Exploiting Spatio-Temporal Coherence for Video Object Detection in Robotics

This paper proposes a method to enhance video object detection for indoor environments in robotics. Concretely, it exploits knowledge about the camera motion between frames to propagate previously detected objects to successive frames. The proposal is rooted in the concepts of planar homography to propose regions of interest where to find objects, and recursive Bayesian filtering to integrate observations over time. The proposal is evaluated on six virtual, indoor environments, accounting for the detection of nine object classes over a total of ∼ 7k frames. Results show that our proposal improves the recall and the F1-score by a factor of 1.41 and 1.27, respectively, as well as it achieves a significant reduction of the object categorization entropy (58.8%) when compared to a two-stage video object detection method used as baseline, at the cost of small time overheads (120 ms) and precision loss (0.92).</p

Technology 2003: The Fourth National Technology Transfer Conference and Exposition, volume 2

Proceedings from symposia of the Technology 2003 Conference and Exposition, Dec. 7-9, 1993, Anaheim, CA, are presented. Volume 2 features papers on artificial intelligence, CAD&E, computer hardware, computer software, information management, photonics, robotics, test and measurement, video and imaging, and virtual reality/simulation

Representational Redundancy Reduction Strategies for Efficient Neural Network Architectures for Visual and Language Tasks

Deep neural networks have transformed a wide variety of domains including natural language processing, image and video processing, and robotics. However, the computational cost of training and inference with these models is high, and the rise of unsupervised pretraining has allowed ever larger networks to be used to further improve performance. Running these large neural networks in compute constrained environments such as on edge devices is infeasible, and the alternative of doing inference using cloud compute can be exceedingly expensive, with the largest language models needing to be distributed across multiple GPUs. Because of these constraints, size reduction and improving inference speed has been a main focus in neural network research. A wide variety of techniques have been proposed to improve the efficiency of existing neural networks including pruning, quantization, and knowledge distillation. In addition there is extensive effort on creating more efficient networks through hand design or an automated process called neural architecture search. However, there remain key domains where where there is significant room for improvement, which we demonstrate in this thesis. In this thesis we aim to improve the efficiency of deep neural networks in terms of inference latency, model size and latent representation size. We take an alternative approach to previous research and instead investigate redundant representations in neural networks. Across three domains of text classification, image classification and generative models we hypothesize that current neural networks contain representational redundancy and show that through the removal of this redundancy we can improve their efficiency. For image classification we hypothesize that convolution kernels contain redundancy in terms of unnecessary channel wise flexibility, and test this by introducing additional weight sharing into the network, preserving or even increasing classification performance while requiring fewer parameters. We show the benefits of this approach on convolution layers on the CIFAR and Imagenet datasets, on both standard models and models explicitly designed to be parameter efficient. For generative models we show it is possible to reduce the size of the latent representation of the model while preserving the quality of the generated images through the unsupervised disentanglement of shape and orientation. To do this we introduce the affine variational autoencoder, a novel training procedure, and demonstrate its effectiveness on the problem of generating 2 dimensional images, as well as 3 dimensional voxel representations of objects. Finally, looking at the transformer model, we note that there is a mismatch between the tasks used for pretraining and the downstream tasks models are fine tuned on, such as text classification.We hypothesize that this results in a redundancy in terms of unnecessary spatial information, and remove it through the introduction of learned sequence length bottlenecks. We aim to create task specific networks given a dataset and performance requirements through the use of a neural architecture search method and learned downsampling. We show that these task specific networks achieve superior performance in terms of inference latency and accuracy tradeoff to standard models without requiring additional pretraining

A Survey of Surface Reconstruction from Point Clouds

International audienceThe area of surface reconstruction has seen substantial progress in the past two decades. The traditional problem addressed by surface reconstruction is to recover the digital representation of a physical shape that has been scanned, where the scanned data contains a wide variety of defects. While much of the earlier work has been focused on reconstructing a piece-wise smooth representation of the original shape, recent work has taken on more specialized priors to address significantly challenging data imperfections, where the reconstruction can take on different representations – not necessarily the explicit geometry. We survey the field of surface reconstruction, and provide a categorization with respect to priors, data imperfections, and reconstruction output. By considering a holistic view of surface reconstruction, we show a detailed characterization of the field, highlight similarities between diverse reconstruction techniques, and provide directions for future work in surface reconstruction

On the power of message passing for learning on graph-structured data

This thesis proposes novel approaches for machine learning on irregularly structured input data such as graphs, point clouds and manifolds. Specifically, we are breaking up with the regularity restriction of conventional deep learning techniques, and propose solutions in designing, implementing and scaling up deep end-to-end representation learning on graph-structured data, known as Graph Neural Networks (GNNs). GNNs capture local graph structure and feature information by following a neural message passing scheme, in which node representations are recursively updated in a trainable and purely local fashion. In this thesis, we demonstrate the generality of message passing through a unified framework suitable for a wide range of operators and learning tasks. Specifically, we analyze the limitations and inherent weaknesses of GNNs and propose efficient solutions to overcome them, both theoretically and in practice, e.g., by conditioning messages via continuous B-spline kernels, by utilizing hierarchical message passing, or by leveraging positional encodings. In addition, we ensure that our proposed methods scale naturally to large input domains. In particular, we propose novel methods to fully eliminate the exponentially increasing dependency of nodes over layers inherent to message passing GNNs. Lastly, we introduce PyTorch Geometric, a deep learning library for implementing and working with graph-based neural network building blocks, built upon PyTorch

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition

International Conference on Continuous Optimization (ICCOPT) 2019 Conference Book

The Sixth International Conference on Continuous Optimization took place on the campus of the Technical University of Berlin, August 3-8, 2019. The ICCOPT is a flagship conference of the Mathematical Optimization Society (MOS), organized every three years. ICCOPT 2019 was hosted by the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) Berlin. It included a Summer School and a Conference with a series of plenary and semi-plenary talks, organized and contributed sessions, and poster sessions. This book comprises the full conference program. It contains, in particular, the scientific program in survey style as well as with all details, and information on the social program, the venue, special meetings, and more

Generalized averaged Gaussian quadrature and applications

A simple numerical method for constructing the optimal generalized averaged Gaussian quadrature formulas will be presented. These formulas exist in many cases in which real positive GaussKronrod formulas do not exist, and can be used as an adequate alternative in order to estimate the error of a Gaussian rule. We also investigate the conditions under which the optimal averaged Gaussian quadrature formulas and their truncated variants are internal