Convolutional neural network | EPFL Graph Search

Convolutional neural network (CNN) is a regularized type of feed-forward neural network that learns feature engineering by itself via filters (or kernel) optimization. Vanishing gradients and exploding gradients, seen during backpropagation in earlier neural networks, are prevented by using regularized weights over fewer connections. For example, for each neuron in the fully-connected layer 10,000 weights would be required for processing an image sized 100 × 100 pixels. However, applying cascaded convolution (or cross-correlation) kernels, only 25 neurons are required to process 5x5-sized tiles Higher-layer features are extracted from wider context windows, compared to lower-layer features. They have applications in:

image and video recognition,
recommender systems,
,
,
,
natural language processing,
brain–computer interfaces, and
financial time series.

CNNs are also known as Shift Invariant or Space Invariant Artificial Neural Networks (SIANN

Neural networks for semantic segmentation of historical city maps: Cross-cultural performance and the impact of figurative diversity

Rémi Guillaume Petitpierre

In this work, we present a new semantic segmentation model for historical city maps that surpasses the state of the art in terms of flexibility and performance. Research in automatic map processing is largely focused on homogeneous corpora or even individual maps, leading to inflexible algorithms. Recently, convolutional neural networks have opened new perspectives for the development of more generic tools. Based on two new maps corpora, the first one centered on Paris and the second one gathering cities from all over the world, we propose a method for operationalizing the figuration based on traditional computer vision algorithms that allows large-scale quantitative analysis. In a second step, we propose a semantic segmentation model based on neural networks and implement several improvements. Finally, we analyze the impact of map figuration on segmentation performance and evaluate future ways to improve the representational flexibility of neural networks. To conclude, we show that these networks are able to semantically segment map data of a very large figurative diversity with efficiency.

2020

Efficient Depth-based Deep Learning Methods for Multi-Party Pose Estimation

Angel Noe Martinez Gonzalez

Human detection and pose estimation are essential components for any artificial system responsive to the presence of humans and that react according to human-centered tasks. Robotic systems are typical examples, for which the body pose represents fine grained information useful to understand the behavior and activities of people, and interact with them. However, it is a challenging research topic with increasing difficulty given the unknown number of people in a usual scenario and factors like occlusions and sensing conditions. Current state-of-the-art methods have largely used deep Convolutional Neural Networks (CNN) to address the task. Traditionally, the selected CNNs are very deep and overparameterized, hence requiring large amounts of data to achieve good generalization and prevent overfitting. As a consequence, they are not straightforward to deploy in the low budget hardware typically available in practical applications such as HRI.This thesis studies methods for efficient and reliable 2D and 3D human pose estimation using deep learning approaches. It investigates novel lightweight convolutional network architectures that achieve real-time performance in multi-person scenarios and explores knowledge distillation methods to boost the performance of these models while keeping their efficiency. Moreover, this thesis addresses the high cost of data collection with annotations that arises with our deep learning-based approaches by relying on a large scale dataset of synthetic images with high variability. Domain adaptation methods and data augmentation strategies are proposed to exploit the synthetic corpus in order to achieve good generalization in sensor data. Additionally, this dissertation studies human 3D motion prediction framed as a sequence-to-sequence problem. Non-autoregressive transformer neural networks are proposed to predict elements in parallel to avoid error propagation from predicted elements, observed in autoregressive methods, while at the same time being efficient.Overall this thesis proposes different efficient and accurate deep learning solutions to design components of a human behaviour understanding system exploited in Human-Robot-Interaction (HRI) scenarios.

EPFL2021

LSSANet: A Long Short Slice-Aware Network for Pulmonary Nodule Detection

Bo Du, Rui Xu, Jiancheng Yang

Convolutional neural networks (CNNs) have been demonstrated to be highly effective in the field of pulmonary nodule detection. However, existing CNN based pulmonary nodule detection methods lack the ability to capture long-range dependencies, which is vital for global information extraction. In computer vision tasks, non-local operations have been widely utilized, but the computational cost could be very high for 3D computed tomography (CT) images. To address this issue, we propose a long short slice-aware network (LSSANet) for the detection of pulmonary nodules. In particular, we develop a new non-local mechanism termed long short slice grouping (LSSG), which splits the compact non-local embeddings into a short-distance slice grouped one and a long-distance slice grouped counterpart. This not only reduces the computational burden, but also keeps long-range dependencies among any elements across slices and in the whole feature map. The proposed LSSG is easy-to-use and can be plugged into many pulmonary nodule detection networks. To verify the performance of LSSANet, we compare with several recently proposed and competitive detection approaches based on 2D/3D CNN. Promising evaluation results on the large-scale PN9 dataset demonstrate the effectiveness of our method. Code is at https:// github.com/Ruixxxx/LSSANet.

SPRINGER INTERNATIONAL PUBLISHING AG2022

Efficient Depth-based Deep Learning Methods for Multi-Party Pose Estimation

Angel Noe Martinez Gonzalez

EPFL2021