基于深度信息和RGB图像的行为识别算法

人体行为识别是计算机视觉领域的一个热点问题, 然而目前大部分算法都是仅使用RGB或深度视频序列, 很少将它们结合起来进行行为识别。由于它们都有各自的优点且信息是互补的, 因此文中研究深度图像和RGB图像的特性, 不仅提出两种鲁棒的深度图和RGB图像上的行为描述算法, 而且将它们有效融合, 进一步结合多个不同核函数的SVM分类器在具有挑战性的DHA数据集上对它们进行评估。大规模实验结果表明, 文中提出的行为描述算法性能比一些最具代表性算法的性能更好。同时, 深度数据和RGB图像融合后算法性能得到进一步提高, 比单独使用深度数据或RGB图像的性能更好, 且具有较好的区分性和鲁棒性。     

多文化场景下的多模态情感识别

自动情感识别是一个非常具有挑战性的课题,并且有着广泛的应用价值.本文探讨了在多文化场景下的多模态情感识别问题.我们从语音声学和面部表情等模态分别提取了不同的情感特征,包括传统的手工定制特征和基于深度学习的特征,并通过多模态融合方法结合不同的模态,比较不同单模态特征和多模态特征融合的情感识别性能.我们在CHEAVD中文多模态情感数据集和AFEW英文多模态情感数据集进行实验,通过跨文化情感识别研究,我们验证了文化因素对于情感识别的重要影响,并提出3种训练策略提高在多文化场景下情感识别的性能,包括：分文化选择模型、多文化联合训练以及基于共同情感空间的多文化联合训练,其中基于共同情感空间的多文化联合训练通过将文化影响与情感特征分离,在语音和多模态情感识别中均取得最好的识别效果.     

多模态数据的行为识别综述

行为识别是当前计算机视觉方向中视频理解领域的重要研究课题。从视频中准确提取人体动作的特征并识别动作,能为医疗、安防等领域提供重要的信息,是一个十分具有前景的方向。本文从数据驱动的角度出发,全面介绍了行为识别技术的研究发展,对具有代表性的行为识别方法或模型进行了系统阐述。行为识别的数据分为RGB模态数据、深度模态数据、骨骼模态数据以及融合模态数据。首先介绍了行为识别的主要过程和人类行为识别领域不同数据模态的公开数据集;然后根据数据模态分类,回顾了RGB模态、深度模态和骨骼模态下基于传统手工特征和深度学习的行为识别方法,以及多模态融合分类下RGB模态与深度模态融合的方法和其他模态融合的方法。传统手工特征法包括基于时空体积和时空兴趣点的方法（RGB模态）、基于运动变化和外观的方法（深度模态）以及基于骨骼特征的方法（骨骼模态）等;深度学习方法主要涉及卷积网络、图卷积网络和混合网络,重点介绍了其改进点、特点以及模型的创新点。基于不同模态的数据集分类进行不同行为识别技术的对比分析。通过类别内部和类别之间两个角度对比分析后,得出不同模态的优缺点与适用场景、手工特征法与深度学习法的区别和融合多模态的优势。最后,总结了行为识别技术当前面临的问题和挑战,并基于数据模态的角度提出了未来可行的研究方向和研究重点。     

Cyclic Autoencoder for Multimodal Data Alignment Using Custom Datasets

The subtitle recognition under multimodal data fusion in this paper aims to recognize text lines from image and audio data. Most existing multimodal fusion methods tend to be associated with pre-fusion as well as post-fusion, which is not reasonable and difficult to interpret. We believe that fusing images and audio before the decision layer, i.e., intermediate fusion, to take advantage of the complementary multimodal data, will benefit text line recognition. To this end, we propose: (i) a novel cyclic autoencoder based on convolutional neural network. The feature dimensions of the two modal data are aligned under the premise of stabilizing the compressed image features, thus the high-dimensional features of different modal data are fused at the shallow level of the model. (ii) A residual attention mechanism that helps us improve the performance of the recognition. Regions of interest in the image are enhanced and regions of disinterest are weakened, thus we can extract the features of the text regions without further increasing the depth of the model (iii) a fully convolutional network for video subtitle recognition. We choose DenseNet-121 as the backbone network for feature extraction, which effectively enabling the recognition of video subtitles in complex backgrounds. The experiments are performed on our custom datasets, and the automatic and manual evaluation results show that our method reaches the state-of-the-art.     

