The task of text-to-image synthesis is to generate photographic images conditioned on given textual descriptions. This challenging task has recently attracted considerable attention from the multimedia community due to its potential applications. Most of the up-to-date approaches are built based on generative adversarial network (GAN) models, and they synthesize images conditioned on the global linguistic representation. However, the sparsity of the global representation results in training difficulties on GANs and a shortage of fine-grained information in the generated images. To address this problem, we propose cross-modal global and local linguistic representations-based generative adversarial networks (CGL-GAN) by incorporating the local linguistic representation into the GAN. In our CGL-GAN, we construct a generator to synthesize the target images and a discriminator to judge whether the generated images conform with the text description. In the discriminator, we construct the cross-modal correlation by projecting the image representations at high and low levels onto the global and local linguistic representations, respectively. We design the hinge loss function to train our CGL-GAN model. We evaluate the proposed CGL-GAN on two publicly available datasets, the CUB and the MS-COCO. The extensive experiments demonstrate that incorporating fine-grained local linguistic information with cross-modal correlation can greatly improve the performance of text-to-image synthesis, even when generating high-resolution images.

The task of aspect-based sentiment analysis aims to identify sentiment polarities of given aspects in a sentence. Recent advances have demonstrated the advantage of incorporating the syntactic dependency structure with graph convolutional networks (GCNs). However, their performance of these GCN-based methods largely depends on the dependency parsers, which would produce diverse parsing results for a sentence. In this article, we propose a dual GCN (DualGCN) that jointly considers the syntax structures and semantic correlations. Our DualGCN model mainly comprises four modules: 1) SynGCN: instead of explicitly encoding syntactic structure, the SynGCN module uses the dependency probability matrix as a graph structure to implicitly integrate the syntactic information; 2) SemGCN: we design the SemGCN module with multihead attention to enhance the performance of the syntactic structure with the semantic information; 3) Regularizers: we propose orthogonal and differential regularizers to precisely capture semantic correlations between words by constraining attention scores in the SemGCN module; and 4) Mutual BiAffine: we use the BiAffine module to bridge relevant information between the SynGCN and SemGCN modules. Extensive experiments are conducted compared with up-to-date pretrained language encoders on two groups of datasets, one including Restaurant14, Laptop14, and Twitter and the other including Restaurant15 and Restaurant16. The experimental results demonstrate that the parsing results of various dependency parsers affect their performance of the GCN-based models. Our DualGCN model achieves superior performance compared with the state-of-the-art approaches. The source code and preprocessed datasets are provided and publicly available on GitHub (see https://github.com/CCChenhao997/DualGCN-ABSA).

Referring Expression Comprehension (REC) is a fundamental task in the vision and language domain, which aims to locate an image region according to a natural language expression. REC requires the models to capture key clues in the text and perform accurate cross-modal reasoning. A recent trend employs transformer-based methods to address this problem. However, most of these methods typically treat image and text equally. They usually perform cross-modal reasoning in a crude way, and utilize textual features as a whole without detailed considerations (e.g., spatial information). This insufficient utilization of textual features will lead to sub-optimal results. In this paper, we propose a Language Guided Reasoning Network (LGR-NET) to fully utilize the guidance of the referring expression. To localize the referred object, we set a prediction token to capture cross-modal features. Furthermore, to sufficiently utilize the textual features, we extend them by our Textual Feature Extender (TFE) from three aspects. First , we design a novel coordinate embedding based on textual features. The coordinate embedding is incorporated to the prediction token to promote its capture of language-related visual features. Second , we employ the extracted textual features for Text-guided Cross-modal Alignment (TCA) and Fusion (TCF), alternately. Third , we devise a novel cross-modal loss to enhance cross-modal alignment between the referring expression and the learnable prediction token. We conduct extensive experiments on five benchmark datasets, and the experimental results show that our LGR-NET achieves a new state-of-the-art. Source code is available at https://github.com/lmc8133/LGR-NET.

The task of Visual Question Answering (VQA) has emerged in recent years for its potential applications. To address the VQA task, the model should fuse feature elements from both images and questions efficiently. Existing models fuse image feature element vi and question feature element qi directly, such as an element product viqi. Those solutions largely ignore the following two key points: 1) Whether vi and qi are in the same space. 2) How to reduce the observation noises in vi and qi. We argue that two differences between those two feature elements themselves, like (vi − vj) and (qi −qj), are more probably in the same space. And the difference operation would be beneficial to reduce observation noise. To achieve this, we first propose Differential Networks (DN), a novel plug-and-play module which enables differences between pair-wise feature elements. With the tool of DN, we then propose DN based Fusion (DF), a novel model for VQA task. We achieve state-of-the-art results on four publicly available datasets. Ablation studies also show the effectiveness of difference operations in DF model.

Wei Pang, Chuan Zhou, Xiao-Hua Zhou, Xiaojie Wang. Findings of the Association for Computational Linguistics ACL 2024. 2024.

Image denoising is a fundamental problem in computer vision and multimedia computation. Non-local filters are effective for image denoising. But existing deep learning methods that use non-local computation structures are mostly designed for high-level tasks, and global self-attention is usually adopted. For the task of image denoising, they have high computational complexity and have a lot of redundant computation of uncorrelated pixels. To solve this problem and combine the marvelous advantages of non-local filter and deep learning, we propose a Convolutional Unbiased Regional (CUR) transformer. Based on the prior that, for each pixel, its similar pixels are usually spatially close, our insights are that (1) we partition the image into non-overlapped windows and perform regional self-attention to reduce the search range of each pixel, and (2) we encourage pixels across different windows to communicate with each other. Based on our insights, the CUR transformer is cascaded by a series of convolutional regional self-attention (CRSA) blocks with U-style short connections. In each CRSA block, we use convolutional layers to extract the query, key, and value features, namely Q , K , and V , of the input feature. Then, we partition the Q , K , and V features into local non-overlapped windows and perform regional self-attention within each window to obtain the output feature of this CRSA block. Among different CRSA blocks, we perform the unbiased window partition by changing the partition positions of the windows. Experimental results show that the CUR transformer outperforms the state-of-the-art methods significantly on four low-level vision tasks, including real and synthetic image denoising, JPEG compression artifact reduction, and low-light image enhancement.