Deep multimodal fusion for semantic image segmentation: A survey - Université d'Évry Access content directly
Journal Articles Image and Vision Computing Year : 2021

Deep multimodal fusion for semantic image segmentation: A survey


Recent advances in deep learning have shown excellent performance in various scene understanding tasks. However, in some complex environments or under challenging conditions, it is necessary to employ multiple modalities that provide complementary information on the same scene. A variety of studies have demonstrated that deep multimodal fusion for semantic image segmentation achieves significant performance improvement. These fusion approaches take the benefits of multiple information sources and generate an optimal joint prediction automatically. This paper describes the essential background concepts of deep multimodal fusion and the relevant applications in computer vision. In particular, we provide a systematic survey of multimodal fusion methodolo-gies, multimodal segmentation datasets, and quantitative evaluations on the benchmark datasets. Existing fusion methods are summarized according to a common taxonomy: early fusion, late fusion, and hybrid fusion. Based on their performance, we analyze the strengths and weaknesses of different fusion strategies. Current challenges and design choices are discussed, aiming to provide the reader with a comprehensive and heuristic view of deep multimodal image seg-mentation.
Fichier principal
Vignette du fichier
Deep_Multimodal_Fusion_for_Semantic_Image_Segmentation__A_Survey.pdf (5.16 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-02963619 , version 1 (10-10-2020)



Yifei Zhang, Désiré Sidibé, Olivier Morel, Fabrice Mériaudeau. Deep multimodal fusion for semantic image segmentation: A survey. Image and Vision Computing, 2021, 105, pp.104042. ⟨10.1016/j.imavis.2020.104042⟩. ⟨hal-02963619⟩
403 View
4126 Download



Gmail Facebook X LinkedIn More