面向听视觉信息的多模态人格识别研究进展

人格识别分析是人格计算研究中一个重要的研究内容,在人类行为分析、人工智能、人机交互、个性化推荐等方面具有重要的应用价值,是近年来心理学、认知学、计算机科学等领域中的一个多学科交叉的热点研究课题。本文介绍了与人格识别相关的各种人格类型表示理论和人格识别数据库,阐述了面向听视觉信息的各种听视觉人格特征提取技术,如手工特征和深度特征,并在此基础上对面向听视觉信息人格识别的多模态融合方法做了详细的分类和归纳,最后概括了面向听视觉信息的多模态人格识别发展趋势,并进行了展望。     

Robust Sensor Fusion: Analysis and Application to Audio Visual Speech Recognition

This paper analyzes the issue of catastrophic fusion, a problem that occurs in multimodal recognition systems that integrate the output from several modules while working in non-stationary environments. For concreteness we frame the analysis with regard to the problem of automatic audio visual speech recognition (AVSR), but the issues at hand are very general and arise in multimodal recognition systems which need to work in a wide variety of contexts. Catastrophic fusion is said to have occurred when the performance of a multimodal system is inferior to the performance of some isolated modules, e.g., when the performance of the audio visual speech recognition system is inferior to that of the audio system alone. Catastrophic fusion arises because recognition modules make implicit assumptions and thus operate correctly only within a certain context. Practice shows that when modules are tested in contexts inconsistent with their assumptions, their influence on the fused product tends to increase, with catastrophic results. We propose a principled solution to this problem based upon Bayesian ideas of competitive models and inference robustification. We study the approach analytically on a classic Gaussian discrimination task and then apply it to a realistic problem on audio visual speech recognition (AVSR) with excellent results.     

多模式汉语连续语音识别中视觉特征的提取和应用

本文对在汉语多模式汉语语音识别系统中利用视觉特征进行了研究,给出了基于多流隐马尔科夫模型(Multi-stream HMM, MSHMM)的听视觉融合方案,并对有关视觉特征的两项关键技术:嘴唇定位和视觉特征提取进行了详细讨论。首先,我们研究了基于模板匹配的嘴唇跟踪方法;然后研究了基于线性变换的低级视觉特征,并与基于动态形状模型的特征作了比较;实验结果表明,引入视觉信息后无噪环境下语音识别声学层首选错误率相对下降36.09%,在噪声环境下的鲁棒性也有明显提高。     

Seeing Human Weight from a Single RGB-D Image

Human weight estimation is useful in a variety of potential applications, e.g., targeted advertisement, entertainment scenarios and forensic science. However, estimating weight only from color cues is particularly challenging since these cues are quite sensitive to lighting and imaging conditions. In this article, we propose a novel weight estimator based on a single RGB-D image, which utilizes the visual color cues and depth information. Our main contributions are three-fold.First, we construct the W8-RGBD dataset including RGB-D images of different people with ground truth weight. Second,the novel sideview shape feature and the feature fusion model are proposed to facilitate weight estimation. Additionally, we consider gender as another important factor for human weight estimation. Third, we conduct comprehensive experiments using various regression models and feature fusion models on the new weight dataset, and encouraging results are obtained based on the proposed features and models.     

LMR-CBT: learning modality-fused representations with CB-Transformer for multimodal emotion recognition from unaligned multimodal sequences

Learning modality-fused representations and processing unaligned multimodal sequences are meaningful and challenging in multimodal emotion recognition. Existing approaches use directional pairwise attention or a message hub to fuse language, visual, and audio modalities. However, these fusion methods are often quadratic in complexity with respect to the modal sequence length, bring redundant information and are not efficient. In this paper, we propose an efficient neural network to learn modality-fused representations with CB-Transformer (LMR-CBT) for multimodal emotion recognition from unaligned multi-modal sequences. Specifically, we first perform feature extraction for the three modalities respectively to obtain the local structure of the sequences. Then, we design an innovative asymmetric transformer with cross-modal blocks (CB-Transformer) that enables complementary learning of different modalities, mainly divided into local temporal learning, cross-modal feature fusion and global self-attention representations. In addition, we splice the fused features with the original features to classify the emotions of the sequences. Finally, we conduct word-aligned and unaligned experiments on three challenging datasets, IEMOCAP, CMU-MOSI, and CMU-MOSEI. The experimental results show the superiority and efficiency of our proposed method in both settings. Compared with the mainstream methods, our approach reaches the state-of-the-art with a minimum number of parameters.     

