253 citations · 368 across the 17 of their papers we have counts for
17 papers
Cross-Level Distillation and Feature Denoising for Cross-Domain Few-Shot Classification
Hao Zheng, Runqi Wang, Jianzhuang Liu +1
The conventional few-shot classification aims at learning a model on a large labeled base dataset and rapidly adapting to a target dataset that is from the same distribution as the…
Point Anywhere: Directed Object Estimation from Omnidirectional Images
Nanami Kotani, Asako Kanezaki
One of the intuitive instruction methods in robot navigation is a pointing gesture. In this study, we propose a method using an omnidirectional camera to eliminate the user/object…
Multi-goal Audio-visual Navigation using Sound Direction Map
Haru Kondoh, Asako Kanezaki
Over the past few years, there has been a great deal of research on navigation tasks in indoor environments using deep reinforcement learning agents. Most of these tasks use only v…
H-SAUR: Hypothesize, Simulate, Act, Update, and Repeat for Understanding Object Articulations from Interactions
Kei Ota, Hsiao-Yu Tung, Kevin A. Smith +5
The world is filled with articulated objects that are difficult to determine how to use from vision alone, e.g., a door might open inwards or outwards. Humans handle these objects…
Object Memory Transformer for Object Goal Navigation
Rui Fukushima, Kei Ota, Asako Kanezaki +2
This paper presents a reinforcement learning method for object goal navigation (ObjNav) where an agent navigates in 3D indoor environments to reach a target object based on long-te…
CTRMs: Learning to Construct Cooperative Timed Roadmaps for Multi-agent Path Planning in Continuous Spaces
Keisuke Okumura, Ryo Yonetani, Mai Nishimura +1
Multi-agent path planning (MAPP) in continuous spaces is a challenging problem with significant practical importance. One promising approach is to first construct graphs approximat…
OPIRL: Sample Efficient Off-Policy Inverse Reinforcement Learning via Distribution Matching
Hana Hoshino, Kei Ota, Asako Kanezaki +1
Inverse Reinforcement Learning (IRL) is attractive in scenarios where reward engineering can be tedious. However, prior IRL algorithms use on-policy transitions, which require inte…
Training Larger Networks for Deep Reinforcement Learning
Kei Ota, Devesh K. Jha, Asako Kanezaki
The success of deep learning in the computer vision and natural language processing communities can be attributed to training of very deep neural networks with millions or billions…
Deep Reactive Planning in Dynamic Environments
Kei Ota, Devesh K. Jha, Tadashi Onishi +5
The main novelty of the proposed approach is that it allows a robot to learn an end-to-end policy which can adapt to changes in the environment during execution. While goal conditi…
Path Planning using Neural A* Search
Ryo Yonetani, Tatsunori Taniai, Mohammadamin Barekatain +2
We present Neural A*, a novel data-driven search method for path planning problems. Despite the recent increasing attention to data-driven path planning, machine learning approache…
Unsupervised Learning of Image Segmentation Based on Differentiable Feature Clustering
Wonjik Kim, Asako Kanezaki, Masayuki Tanaka
The usage of convolutional neural networks (CNNs) for unsupervised image segmentation was investigated in this study. In the proposed approach, label prediction and network paramet…
Efficient Exploration in Constrained Environments with Goal-Oriented Reference Path
Kei Ota, Yoko Sasaki, Devesh K. Jha +2
In this paper, we consider the problem of building learning agents that can efficiently learn to navigate in constrained environments. The main goal is to design agents that can ef…
Salient object detection on hyperspectral images using features learned from unsupervised segmentation task
Nevrez Imamoglu, Guanqun Ding, Yuming Fang +3
Various saliency detection algorithms from color images have been proposed to mimic eye fixation or attentive object detection response of human observers for the same scenes. Howe…
An Integration of Bottom-up and Top-Down Salient Cues on RGB-D Data: Saliency from Objectness vs. Non-Objectness
Nevrez Imamoglu, Wataru Shimoda, Chi Zhang +4
Bottom-up and top-down visual cues are two types of information that helps the visual saliency models. These salient cues can be from spatial distributions of the features (space-b…
cvpaper.challenge in 2016: Futuristic Computer Vision through 1,600 Papers Survey
Hirokatsu Kataoka, Soma Shirakabe, Yun He +14
The paper gives futuristic challenges disscussed in the cvpaper.challenge. In 2015 and 2016, we thoroughly study 1,600+ papers in several conferences/journals such as CVPR/ICCV/ECC…
RotationNet: Joint Object Categorization and Pose Estimation Using Multiviews from Unsupervised Viewpoints
Asako Kanezaki, Yasuyuki Matsushita, Yoshifumi Nishida
We propose a Convolutional Neural Network (CNN)-based model "RotationNet," which takes multi-view images of an object as input and jointly estimates its pose and object category. U…
Recognizing Activities of Daily Living with a Wrist-mounted Camera
Katsunori Ohnishi, Atsushi Kanehira, Asako Kanezaki +1
We present a novel dataset and a novel algorithm for recognizing activities of daily living (ADL) from a first-person wearable camera. Handled objects are crucially important for e…