62 research outputs found
Attention Mechanisms for Object Recognition with Event-Based Cameras
Event-based cameras are neuromorphic sensors capable of efficiently encoding
visual information in the form of sparse sequences of events. Being
biologically inspired, they are commonly used to exploit some of the
computational and power consumption benefits of biological vision. In this
paper we focus on a specific feature of vision: visual attention. We propose
two attentive models for event based vision: an algorithm that tracks events
activity within the field of view to locate regions of interest and a
fully-differentiable attention procedure based on DRAW neural model. We
highlight the strengths and weaknesses of the proposed methods on four
datasets, the Shifted N-MNIST, Shifted MNIST-DVS, CIFAR10-DVS and N-Caltech101
collections, using the Phased LSTM recognition network as a baseline reference
model obtaining improvements in terms of both translation and scale invariance.Comment: WACV2019 camera-ready submissio
The Global Online Freedom Act: A Critique of Its Objectives, Methods, and Ultimate Effectiveness Combating American Businesses That Facilitate Internet Censorship in the People\u27s Republic of China
Spatial Temporal Transformer Network for Skeleton-based Action Recognition
Skeleton-based human action recognition has achieved a great interest in
recent years, as skeleton data has been demonstrated to be robust to
illumination changes, body scales, dynamic camera views, and complex
background. Nevertheless, an effective encoding of the latent information
underlying the 3D skeleton is still an open problem. In this work, we propose a
novel Spatial-Temporal Transformer network (ST-TR) which models dependencies
between joints using the Transformer self-attention operator. In our ST-TR
model, a Spatial Self-Attention module (SSA) is used to understand intra-frame
interactions between different body parts, and a Temporal Self-Attention module
(TSA) to model inter-frame correlations. The two are combined in a two-stream
network which outperforms state-of-the-art models using the same input data on
both NTU-RGB+D 60 and NTU-RGB+D 120.Comment: Accepted as ICPRW2020 (FBE2020, Workshop on Facial and Body
Expressions, micro-expressions and behavior recognition) 8 pages, 2 figures.
arXiv admin note: substantial text overlap with arXiv:2008.0740
Spatial Temporal Transformer Network for Skeleton-Based Action Recognition
Skeleton-based human action recognition has achieved a great interest in recent years, as skeleton data has been demonstrated to be robust to illumination changes, body scales, dynamic camera views, and complex background. Nevertheless, an effective encoding of the latent information underlying the 3D skeleton is still an open problem. In this work, we propose a novel Spatial-Temporal Transformer network (ST-TR) which models dependencies between joints using the Transformer self-attention operator. In our ST-TR model, a Spatial Self-Attention module (SSA) is used to understand intra-frame interactions between different body parts, and a Temporal Self-Attention module (TSA) to model inter-frame correlations. The two are combined in a two-stream network which outperforms state-of-the-art models using the same input data on both NTU-RGB+D 60 and NTU-RGB+D 120
Asynchronous Convolutional Networks for Object Detection in Neuromorphic Cameras
Event-based cameras, also known as neuromorphic cameras, are bioinspired
sensors able to perceive changes in the scene at high frequency with low power
consumption. Becoming available only very recently, a limited amount of work
addresses object detection on these devices. In this paper we propose two
neural networks architectures for object detection: YOLE, which integrates the
events into surfaces and uses a frame-based model to process them, and fcYOLE,
an asynchronous event-based fully convolutional network which uses a novel and
general formalization of the convolutional and max pooling layers to exploit
the sparsity of camera events. We evaluate the algorithm with different
extensions of publicly available datasets and on a novel synthetic dataset.Comment: accepted at CVPR2019 Event-based Vision Worksho
A 5-Point Minimal Solver for Event Camera Relative Motion Estimation
Event-based cameras are ideal for line-based motion estimation, since they predominantly respond to edges in the scene. However, accurately determining the camera displacement based on events continues to be an open problem. This is because line feature extraction and dynamics estimation are tightly coupled when using event cameras, and no precise model is currently available for describing the complex structures generated by lines in the space-time volume of events. We solve this problem by deriving the correct non-linear parametrization of such manifolds, which we term eventails, and demonstrate its application to eventbased linear motion estimation, with known rotation from an Inertial Measurement Unit. Using this parametrization, we introduce a novel minimal 5-point solver that jointly estimates line parameters and linear camera velocity projections, which can be fused into a single, averaged linear velocity when considering multiple lines. We demonstrate on both synthetic and real data that our solver generates more stable relative motion estimates than other methods while capturing more inliers than clustering based on spatiotemporal planes. In particular, our method consistently achieves a 100% success rate in estimating linear velocity where existing closed-form solvers only achieve between 23% and 70%. The proposed eventails contribute to a better understanding of spatio-temporal event-generated geometries and we thus believe it will become a core building block of future event-based motion estimation algorithms
Recommended from our members
A Comparison of the MMPI, Faschingbauer's Abbreviated MMPI and the MMPI-168 with Selected Medical Patients and Medical School Applicants
The Minnesota Multiphasic Personality Inventory (MMPI) is often used for evaluating candidates for gastric bypass surgery, chronic pain patients, head trauma victims, and medical school applicants. However, due to the considerable time involved in completing and scoring the standard MMPI, researchers have attempted to devise short versions of this instrument to reduce the time required while providing similar results. In recent years, the Faschingbauer Abbreviated MMPI (FAM) and the MMPI-16 8 have been proposed as viable MMPI substitutes. The present study examined the comparability between profiles using these short versions of the MMPI with the patterns obtained using the entire measure. Participants consisted of equal numbers of gastric bypass candidates, chronic pain patients, head trauma victims, and medical school applicants. Scores on the FAM tended to be similar to scores on the complete MMPI for gastric bypass, chronic pain and head trauma patients. In contrast, the MMPI-16 8 yielded profiles which were similar to complete MMPI profiles with chronic pain and head trauma patients
The Global Online Freedom Act: A Critique of Its Objectives, Methods, and Ultimate Effectiveness Combating American Businesses That Facilitate Internet Censorship in the People\u27s Republic of China
- …