基于注意力感知和语义感知的RGB-D室内图像语义分割算法

近年来,全卷积神经网络有效提升了语义分割任务的准确率.然而,由于室内环境的复杂性,室内场景语义分割仍然是一个具有挑战性的问题.随着深度传感器的出现,人们开始考虑利用深度信息提升语义分割效果.以往的研究大多简单地使用等权值的拼接或求和操作来融合RGB特征和深度特征,未能充分利用RGB特征与深度特征之间的互补信息.本文提出...     

基于RGB-D视频的多模态手势识别

本文是对SKIG RGB-D多模态的孤立手势视频进行手势识别研究.首先将RGB和Depth两种单模态视频提取成图片的形式保存,然后采样成长度为32帧的手势序列分别输入到本文提出的稠密连接的3DCNN组件学习短期的时空域特征,然后将提取的时空域特征输入到卷积GRU网络进行长期的时空域特征学习,最终对单模态训练好的网络进行多模态融合,提升网络识别准确率.本文在SKIG数据集上取得了99.07%的识别准确率,达到了极高的准确率,证明了本文提出的网络模型的有效性.     

融合动作特征的多模态情绪识别

近年来,利用计算机技术实现基于多模态数据的情绪识别成为自然人机交互和人工智能领域重要 的研究方向之一。利用视觉模态信息的情绪识别工作通常都将重点放在脸部特征上,很少考虑动作特征以及融合 动作特征的多模态特征。虽然动作与情绪之间有着紧密的联系,但是从视觉模态中提取有效的动作信息用于情绪 识别的难度较大。以动作与情绪的关系作为出发点,在经典的 MELD 多模态情绪识别数据集中引入视觉模态的 动作数据,采用 ST-GCN 网络模型提取肢体动作特征,并利用该特征实现基于 LSTM 网络模型的单模态情绪识别。 进一步在 MELD 数据集文本特征和音频特征的基础上引入肢体动作特征,提升了基于 LSTM 网络融合模型的多 模态情绪识别准确率,并且结合文本特征和肢体动作特征提升了上下文记忆模型的文本单模态情绪识别准确率, 实验显示虽然肢体动作特征用于单模态情绪识别的准确度无法超越传统的文本特征和音频特征,但是该特征对于 多模态情绪识别具有重要作用。基于单模态和多模态特征的情绪识别实验验证了人体动作中含有情绪信息,利用 肢体动作特征实现多模态情绪识别具有重要的发展潜力。     

基于遥感图像的多模态小目标检测

由于遥感图像目标往往较小且容易受光线、天气等因素的影响,所以单一模态下基于深度学习的遥感图像目标检测的准确度较低.然而,不同模态间的图像信息可以相互增强提高目标检测的性能.因此,基于RGB和红外图像,提出了一种适用于遥感图像多模态小目标检测的平衡多模态深度模型.相比简单地相加、点乘和拼接的方式融合2个模态的特征信息,设...     

基于多尺度特征迁移学习的步态识别研究

为了解决行人步态数据集样本量较少、单特征或多特征融合的步态识别算法特征描述不足的问题,提出了一种基于多尺度特征深度迁移学习的行人步态识别方法。该算法步骤包括：改进VGG-16网络,去除网络中最后一个最大池化层（Maxpool Layer）,融合空间金字塔池化网络结构（SPP）获取行人步态能量图（GEI）的多尺度信息,利用Imagenet数据集预训练此网络模型,将提取特征能力迁移至行人步态识别网络模型中,采用行人步态样本集微调网络,修改网络中的全连接层参数,应用于行人步态识别研究。该方法在中科院自动化研究所的CASIA-B步态数据集上的识别精度达到了95.7%,与单一步态特征的步态识别方法以及融合多种步态特征的识别方法相比,步态识别率有了明显提升,表明该方法有更好的识别性能。     

A review of affective computing: From unimodal analysis to multimodal fusion

Affective computing is an emerging interdisciplinary research field bringing together researchers and practitioners from various fields, ranging from artificial intelligence, natural language processing, to cognitive and social sciences. With the proliferation of videos posted online (e.g., on YouTube, Facebook, Twitter) for product reviews, movie reviews, political views, and more, affective computing research has increasingly evolved from conventional unimodal analysis to more complex forms of multimodal analysis. This is the primary motivation behind our first of its kind, comprehensive literature review of the diverse field of affective computing. Furthermore, existing literature surveys lack a detailed discussion of state of the art in multimodal affect analysis frameworks, which this review aims to address. Multimodality is defined by the presence of more than one modality or channel, e.g., visual, audio, text, gestures, and eye gage. In this paper, we focus mainly on the use of audio, visual and text information for multimodal affect analysis, since around 90% of the relevant literature appears to cover these three modalities. Following an overview of different techniques for unimodal affect analysis, we outline existing methods for fusing information from different modalities. As part of this review, we carry out an extensive study of different categories of state-of-the-art fusion techniques, followed by a critical analysis of potential performance improvements with multimodal analysis compared to unimodal analysis. A comprehensive overview of these two complementary fields aims to form the building blocks for readers, to better understand this challenging and exciting research field.     

