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Compared with typically developed people, patients with autism spectrum disorder (ASD) have many differences, including visual attention. Recently, researchers have employed saliency prediction to conduct early diagnosis for patients with ASD by observing whether the fixation maps of the patients are consistent with prediction results. Though numerous saliency models have been designed, which promotes the performance of saliency prediction significantly, the efforts major in atypical attention prediction are fewer. Therefore, we propose a novel atypical saliency model, namely attention-guided dual-branch integration network, to perform atypical visual saliency prediction. Generally, our model is a U-shaped architecture including an encoder and a decoder. In the encoder, we first deploy the dual-branch fusion module to extract multiscale deep features, where the dual branches consist of a coarse branch and a fine branch. Particularly, the multiscale features of each branch are integrated in a bilateral way. Then, we employ the multiattention module consisting of dilated convolution, channel attention, and spatial attention to further elevate the high-level semantic cues. Particularly, the attention units are constructed in a cascaded way. Besides, to give an effective representation for the differences between atypical saliency estimation and the typical saliency estimation, we design a new discriminative mask to compute the loss function. Extensive experiments are performed on challenging datasets, and the results show the superiority and effectiveness of our atypical saliency model when compared with the state-of-the-art saliency prediction models. [ABSTRACT FROM AUTHOR]

With the development of productivity, people set higher demands on the quality of steel. In recent years, artificial intelligence, especially deep learning-based computer vision technology has attracted great attention, which can be used for detecting steel surface defects such as scratches, patches, and rust spots. However, due to the complexity of the strip steel surface, it is still a challenge to accurately and effectively detect defect regions by the existing defects detection methods. Therefore, we propose a unique saliency model, i.e., deeper feature integration network to highlight the defect regions on the strip steel surface. To be specific, after each encoder stage, we introduce the multiscale global feature extraction module to elevate the multiscale deep features from the encoder. Meanwhile, we deploy the deeper feature extraction module, which contains a bidirectional feature extraction unit, to dig the effective representation for defects. Particularly, the forward one is equipped with a channel-space weighted module and the backward one is equipped with a split attention module. After that, the features from the two branches are progressively integrated by the decoder, yielding the final high-quality saliency maps that give a good depiction of the strip steel surface defects. Extensive experiments are executed on the public dataset, and the comparison results show that our model performs better than the 15 state-of-the-art methods, which proves the effectiveness and superiority of the proposed model. [ABSTRACT FROM AUTHOR]

The challenge of video quality assessment (VQA) modeling for user-generated content (UGC) (i.e., UGC-VQA) is how to accurately extract discriminative features and elaborately quantify interfeature interactions by following the behavior patterns of human eye-brain vision perception. To address this issue, we propose the Deeper Spatial–Temporal Scoring Network (DSTS-Net) to give a precise VQA. Concretely, we first deploy the multiscale feature extraction module to characterize content-aware features accounting for nonlinear reverse hierarchy theory in video perception process, which is not fully considered in the reported UGC-VQA models. Hierarchical handcraft and semantic features are simultaneously considered using content adaptive weighting. Second, we develop a feature integration structure, i.e., deeper gated recurrent unit (DGRU), to fully imitate the interfeature interactions in visionary perception, including feedforward and feedback processes. Third, the dual DGRU structure is employed to further account for interframe interactions of hierarchical features, imitating the nonlinearity of perception as much as possible. Finally, improved pooling is achieved in the local adaptive smoothing module accounting for the temporal hysteresis. Holistic validation of the proposed method on four public challenging UGC-VQA datasets presents a comparable performance over the state-of-the-art no-reference VQA methods, especially our method can give an accurate prediction of the low quality videos with weak temporal correlation. To promote reproducible research and public evaluation, an implementation of our method has been made available online: https://github.com/liu0527aa/DSTS-Net. [ABSTRACT FROM AUTHOR]

The dense connection is a powerful technique to build wider and deeper convolution neural networks (CNNs) for handling several computer vision tasks. Despite the excellent performance, it consumes numerous parameters and produces a large weight model file. We studied the distribution of convolution layers and proposed a hard parameter sharing approach known as convolution pool (CP) for compressing dense-connection-based image restoration CNN models. CP is used to reallocate the parameters to specific convolution layers to ensure that some can be shared in different layers. We design a set of dense-connection-based baselines for three typical image restoration tasks, including image denoising, super-resolution, and JPEG deblocking, to validate the performance of the proposed method. Moreover, we comprehensively analyze the potential problems by introducing CP, including group convolution, dilated convolution, and modeling efficiency. Experimental results demonstrate that the proposed method can efficiently achieve an impressive compression rate with negligible performance reduction. [ABSTRACT FROM AUTHOR]