4,976 research outputs found
Substructure and Boundary Modeling for Continuous Action Recognition
This paper introduces a probabilistic graphical model for continuous action
recognition with two novel components: substructure transition model and
discriminative boundary model. The first component encodes the sparse and
global temporal transition prior between action primitives in state-space model
to handle the large spatial-temporal variations within an action class. The
second component enforces the action duration constraint in a discriminative
way to locate the transition boundaries between actions more accurately. The
two components are integrated into a unified graphical structure to enable
effective training and inference. Our comprehensive experimental results on
both public and in-house datasets show that, with the capability to incorporate
additional information that had not been explicitly or efficiently modeled by
previous methods, our proposed algorithm achieved significantly improved
performance for continuous action recognition.Comment: Detailed version of the CVPR 2012 paper. 15 pages, 6 figure
A Unified Framework for Mutual Improvement of SLAM and Semantic Segmentation
This paper presents a novel framework for simultaneously implementing
localization and segmentation, which are two of the most important vision-based
tasks for robotics. While the goals and techniques used for them were
considered to be different previously, we show that by making use of the
intermediate results of the two modules, their performance can be enhanced at
the same time. Our framework is able to handle both the instantaneous motion
and long-term changes of instances in localization with the help of the
segmentation result, which also benefits from the refined 3D pose information.
We conduct experiments on various datasets, and prove that our framework works
effectively on improving the precision and robustness of the two tasks and
outperforms existing localization and segmentation algorithms.Comment: 7 pages, 5 figures.This work has been accepted by ICRA 2019. The demo
video can be found at https://youtu.be/Bkt53dAehj
One Point is All You Need: Directional Attention Point for Feature Learning
We present a novel attention-based mechanism for learning enhanced point
features for tasks such as point cloud classification and segmentation. Our key
message is that if the right attention point is selected, then "one point is
all you need" -- not a sequence as in a recurrent model and not a pre-selected
set as in all prior works. Also, where the attention point is should be
learned, from data and specific to the task at hand. Our mechanism is
characterized by a new and simple convolution, which combines the feature at an
input point with the feature at its associated attention point. We call such a
point a directional attention point (DAP), since it is found by adding to the
original point an offset vector that is learned by maximizing the task
performance in training. We show that our attention mechanism can be easily
incorporated into state-of-the-art point cloud classification and segmentation
networks. Extensive experiments on common benchmarks such as ModelNet40,
ShapeNetPart, and S3DIS demonstrate that our DAP-enabled networks consistently
outperform the respective original networks, as well as all other competitive
alternatives, including those employing pre-selected sets of attention points
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