A systematic review on affective computing: emotion models,databases, and recent advances

Affective computing conjoins the research topics of emotion recognition and sentiment analysis, and can be realized with unimodal or multimodal data, consisting primarily of physical information (e.g., text, audio, and visual) and physiological signals (e.g., EEG and ECG). Physical-based affect recognition caters to more researchers due to the availability of multiple public databases, but it is challenging to reveal one's inner emotion hidden purposefully from facial expressions, audio tones, body gestures, etc. Physiological signals can generate more precise and reliable emotional results; yet, the difficulty in acquiring these signals hinders their practical application. Besides, by fusing physical information and physiological signals, useful features of emotional states can be obtained to enhance the performance of affective computing models. While existing reviews focus on one specific aspect of affective computing, we provide a systematical survey of important components: emotion models, databases, and recent advances. Firstly, we introduce two typical emotion models followed by five kinds of commonly used databases for affective computing. Next, we survey and taxonomize state-of-the-art unimodal affect recognition and multimodal affective analysis in terms of their detailed architectures and performances. Finally, we discuss some critical aspects of affective computing and its applications and conclude this review by pointing out some of the most promising future directions, such as the establishment of benchmark database and fusion strategies. The overarching goal of this systematic review is to help academic and industrial researchers understand the recent advances as well as new developments in this fast-paced, high-impact domain.     

Audio-visual emotion fusion (AVEF): A deep efficient weighted approach

The multi-modal emotion recognition lacks the explicit mapping relation between emotion state and audio and image features, so extracting the effective emotion information from the audio/visual data is always a challenging issue. In addition, the modeling of noise and data redundancy is not solved well, so that the emotion recognition model is often confronted with the problem of low efficiency. The deep neural network (DNN) performs excellently in the aspects of feature extraction and highly non-linear feature fusion, and the cross-modal noise modeling has great potential in solving the data pollution and data redundancy. Inspired by these, our paper proposes a deep weighted fusion method for audio-visual emotion recognition. Firstly, we conduct the cross-modal noise modeling for the audio and video data, which eliminates most of the data pollution in the audio channel and the data redundancy in visual channel. The noise modeling is implemented by the voice activity detection(VAD), and the data redundancy in the visual data is solved through aligning the speech area both in audio and visual data. Then, we extract the audio emotion features and visual expression features via two feature extractors. The audio emotion feature extractor, audio-net, is a 2D CNN, which accepting the image-based Mel-spectrograms as input data. On the other hand, the facial expression feature extractor, visual-net, is a 3D CNN to which facial expression image sequence is feeded. To train the two convolutional neural networks on the small data set efficiently, we adopt the strategy of transfer learning. Next, we employ the deep belief network(DBN) for highly non-linear fusion of multi-modal emotion features. We train the feature extractors and the fusion network synchronously. And finally the emotion classification is obtained by the support vector machine using the output of the fusion network. With consideration of cross-modal feature fusion, denoising and redundancy removing, our fusion method show excellent performance on the selected data set.     

Discovering joint audio–visual codewords for video event detection

Detecting complex events in videos is intrinsically a multimodal problem since both audio and visual channels provide important clues. While conventional methods fuse both modalities at a superficial level, in this paper we propose a new representation—called bi-modal words—to explore representative joint audio–visual patterns. We first build a bipartite graph to model relation across the quantized words extracted from the visual and audio modalities. Partitioning over the bipartite graph is then applied to produce the bi-modal words that reveal the joint patterns across modalities. Different pooling strategies are then employed to re-quantize the visual and audio words into the bi-modal words and form bi-modal Bag-of-Words representations. Since it is difficult to predict the suitable number of bi-modal words, we generate bi-modal words at different levels (i.e., codebooks with different sizes), and use multiple kernel learning to combine the resulting multiple representations during event classifier learning. Experimental results on three popular datasets show that the proposed method achieves statistically significant performance gains over methods using individual visual and audio feature alone and existing popular multi-modal fusion methods. We also find that average pooling is particularly suitable for bi-modal representation, and using multiple kernel learning to combine multi-modal representations at various granularities is helpful.