Visual Attention: Deep Rare Features
This addresses the challenge of generic visual attention modeling for applications in image analysis, though it appears incremental as it hybridizes existing approaches.
The paper tackles the problem of DNNs being inefficient at detecting surprising or unusual data in images due to low occurrence probability, and proposes DeepRare2019, which combines DNN feature extraction with feature-engineered algorithms, achieving top-3 performance on three eye-tracking datasets without training and in under a second per image on CPU.
Human visual system is modeled in engineering field providing feature-engineered methods which detect contrasted/surprising/unusual data into images. This data is "interesting" for humans and leads to numerous applications. Deep learning (DNNs) drastically improved the algorithms efficiency on the main benchmark datasets. However, DNN-based models are counter-intuitive: surprising or unusual data is by definition difficult to learn because of its low occurrence probability. In reality, DNNs models mainly learn top-down features such as faces, text, people, or animals which usually attract human attention, but they have low efficiency in extracting surprising or unusual data in the images. In this paper, we propose a model called DeepRare2019 (DR) which uses the power of DNNs feature extraction and the genericity of feature-engineered algorithms. DR 1) does not need any training, 2) it takes less than a second per image on CPU only and 3) our tests on three very different eye-tracking datasets show that DR is generic and is always in the top-3 models on all datasets and metrics while no other model exhibits such a regularity and genericity. DeepRare2019 code can be found at https://github.com/numediart/VisualAttention-RareFamily