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Extracting color themes from an image is to get a color palette that consists of dominant colors of the image. In this article, we construct a color network to build the intrinsic connections of color information of pixels. By applying improved linear iterative clustering (SLIC) algorithm [1,2] , we obtain initial color themes. In the following stage, with learning from human-extract color themes, we can get the final sorted color themes result. Experimental results demonstrate that our model outperforms previous approaches in terms of the number of themes, span and accuracy. [ABSTRACT FROM AUTHOR]

Deep learning (DL)-based models especially Convolutional Neural Network (CNN) models have recently achieved great success in medical image classifications. It is usually time-consuming and labor-intensive to train a practical classification model due to the requirement of large data volume. Since medical images are more difficult to acquire and label for model training, many scholars have applied transfer learning by pre-training a model on a larger dataset and then fine-tuning it on the target dataset to obtain better classification results. However, such approach, relying on individual expertise to select related datasets, is subjective and inconsistent in performance. In this paper, we propose a simple yet effective method for measuring the transferability between different datasets, and build a Dataset Map (DataMap) that can be used as a tool to find the most relevant datasets for transfer learning on target dataset. Recent studies show that the convolutional kernels in CNN models have different function roles. Therefore, we adopt the similarity between the convolution kernels to measure the transferability between datasets. Firstly, the gradient attribution is adopted to attribute the task related convolution kernels from last few convolution layers of the same pre-trained model architecture trained with different datasets. Then, the similarity between attributed convolutional kernels is calculated to denote the transferability between different datasets. Finally, we build a DataMap with 20 medical image datasets. Extensive experimental tests on 3 mainstream CNN architectures show that the proposed method can effectively measure the transferability between different datasets. With the guidance of the DataMap, the transfer learning can achieve the best performance on various training tasks, and the accuracy of the CNN classifier can be improved by 1% to 5% through pre-training. • The first DataMap used to instruct transfer learning for medical image classificaton. • Adopt the similarity between the convolution kernels to measure the transferability. • With the guidance of DataMap, transfer learning can achieve the best performance. [ABSTRACT FROM AUTHOR]

With the wide-spread of smartphones, mobile phone screen has become an important IO device in HCI and its quality is of great matter in interaction. Traditional defect detection process involves heavy labor cost or relies on unstable low-level features and suffers from both scale and model sensitive problems. Screen defect varies in size, shape, intensity and is hard to be described. Efficient and accurate detection system remains an urgent need in mobile phone screen manufacturing. In this paper, we propose an end-to-end screen defect detection framework. A defect detection network with merging and splitting strategies (MSDDN) to deal with multiple size and shape variations of defect image patches is firstly designed. After training, feature maps of the last layer before the output of MSDDN can be regarded as good representations of a screen image patch. These feature maps are concatenated into a unified feature vector. We then train a recurrent neural network (SCN) to decide which input screen image patch in a sequence is the most likely to contain defects, where the patches are cropped from the same image, and the feature maps are used as input. As SCN emphasizes on the comparison of image patches from one image, it is less sensitive to different screen batches. The patch with the highest probability of containing defects, or called focus area, is further processed with a sliding window to fill in the MSDDN to produce the final results. Finally, to improve the efficiency of the calculation process to fulfill the real industrial demand, we perform both filter selection and weight quantization on the weights in MSDDN under the purpose of building a low-precision version network without great loss in accuracy (MSDDN-l). Experimental results show MSDDN can better handle the defect variations than traditional models and general purpose convolutional neural networks. Meanwhile, SCN can accurately predict the focus area, and MSDDN-l can greatly improve the efficiency. [ABSTRACT FROM AUTHOR]

Vision transformers have achieved remarkable success in computer vision tasks by using multi-head self-attention modules to capture long-range dependencies within images. However, the high inference computation cost poses a new challenge. Several methods have been proposed to address this problem, mainly by slimming patches. In the inference stage, these methods classify patches into two classes, one to keep and the other to discard in multiple layers. This approach results in additional computation at every layer where patches are discarded, which hinders inference acceleration. In this study, we tackle the patch slimming problem from a different perspective by proposing a life regression module that determines the lifespan of each image patch in one go. During inference, the patch is discarded once the current layer index exceeds its life. Our proposed method avoids additional computation and parameters in multiple layers to enhance inference speed while maintaining competitive performance. Additionally, our approach 1 1 https://github.com/cjwcommuny/life-regression. requires fewer training epochs than other patch slimming methods. [ABSTRACT FROM AUTHOR]