Zobrazeno 1 - 10
of 284
pro vyhledávání: '"Kaneda, Kazufumi"'
Autor:
Miao, JiangDong, Ikeda, Tatsuya, Raytchev, Bisser, Mizoguchi, Ryota, Hiraoka, Takenori, Nakashima, Takuji, Shimizu, Keigo, Higaki, Toru, Kaneda, Kazufumi
Although 3D object editing has the potential to significantly influence various industries, recent research in 3D generation and editing has primarily focused on converting text and images into 3D models, often overlooking the need for fine-grained c
Externí odkaz:
http://arxiv.org/abs/2410.23931
In this paper we propose a novel method which leverages the uncertainty measures provided by Bayesian deep networks through curriculum learning so that the uncertainty estimates are fed back to the system to resample the training data more densely in
Externí odkaz:
http://arxiv.org/abs/2108.11693
Publikováno v:
IEEE Access, Vol.8, pp. 180633-180645, Sep 2020
We propose a novel approach to identify the difficulty of visual questions for Visual Question Answering (VQA) without direct supervision or annotations to the difficulty. Prior works have considered the diversity of ground-truth answers of human ann
Externí odkaz:
http://arxiv.org/abs/2004.05595
Visual question answering (VQA) is a task of answering a visual question that is a pair of question and image. Some visual questions are ambiguous and some are clear, and it may be appropriate to change the ambiguity of questions from situation to si
Externí odkaz:
http://arxiv.org/abs/2004.04963
Publikováno v:
in Proc. of MIRU2018
We propose an easy-to-use non-overlapping camera calibration method. First, successive images are fed to a PoseNet-based network to obtain ego-motion of cameras between frames. Next, the pose between cameras are estimated. Instead of using a batch me
Externí odkaz:
http://arxiv.org/abs/2002.08005
Autor:
Ogawa, Daisuke, Tamaki, Toru, Hirakawa, Tsubasa, Raytchev, Bisser, Kaneda, Kazufumi, Yoda, Ken
An efficient inverse reinforcement learning for generating trajectories is proposed based of 2D and 3D activity forecasting. We modify reward function with $L_p$ norm and propose convolution into value iteration steps, which is called convolutional v
Externí odkaz:
http://arxiv.org/abs/1912.05729
Publikováno v:
Advanced Robotics, Volume 33, 2019 - Issue 3-4: Special Issue on Systems Science of Bio-navigation, Pages 153-168
In this paper, we propose a method for semantic segmentation of pedestrian trajectories based on pedestrian behavior models, or agents. The agents model the dynamics of pedestrian movements in two-dimensional space using a linear dynamics model and c
Externí odkaz:
http://arxiv.org/abs/1912.05727
In this paper we propose a novel deep learning-based algorithm for biomedical image segmentation which uses a sequential attention mechanism able to shift the focus of attention across the image in a selective way, allowing subareas which are more di
Externí odkaz:
http://arxiv.org/abs/1909.12612
In many cases, such as trajectories clustering and classification, we often divide a trajectory into segments as preprocessing. In this paper, we propose a trajectory semantic segmentation method based on learned behavior models. In the proposed meth
Externí odkaz:
http://arxiv.org/abs/1802.09659
We propose a feature for action recognition called Trajectory-Set (TS), on top of the improved Dense Trajectory (iDT). The TS feature encodes only trajectories around densely sampled interest points, without any appearance features. Experimental resu
Externí odkaz:
http://arxiv.org/abs/1711.10143