In the pathology image analysis, obtaining and maintaining high-quality annotated samples is an extremely labor-intensive task. To overcome this challenge, mixing-based methods have introduced new relationships to traditional preprocessing data augmentation techniques. Nonetheless, these methods fail to fully consider the unique features of pathology images, such as local specificity, global distribution, and inner/outer sample instance relationships. To better comprehend these characteristics and create valuable pseudosamples, we propose the CellMix framework, which employs a novel distribution-oriented in-place shuffle approach. The images are divided into patches based on the granularity of pathology instances, and the patches are in-place shuffled within the same batch. Thus, the locational relationships among instances can be effectively preserved while new relationships can be further introduced. Moreover, inspired by curriculum learning (CL), a loss-driven strategy is designed to control the relationship augmentation. This strategy enables the model to adaptively explore the instances at multiple scales and efficiently handle distribution-related noise under various difficulties. Our experiments in pathology image classification tasks demonstrate state-of-the-art (SOTA) performance on seven distinct datasets. This innovative instance relationship-centered method sheds light on general data augmentation for pathology image classification. The associated codes are available at: https://github.com/sagizty/CellMix.

Multimodal sarcasm detection, aiming to detect the ironic sentiment within multimodal social data, has gained substantial popularity in both the natural language processing and computer vision communities. Recently, graph-based studies by drawing sentimental relations to detect multimodal sarcasm have made notable advancements. However, they have neglected exploiting graph-based global semantic congruity from existing instances to facilitate the prediction, which ultimately hinders the model's performance. In this paper, we introduce a new inference paradigm that leverages global graph-based semantic awareness to handle this task. Firstly, we construct fine-grained multimodal graphs for each instance and integrate them into semantic space to draw graph-based relations. During inference, we leverage global semantic congruity to retrieve k-nearest neighbor instances in semantic space as references for voting on the final prediction. To enhance the semantic correlation of representation in semantic space, we also introduce label-aware graph contrastive learning to further improve the performance. Experimental results demonstrate that our model achieves state-of-the-art (SOTA) performance in multimodal sarcasm detection. The code will be available at https://github.com/upccpu/G2SAM.

In this paper, we present a novel system (denoted as Polaca) to generate poetic Chinese landscape painting with calligraphy. Unlike previous single image-to-image painting generation, Polaca takes the classic poetry as input and outputs the artistic landscape painting image with the corresponding calligraphy. It is equipped with three different modules to complete the whole piece of landscape painting artwork: the first one is a text-to-image module to generate landscape painting image, the second one is an image-to-image module to generate stylistic calligraphy image, and the third one is an image fusion module to fuse the two images into a whole piece of aesthetic artwork.