41,659 research outputs found
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
On human motion prediction using recurrent neural networks
Human motion modelling is a classical problem at the intersection of graphics
and computer vision, with applications spanning human-computer interaction,
motion synthesis, and motion prediction for virtual and augmented reality.
Following the success of deep learning methods in several computer vision
tasks, recent work has focused on using deep recurrent neural networks (RNNs)
to model human motion, with the goal of learning time-dependent representations
that perform tasks such as short-term motion prediction and long-term human
motion synthesis. We examine recent work, with a focus on the evaluation
methodologies commonly used in the literature, and show that, surprisingly,
state-of-the-art performance can be achieved by a simple baseline that does not
attempt to model motion at all. We investigate this result, and analyze recent
RNN methods by looking at the architectures, loss functions, and training
procedures used in state-of-the-art approaches. We propose three changes to the
standard RNN models typically used for human motion, which result in a simple
and scalable RNN architecture that obtains state-of-the-art performance on
human motion prediction.Comment: Accepted at CVPR 1
Non-local Neural Networks
Both convolutional and recurrent operations are building blocks that process
one local neighborhood at a time. In this paper, we present non-local
operations as a generic family of building blocks for capturing long-range
dependencies. Inspired by the classical non-local means method in computer
vision, our non-local operation computes the response at a position as a
weighted sum of the features at all positions. This building block can be
plugged into many computer vision architectures. On the task of video
classification, even without any bells and whistles, our non-local models can
compete or outperform current competition winners on both Kinetics and Charades
datasets. In static image recognition, our non-local models improve object
detection/segmentation and pose estimation on the COCO suite of tasks. Code is
available at https://github.com/facebookresearch/video-nonlocal-net .Comment: CVPR 2018, code is available at:
https://github.com/facebookresearch/video-nonlocal-ne
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