3,413 research outputs found
Analyzing Modular CNN Architectures for Joint Depth Prediction and Semantic Segmentation
This paper addresses the task of designing a modular neural network
architecture that jointly solves different tasks. As an example we use the
tasks of depth estimation and semantic segmentation given a single RGB image.
The main focus of this work is to analyze the cross-modality influence between
depth and semantic prediction maps on their joint refinement. While most
previous works solely focus on measuring improvements in accuracy, we propose a
way to quantify the cross-modality influence. We show that there is a
relationship between final accuracy and cross-modality influence, although not
a simple linear one. Hence a larger cross-modality influence does not
necessarily translate into an improved accuracy. We find that a beneficial
balance between the cross-modality influences can be achieved by network
architecture and conjecture that this relationship can be utilized to
understand different network design choices. Towards this end we propose a
Convolutional Neural Network (CNN) architecture that fuses the state of the
state-of-the-art results for depth estimation and semantic labeling. By
balancing the cross-modality influences between depth and semantic prediction,
we achieve improved results for both tasks using the NYU-Depth v2 benchmark.Comment: Accepted to ICRA 201
Interpretable 3D Human Action Analysis with Temporal Convolutional Networks
The discriminative power of modern deep learning models for 3D human action
recognition is growing ever so potent. In conjunction with the recent
resurgence of 3D human action representation with 3D skeletons, the quality and
the pace of recent progress have been significant. However, the inner workings
of state-of-the-art learning based methods in 3D human action recognition still
remain mostly black-box. In this work, we propose to use a new class of models
known as Temporal Convolutional Neural Networks (TCN) for 3D human action
recognition. Compared to popular LSTM-based Recurrent Neural Network models,
given interpretable input such as 3D skeletons, TCN provides us a way to
explicitly learn readily interpretable spatio-temporal representations for 3D
human action recognition. We provide our strategy in re-designing the TCN with
interpretability in mind and how such characteristics of the model is leveraged
to construct a powerful 3D activity recognition method. Through this work, we
wish to take a step towards a spatio-temporal model that is easier to
understand, explain and interpret. The resulting model, Res-TCN, achieves
state-of-the-art results on the largest 3D human action recognition dataset,
NTU-RGBD.Comment: 8 pages, 5 figures, BNMW CVPR 2017 Submissio
Ego Vehicle Speed Estimation using 3D Convolution with Masked Attention
Speed estimation of an ego vehicle is crucial to enable autonomous driving
and advanced driver assistance technologies. Due to functional and legacy
issues, conventional methods depend on in-car sensors to extract vehicle speed
through the Controller Area Network bus. However, it is desirable to have
modular systems that are not susceptible to external sensors to execute
perception tasks. In this paper, we propose a novel 3D-CNN with
masked-attention architecture to estimate ego vehicle speed using a single
front-facing monocular camera. To demonstrate the effectiveness of our method,
we conduct experiments on two publicly available datasets, nuImages and KITTI.
We also demonstrate the efficacy of masked-attention by comparing our method
with a traditional 3D-CNN.Comment: 13 pages, 6 figure
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