We study classifiers operating under severe classification time constraints, corresponding to 1-1000 CPU microseconds, using Convolutional Tables Ensemble (CTE), an inherently fast architecture for object category recognition. The architecture is based on convolutionally-applied sparse feature extraction, using trees or ferns, and a linear voting layer. Several structure and optimization variants are considered, including novel decision functions, tree learning algorithm, and distillation from CNN to CTE architecture. Accuracy improvements of 24-45% over related art of similar speed are demonstrated on standard object recognition benchmarks. Using Pareto speed-accuracy curves, we show that CTE can provide better accuracy than Convolutional Neural Networks (CNN) for a certain range of classification time constraints, or alternatively provide similar error rates with 5-200X speedup.
We train generative 'up-convolutional' neural networks which are able to generate images of objects given object style, viewpoint, and color. We train the networks on rendered 3D models of chairs, tables, and cars. Our experiments show that the networks do not merely learn all images by heart, but rather find a meaningful representation of 3D models allowing them to assess the similarity of different models, interpolate between given views to generate the missing ones, extrapolate views, and invent new objects not present in the training set by recombining training instances, or even two diffe…
Deep Convolutional Neural Networks (DCNNs) have recently been applied successfully to a variety of vision and multimedia tasks, thus driving development of novel solutions in several application domains. Document analysis is a particularly promising area for DCNNs: indeed, the number of available digital documents has reached unprecedented levels, and humans are no longer able to discover and retrieve all the information contained in these documents without the help of automation. Under this scenario, DCNNs offers a viable solution to automate the information extraction process from digital do…
University of Bordeaux（波尔多大学）；CNRS（法国国家科学研究中心）；Bordeaux INP（波尔多国立高等物理、数学与信息学院）；University of La Rochelle（拉罗谢尔大学）
The paper addresses the problem of recognition of actions in video with low inter-class variability such as Table Tennis strokes. Two stream, "twin" convolutional neural networks are used with 3D convolutions both on RGB data and optical flow. Actions are recognized by classification of temporal windows. We introduce 3D attention modules and examine their impact on classification efficiency. In the context of the study of sportsmen performances, a corpus of the particular actions of table tennis strokes is considered. The use of attention blocks in the network speeds up the training step and i…
Online Streaming Video Super-Resolution with Convolutional Look-Up Table
Zhejiang University（浙江大学）；Tongji University（同济大学）；University of Science and Technology of China（中国科学技术大学）；Shanghai Jiao Tong University（上海交通大学）；Microsoft Research Asia（微软亚洲研究院）
3D convolutional networks is a good means to perform tasks such as video segmentation into coherent spatio-temporal chunks and classification of them with regard to a target taxonomy. In the chapter we are interested in the classification of continuous video takes with repeatable actions, such as strokes of table tennis. Filmed in a free marker less ecological environment, these videos represent a challenge from both segmentation and classification point of view. The 3D convnets are an efficient tool for solving these problems with window-based approaches.
intoPIX（intoPIX公司）；Université catholique de Louvain（鲁汶天主教大学）
Convolutional autoencoders are now at the forefront of image compression research. To improve their entropy coding, encoder output is typically analyzed with a second autoencoder to generate per-variable parametrized prior probability distributions. We instead propose a compression scheme that uses a single convolutional autoencoder and multiple learned prior distributions working as a competition of experts. Trained prior distributions are stored in a static table of cumulative distribution functions. During inference, this table is used by an entropy coder as a look-up-table to determine the…
Hector Research Institute of Education Sciences and Psychology, University of Tübingen（蒂宾根大学赫克托教育科学与心理学研究所）；University of Mannheim（曼海姆大学）
Tables on the web constitute a valuable data source for many applications, like factual search and knowledge base augmentation. However, as genuine tables containing relational knowledge only account for a small proportion of tables on the web, reliable genuine web table classification is a crucial first step of table extraction. Previous works usually rely on explicit feature construction from the HTML code. In contrast, we propose an approach for web table classification by exploiting the full visual appearance of a table, which works purely by applying a convolutional neural network on the …
European Organization for Nuclear Research (CERN)（欧洲核子研究组织）；Institute of Physics Belgrade（贝尔格莱德物理研究所）；Politecnico di Milano（米兰理工大学）；California Institute of Technology（加州理工学院）；Chalmers University of Technology（查尔姆斯理工大学）；Zenseact（Zenseact公司）；ICEPP, University of Tokyo（东京大学国际基本粒子物理研究所）；Columbia University（哥伦比亚大学）；University of California San Diego（加州大学圣地亚哥分校）；Massachusetts Institute of Technology（麻省理工学院）；Fermi National Accelerator Laboratory（费米国家加速器实验室）；Purdue University（普渡大学）；HawkEye360（鹰眼360公司）；University of Illinois at Chicago（伊利诺伊大学芝加哥分校）；Rhodes College（罗德学院）
We introduce an automated tool for deploying ultra low-latency, low-power deep neural networks with convolutional layers on FPGAs. By extending the hls4ml library, we demonstrate an inference latency of $5\,μ$s using convolutional architectures, targeting microsecond latency applications like those at the CERN Large Hadron Collider. Considering benchmark models trained on the Street View House Numbers Dataset, we demonstrate various methods for model compression in order to fit the computational constraints of a typical FPGA device used in trigger and data acquisition systems of particle detec…
