浙大综述：多模态深度学习新进展与未来趋势

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随着深度学习技术的飞速发展，其在众多领域展现出了强大的潜力，尤其是在多模态深度学习（Multimodal Deep Learning, MMDL）的场景下。浙江大学的研究团队发布了一篇名为《Recent Advances and Trends in Multimodal Deep Learning: A Review》的综述论文，该文旨在全面探讨多模态深度学习的最新进展和趋势。论文指出，尽管单模态深度学习已经在诸如图像识别、自然语言处理等领域取得了显著成果，但它无法完全模拟人类学习过程中整合多元感知输入的能力。人类学习往往依赖于视觉、听觉、文本理解、语音识别、肢体动作和面部表情等多种感官的协同作用。因此，多模态学习的研究对于提升人工智能系统的智能水平至关重要。该综述论文涵盖了多种模态的信息处理，包括但不限于： 1. **图像**：图像数据在计算机视觉任务中扮演核心角色，如物体识别、场景理解等。通过融合图像特征，模型能够更好地理解和解析复杂环境。 2. **视频**：视频提供了动态信息，常用于行为识别、动作分析以及视频内容理解，如视频问答系统。 3. **文本**：自然语言处理中的关键元素，用于处理文本信息，进行情感分析、机器翻译、对话系统等。 4. **音频**：音频模态涉及语音识别、音乐分类、声纹识别等，利用声音信号解读人类意图和情感。 5. **肢体动作与面部表情**：这些非言语信息对于理解人类交互和情感至关重要，例如在虚拟现实或增强现实中的应用。 6. **生理信号**：如心率、脑电波等，有助于健康监测、情绪识别等生物信号处理任务。论文深入剖析了过去和当前的基线方法，并对近期多模态深度学习的突破性进展进行了详尽研究。它构建了一个精细的分类体系，以便更好地组织和理解不同模态间的融合策略和技术挑战。此外，文章还讨论了未来可能的研究方向和多模态深度学习的实际应用场景，如跨模态迁移学习、联合表示学习和多模态预训练模型等。这篇综述为研究人员和开发者提供了一个宝贵的指南，帮助他们紧跟多模态深度学习领域的前沿动态，推动人工智能技术向更深层次和全面的应用迈进。

Recent Advances and Trends in Multimodal Deep Learning: A Review • 7

Table 3. Comparative analysis of Image Description models. Where, EDID = Encoder-Decoder based Image Description,

SCID = Semantic Concept-based Image Description, and AID = Aention-based Image Description.

Paper Year Architecture Multimedia Dataset Evaluation Metrics

J. Wu et al. [159] 2017 CNN/VGG16-InceptionV3,

Stacked GRU

Image, Text MS-COCO BLEU, CIDEr, METEOR

R. Hu et al. [60] 2017 Faster RCNN/VGG16,

RNN/BLSTM

Image, Text Visual Genome, Google-Ref Top-1 precision (P@1) metric

EDID L. Guo et al. [46] 2019 Deep CNN, GAN, RNN/LSTM,

GRU

Image, Text FlickrStyle10K, SentiCap, MS-

COCO

BLEU, CIDEr, METEOR, PPLX

X. He et al [54] 2019 CNN/VGG16, RNN/LSTM Image, Text Flickr30k, MS-COCO BLEU, CIDEr, METEOR

Y. Feng et al. [37] 2019 CNN/InceptionV4, RNN/LSTM Image, Text MS-COCO BLEU, ROUGE, CIDEr, METEOR,

SPICE

W. Wang et al. [154] 2018 CNN/VGG16, RNN/LSTM Image,Text MS-COCO BLEU, CIDEr, METEOR

SCID P. Cao et al. [22] 2019 CNN/VGG16, RNN/BLSTM Image, Text Flickr8k, MS-COCO BLEU, CIDEr, METEOR

L. Cheng et al. [29] 2020 Faster-RCNN, RNN/LSTM Image, Text MS-COCO BLEU, SPICE, METEOR, CIDEr,

ROUGE

L. Li et al. [80] 2017 CNN/VGG16-Faster RCNN,

RNN/LSTM

Image, Text Flickr8K, Flickr30K, MS-COCO METEOR, ROUGE

𝐿

, CIDEr,

BLEU

P. Anderson et al. [7] 2018 Faster RCNN/ResNet101,

RNN/LSTM, GRU

Image, Text Visual Genome Dataset, MS-COCO,

VQA v2.0

BLEU, METEOR, CIDEr, SPICE,

ROUGE

M. Liu et al. [86] 2020 CNN/InceptionV4, RNN/LSTM Image, Text Flickr8k-CN, Flickr8k-CN, AIC-ICC BLEU, ROUGE, CIDEr, METEOR

AID M. Liu et al. [87] 2020 CNN/InceptionV4, RNN/LSTM Image, Text AIC-ICC BLEU, ROUGE, CIDEr, METEOR

B. Wang et al. [150] 2020 CNN/InceptionV4, RNN/LSTM Image, Text Flickr8K, Flickr8k-CN BLEU, ROUGE, CIDEr, METEOR

Y. Wei et al. [158] 2020 GAN, RNN/LSTM Image, Text MS-COCO BLEU, METEOR, CIDEr, SPICE,

ROUGE

LU Jiasen et al. [68] 2020 CNN/ResNet, RNN/LSTM Image, Text Flickr30K, MS-COCO BLEU, ROUGE, CIDEr, METEOR

generation. B. Wang et al. [

150

] proposed an E2E-DL approach for image description using a semantic attention

mechanism. In this approach, features are extracted from specic image regions using an attention mechanism for

producing corresponding descriptions. This approach can transform English language knowledge representations

into the Chinese language to get cross-lingual image description. Y. Wei et al. [

158

] proposed an image description

framework by using multi attention mechanism to extract local and non-local feature representations. LU Jiasen

et al. [

] proposed an adaptive attention mechanism based image description model. Attention mechanism

merges visual features extracted from the image by CNN architecture and linguistic features by LSTM architecture.

During the deep learning era (2010 to date), many authors contributed a lot by proposing various techniques

to describe the visual contents of an image in the domain of image description. Dierent image description

approaches described in section 3.1.1, 3.1.2, and 3.1.3 are analyzed comparatively according to architectures,

multimedia, publication year, datasets, and evaluation metrics in Table 3. The architectures used in these proposed

techniques are explained briey in section 4. Similarly, datasets and evaluation metrics are discussed in sections

5 and 6, respectively.

3.2 Multimodal Video Description:

Like image Description, video description is used to generate a textual description of visual contents provided

through input video. It has various applications in video subtitling, visually impaired videos, video surveillance,

sign language video description, and human-robot interaction. Advancements in this eld open up many oppor-

tunities in various application domains. During this process, mainly two types of modalities are used, i.e., video

stream and text. The general structure diagram of the video description is shown in Figures 3 (a) & (b) and 4.

During the deep learning era, many authors contribute to video description by using various methods. At the

start of this DL era, some classical and statistical video description approaches are proposed based on Subject,

Object, Verb (SVO) tuple methods [

]. These SVO tuple-based methods laid the foundation for the description of

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