Joint Learning of Image Aesthetic Quality Assessment and Semantic Recognition Based on Feature Enhancement
- Resource Type
- Conference
- Authors
- Liu, Xiangfei; Nie, Xiushan; Shen, Zhen; Yin, Yilong
- Source
- ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2021 - 2021 IEEE International Conference on. :2075-2079 Jun, 2021
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Computing and Processing
Signal Processing and Analysis
Deep learning
Image recognition
Semantics
Speech recognition
Benchmark testing
Signal processing
Quality assessment
Image aesthetics quality assessment
semantics recognition
multi-task learning
- Language
- ISSN
- 2379-190X
Aesthetic quality assessment and semantic recognition are the two fundamental aspects of image perception and understanding tasks. Though these two tasks are related, most of the current research generally treats them as independent problems without any interaction. In this paper, we explore the relationships between aesthetic quality assessment and semantic recognition task, and employ a multi-task convolutional neural network with feature enhancement mechanism to effectively integrate these two tasks. A novel Enhanced Aggregation of Features Network (EAFNet) for joint learning of the two tasks is proposed to enhance the valid features and suppress the invalid features of each task in both channel and spatial dimensions. Experiments conducted on two benchmark datasets well verify the superior performance of EAFNet in handling aesthetic quality assessment and semantic recognition tasks.