In recent years, vision transformer (ViT) has achieved remarkable breakthroughs in fine-grained visual classification (FGVC) because of its self-attention mechanism that excels in extracting distinctive features from different pixels. However, pure ViT falls short in capturing the crucial multi-scale, local, and low-layer features that hold significance for FGVC. To compensate for these shortcomings, a new hybrid network called HVCNet is designed, which fuses the advantages of ViT and convolutional neural networks (CNN). The three modifications in the original ViT are: 1) using a multi-scale image-to-tokens (MIT) module instead of directly tokenizing the raw input image, thus enabling the network to capture the features at different scales; 2) substituting feed-forward network in ViT's encoder with mixed convolution feed-forward (MCF) module, which enhances the capability of the network in capturing the local and multi-scale features; 3) designing multi-layer feature selection (MFS) module to address the issue of deep-layer tokens in ViT to avoid ignoring the local and low-layer features. The experiment results indicate that the proposed method surpasses state-of-the-art methods on publicly datasets.

Machine translation quality estimation (QE) refers to the quality assessment of machine translations without a given reference translation. Supervised QE models based on neural networks have achieved state-of-the-art results. But this method requires large-scale training data, which requires bilingual experts to create high-quality labels. This is often very costly. Therefore, we propose a sentence-level machine translation QE data augmentation method based on information dropping. Firstly, we calculate the subwords information of the target translation based on the conditional language model. Subsequently, some subwords in the target translation are randomly deleted or replaced. We obtain the pseudo quality score by calculating the remaining information. Finally, the original and augmented data are combined to train the final model. This pseudo-data generation method based on information dropping strategy enables us to obtain more faithful and diverse training samples without requiring additional corpus resources. Experimental results show that we improve the correlation with human judgment by an average of 5.96% in the seven translation directions of the MLQE-PE dataset, while improving the model's robustness to low adequacy samples. In addition, the method does not require any modifications to the model architecture.

Chinese idiom cloze-style reading comprehension task is of great significance for improving the machine’s ability to understand Chinese idioms, which is one of the essential application requirements in advanced artificial intelligence. Existing methods suffer from an insufficient deep semantic understanding of the text. To solve this problem, this paper proposes a novelRetrospective Multi-granularity Fusion Network (RMFNet)for Chinese idiom cloze-style reading comprehension. Our RMFNet is equipped with two novel modules to model deeper contextual information of passage and Chinese idioms, respectively. First, we propose a novelMulti-granularity Passage Fusion (MgPF)module, which enhances the passage representation by integrating different semantic perspectives. Second, we propose aRetrospective Reading (Re \(^2\) )module that implements a back-and-forth reading mechanism to concentrate on critical Chinese idioms, thereby generating an ultimate memory for the whole text. Notably, the intuition of the MgPF module and the Re \(^2\) module is based on human reading strategies in the real world. The strategies in these modules are similar to how humans perceive the text. Extensive experiments are conducted on Chinese benchmark datasets to evaluate the effectiveness and superiority of the proposed method. Our RMFNet achieves state-of-the-art performance and in-depth analysis verifies its capability for understanding the deep semantics of the text.