method

UAV/UGV Autonomous Cooperation: UAV Assists UGV to Climb a Cliff by Attaching a Tether

This paper proposes a novel cooperative system for an Unmanned Aerial Vehicle (UAV) and an Unmanned Ground Vehicle (UGV) which utilizes the UAV not only as a flying sensor but also as a tether attachment device. Two robots are connected with a tether, allowing the UAV to anchor the tether to a structure located at the top of a steep terrain, impossible to reach for UGVs. Thus, enhancing the poor traversability of the UGV by not only providing a wider range of scanning and mapping from the air, but also by allowing the UGV to climb steep terrains with the winding of the tether. In addition, we present an autonomous framework for the collaborative navigation and tether attachment in an unknown environment. The UAV employs visual inertial navigation with 3D voxel mapping and obstacle avoidance planning. The UGV makes use of the voxel map and generates an elevation map to execute path planning based on a traversability analysis. Furthermore, we compared the pros and cons of possible methods for the tether anchoring from multiple points of view. To increase the probability of successful anchoring, we evaluated the anchoring strategy with an experiment. Finally, the feasibility and capability of our proposed system were demonstrated by an autonomous mission experiment in the field with an obstacle and a cliff.Comment: 7 pages, 8 figures, accepted to 2019 International Conference on Robotics & Automation. Video: https://youtu.be/UzTT8Ckjz1

Surface modification of a Mo-substrate to form an oxidation-resistant MoSi2 layer using a Si-saturated tin bath

Molybdenum disilicide (MoSi2) is one of the most heat and oxidation resistant materials used in high-temperature applications. In this work, the feasibility of forming an oxidation-resistant MoSi2 coating using a low-temperature Sn-Si bath by the hot dipping method was evaluated. It was found that a smooth and dense MoSi2 layer can be successfully synthesized on the surface of a Mo-substrate at 700 °C to 1000 °C. Using this method, MoSi2 can be synthesized more rapidly than using conventional methods and at much low operating temperatures. The diffusion through the product layer controlled the growth of the formed MoSi2 layer, and the growth rate was fastest at 800 °C. This was due to the replacement by the larger tin atoms that led to the expansion of the lattice, allowing the faster diffusion of silicon through the silicide layer towards the unreacted Mo-substrate. Oxidation resistance test at 1150 °C for 2 h confirmed that the MoSi2 layer formed by the present method protects the Mo-substrate from rapid oxidation at elevated temperatures

Learning Risk-Aware Quadrupedal Locomotion using Distributional Reinforcement Learning

Deployment in hazardous environments requires robots to understand the risks associated with their actions and movements to prevent accidents. Despite its importance, these risks are not explicitly modeled by currently deployed locomotion controllers for legged robots. In this work, we propose a risk sensitive locomotion training method employing distributional reinforcement learning to consider safety explicitly. Instead of relying on a value expectation, we estimate the complete value distribution to account for uncertainty in the robot's interaction with the environment. The value distribution is consumed by a risk metric to extract risk sensitive value estimates. These are integrated into Proximal Policy Optimization (PPO) to derive our method, Distributional Proximal Policy Optimization (DPPO). The risk preference, ranging from risk-averse to risk-seeking, can be controlled by a single parameter, which enables to adjust the robot's behavior dynamically. Importantly, our approach removes the need for additional reward function tuning to achieve risk sensitivity. We show emergent risk sensitive locomotion behavior in simulation and on the quadrupedal robot ANYmal

Few-shot Personalized Saliency Prediction Based on Inter-personnel Gaze Patterns

This paper presents few-shot personalized saliency prediction based on inter-personnel gaze patterns. In contrast to a general saliency map, a personalized saliecny map (PSM) has been great potential since its map indicates the person-specific visual attention that is useful for obtaining individual visual preferences from heterogeneity of gazed areas. The PSM prediction is needed for acquiring the PSM for the unseen image, but its prediction is still a challenging task due to the complexity of individual gaze patterns. For modeling individual gaze patterns for various images, although the eye-tracking data obtained from each person is necessary to construct PSMs, it is difficult to acquire the massive amounts of such data. Here, one solution for efficient PSM prediction from the limited amount of data can be the effective use of eye-tracking data obtained from other persons. In this paper, to effectively treat the PSMs of other persons, we focus on the effective selection of images to acquire eye-tracking data and the preservation of structural information of PSMs of other persons. In the experimental results, we confirm that the above two focuses are effective for the PSM prediction with the limited amount of eye-tracking data.Comment: 5pages, 3 figure

Self-Supervised Learning for Gastritis Detection with Gastric X-Ray Images

We propose a novel self-supervised learning method for medical image analysis. Great progress has been made in medical image analysis because of the development of supervised learning based on deep convolutional neural networks. However, annotating complex medical images usually requires expert knowledge, making it difficult for a wide range of real-world applications ($e.g.$, computer-aided diagnosis systems). Our self-supervised learning method introduces a cross-view loss and a cross-model loss to solve the insufficient available annotations in medical image analysis. Experimental results show that our method can achieve high detection performance for gastritis detection with only a small number of annotations

Soft-Label Anonymous Gastric X-ray Image Distillation

This paper presents a soft-label anonymous gastric X-ray image distillation method based on a gradient descent approach. The sharing of medical data is demanded to construct high-accuracy computer-aided diagnosis (CAD) systems. However, the large size of the medical dataset and privacy protection are remaining problems in medical data sharing, which hindered the research of CAD systems. The idea of our distillation method is to extract the valid information of the medical dataset and generate a tiny distilled dataset that has a different data distribution. Different from model distillation, our method aims to find the optimal distilled images, distilled labels and the optimized learning rate. Experimental results show that the proposed method can not only effectively compress the medical dataset but also anonymize medical images to protect the patient's private information. The proposed approach can improve the efficiency and security of medical data sharing.Comment: Published as a conference paper at ICIP 202

Dataset Distillation using Parameter Pruning

The acquisition of advanced models relies on large datasets in many fields, which makes storing datasets and training models expensive. As a solution, dataset distillation can synthesize a small dataset that preserves most information of the original large dataset. The recently proposed dataset distillation method by matching network parameters has been proven effective for several datasets. However, the dimension of network parameters is usually large. And we found that a few parameters in the distillation process are difficult to match, which harms the distillation performance. Based on this observation, this paper proposes a new method to solve the problem using parameter pruning. The proposed method can synthesize more robust distilled datasets and improve the distillation performance by pruning difficult-to-match parameters in the distillation process. Experimental results on three datasets show that the proposed method outperformed other state-of-the-art dataset distillation methods