23,129 research outputs found
TACOformer:Token-channel compounded Cross Attention for Multimodal Emotion Recognition
Recently, emotion recognition based on physiological signals has emerged as a
field with intensive research. The utilization of multi-modal, multi-channel
physiological signals has significantly improved the performance of emotion
recognition systems, due to their complementarity. However, effectively
integrating emotion-related semantic information from different modalities and
capturing inter-modal dependencies remains a challenging issue. Many existing
multimodal fusion methods ignore either token-to-token or channel-to-channel
correlations of multichannel signals from different modalities, which limits
the classification capability of the models to some extent. In this paper, we
propose a comprehensive perspective of multimodal fusion that integrates
channel-level and token-level cross-modal interactions. Specifically, we
introduce a unified cross attention module called Token-chAnnel COmpound (TACO)
Cross Attention to perform multimodal fusion, which simultaneously models
channel-level and token-level dependencies between modalities. Additionally, we
propose a 2D position encoding method to preserve information about the spatial
distribution of EEG signal channels, then we use two transformer encoders ahead
of the fusion module to capture long-term temporal dependencies from the EEG
signal and the peripheral physiological signal, respectively.
Subject-independent experiments on emotional dataset DEAP and Dreamer
demonstrate that the proposed model achieves state-of-the-art performance.Comment: Accepted by IJCAI 2023- AI4TS worksho
Fusion of Multispectral Data Through Illumination-aware Deep Neural Networks for Pedestrian Detection
Multispectral pedestrian detection has received extensive attention in recent
years as a promising solution to facilitate robust human target detection for
around-the-clock applications (e.g. security surveillance and autonomous
driving). In this paper, we demonstrate illumination information encoded in
multispectral images can be utilized to significantly boost performance of
pedestrian detection. A novel illumination-aware weighting mechanism is present
to accurately depict illumination condition of a scene. Such illumination
information is incorporated into two-stream deep convolutional neural networks
to learn multispectral human-related features under different illumination
conditions (daytime and nighttime). Moreover, we utilized illumination
information together with multispectral data to generate more accurate semantic
segmentation which are used to boost pedestrian detection accuracy. Putting all
of the pieces together, we present a powerful framework for multispectral
pedestrian detection based on multi-task learning of illumination-aware
pedestrian detection and semantic segmentation. Our proposed method is trained
end-to-end using a well-designed multi-task loss function and outperforms
state-of-the-art approaches on KAIST multispectral pedestrian dataset
Multispectral Deep Neural Networks for Pedestrian Detection
Multispectral pedestrian detection is essential for around-the-clock
applications, e.g., surveillance and autonomous driving. We deeply analyze
Faster R-CNN for multispectral pedestrian detection task and then model it into
a convolutional network (ConvNet) fusion problem. Further, we discover that
ConvNet-based pedestrian detectors trained by color or thermal images
separately provide complementary information in discriminating human instances.
Thus there is a large potential to improve pedestrian detection by using color
and thermal images in DNNs simultaneously. We carefully design four ConvNet
fusion architectures that integrate two-branch ConvNets on different DNNs
stages, all of which yield better performance compared with the baseline
detector. Our experimental results on KAIST pedestrian benchmark show that the
Halfway Fusion model that performs fusion on the middle-level convolutional
features outperforms the baseline method by 11% and yields a missing rate 3.5%
lower than the other proposed architectures.Comment: 13 pages, 8 figures, BMVC 2016 ora
Multi-View Deep Learning for Consistent Semantic Mapping with RGB-D Cameras
Visual scene understanding is an important capability that enables robots to
purposefully act in their environment. In this paper, we propose a novel
approach to object-class segmentation from multiple RGB-D views using deep
learning. We train a deep neural network to predict object-class semantics that
is consistent from several view points in a semi-supervised way. At test time,
the semantics predictions of our network can be fused more consistently in
semantic keyframe maps than predictions of a network trained on individual
views. We base our network architecture on a recent single-view deep learning
approach to RGB and depth fusion for semantic object-class segmentation and
enhance it with multi-scale loss minimization. We obtain the camera trajectory
using RGB-D SLAM and warp the predictions of RGB-D images into ground-truth
annotated frames in order to enforce multi-view consistency during training. At
test time, predictions from multiple views are fused into keyframes. We propose
and analyze several methods for enforcing multi-view consistency during
training and testing. We evaluate the benefit of multi-view consistency
training and demonstrate that pooling of deep features and fusion over multiple
views outperforms single-view baselines on the NYUDv2 benchmark for semantic
segmentation. Our end-to-end trained network achieves state-of-the-art
performance on the NYUDv2 dataset in single-view segmentation as well as
multi-view semantic fusion.Comment: the 2017 IEEE/RSJ International Conference on Intelligent Robots and
Systems (IROS 2017
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