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Recognizing Multiple Ingredients in Food Images Using a Single-Ingredient Classification Model
The method localizes the candidate regions of the ingredients using the locating and sliding window techniques.
Exploring CNN-based models for image's aesthetic score prediction with using ensemble
Moreover, the attention regions of the models to the images are extracted to analyze the consistency with the subjects in the images.
Exploring to establish an appropriate model for image aesthetic assessment via CNN-based RSRL: An empirical study
The experimental results show that the FD measure is effective for establishing the appropriate model from the multiple score prediction models with different CNN structures.
CNN-based Repetitive self-revised learning for photos' aesthetics imbalanced classification
In order to realize the auto-assessment of photo aesthetics, we focus on using repetitive self-revised learning (RSRL) to train the CNN-based aesthetics classification network by imbalanced data set.
Sample-specific repetitive learning for photo aesthetic assessment and highlight region extraction
In order to realize the auto-assessment of photo aesthetics, we focus on retraining the CNN-based aesthetic assessment model by dropping out the unavailable samples in the middle levels from the training data set repetitively to overcome the effect of imbalanced aesthetic data on classification.