Neural architecture search has attracted wide attentions in both academia and industry. To accelerate it, researchers proposed weight-sharing methods which first train a super-network to reuse computation among different operators, from which exponentially many sub-networks can be sampled and efficiently evaluated. These methods enjoy great advantages in terms of computational costs, but the sampled sub-networks are not guaranteed to be estimated precisely unless an individual training process is taken. This paper owes such inaccuracy to the inevitable mismatch between assembled network layers…
Northwestern University（西北大学）；Rehabilitation Institute of Chicago（芝加哥康复研究所）
We present a novel object detection pipeline for localization and recognition in three dimensional environments. Our approach makes use of an RGB-D sensor and combines state-of-the-art techniques from the robotics and computer vision communities to create a robust, real-time detection system. We focus specifically on solving the object detection problem for tabletop scenes, a common environment for assistive manipulators. Our detection pipeline locates objects in a point cloud representation of the scene. These clusters are subsequently used to compute a bounding box around each object in the …
University of Leipzig（莱比锡大学）；Max Planck Institute for Evolutionary Anthropology（马克斯·普朗克进化人类学研究所）
As participants of the MediaEval 2022 Sport Task, we propose a two-stream network approach for the classification and detection of table tennis strokes. Each stream is a succession of 3D Convolutional Neural Network (CNN) blocks using attention mechanisms. Each stream processes different 4D inputs. Our method utilizes raw RGB data and pose information computed from MMPose toolbox. The pose information is treated as an image by applying the pose either on a black background or on the original RGB frame it has been computed from. Best performance is obtained by feeding raw RGB data to one stream…
University of Washington（华盛顿大学）；Indian Institute of Technology Gandhinagar（印度理工学院甘地纳加尔分校）
Automated real-time prediction of the ergonomic risks of manipulating objects is a key unsolved challenge in developing effective human-robot collaboration systems for logistics and manufacturing applications. We present a foundational paradigm to address this challenge by formulating the problem as one of action segmentation from RGB-D camera videos. Spatial features are first learned using a deep convolutional model from the video frames, which are then fed sequentially to temporal convolutional networks to semantically segment the frames into a hierarchy of actions, which are either ergonom…
Over recent years, a myriad of novel convolutional network architectures have been developed to advance state-of-the-art performance on challenging recognition tasks. As computational resources improve, a great deal of effort has been placed in efficiently scaling up existing designs and generating new architectures with Neural Architecture Search (NAS) algorithms. While network topology has proven to be a critical factor for model performance, we show that significant gains are being left on the table by keeping topology static at inference-time. Due to challenges such as scale variation, we …
The Hong Kong Polytechnic University（香港理工大学）；DJI Innovations Co., Ltd（大疆创新科技有限公司）
Recent years have witnessed the increasing popularity of learning based methods to enhance the color and tone of photos. However, many existing photo enhancement methods either deliver unsatisfactory results or consume too much computational and memory resources, hindering their application to high-resolution images (usually with more than 12 megapixels) in practice. In this paper, we learn image-adaptive 3-dimensional lookup tables (3D LUTs) to achieve fast and robust photo enhancement. 3D LUTs are widely used for manipulating color and tone of photos, but they are usually manually tuned and …
Time Distributed Deep Learning Models for Purely Exogenous Forecasting: Application to Water Table Depth Prediction using Weather Image Time Series
Computer Science Department - University of Turin（都灵大学计算机科学系）；LISTIC Laboratory - Université Savoie Mont Blanc（萨瓦伊-蒙布莱大学LISTIC实验室）；Interuniversity Department of Regional and Urban Studies and Planning, Politecnico di Torino and University of Turin（都灵理工学院和都灵大学区域与城市研究与规划联合学院）
Pre-processing training data improves accuracy and generalisability of convolutional neural network based landscape semantic segmentation
Remote Sensing Research Centre, The University of Queensland（昆士兰大学遥感研究中心）；Applied Agricultural Remote Sensing Centre, The University of New England（新英格兰大学应用农业遥感中心）
Event-based Gaze Control System for Accurate Real-time Spin Estimation in Professional Ball Games
Sony AI, Tokyo, Japan（索尼AI，东京，日本）；Sony AI, Zürich, Switzerland（索尼AI，苏黎世，瑞士）；Sony Advanced Visual Sensing, Sony Europe Ltd., Zurich, Switzerland（索尼高级视觉感知，索尼欧洲有限公司，苏黎世，瑞士）
University of Oxford（牛津大学）；University College London（伦敦大学学院）；Niantic（尼安蒂克）
While learning models of intuitive physics is an increasingly active area of research, current approaches still fall short of natural intelligences in one important regard: they require external supervision, such as explicit access to physical states, at training and sometimes even at test times. Some authors have relaxed such requirements by supplementing the model with an handcrafted physical simulator. Still, the resulting methods are unable to automatically learn new complex environments and to understand physical interactions within them. In this work, we demonstrated for the first time l…
