Noise-robust Vision-language Pre-training with Positive-negative Learning.

Vision-Language Pre-training (VLP) has shown promising performance in various tasks by learning a generic image-text representation space. However, most existing VLP methods encounter the Noisy Correspondence (NC) problem which refers to wrongly matched image-text pairs harvested from the wild. In this paper, we empirically study the influence of NC on the VLP model and obtain the following two observations.

Cross-Modal Retrieval With Noisy Correspondence via Consistency Refining and Mining.

The success of existing cross-modal retrieval (CMR) methods heavily rely on the assumption that the annotated cross-modal correspondence is faultless. In practice, however, the correspondence of some pairs would be inevitably contaminated during data collection or annotation, thus leading to the so-called Noisy Correspondence (NC) problem. To alleviate the influence of NC, we propose a novel method termed Consistency REfining And Mining (CREAM) by revealing and exploiting the difference between correspondence and consistency.

Semantic Invariant Multi-View Clustering With Fully Incomplete Information.

Robust multi-view learning with incomplete information has received significant attention due to issues such as incomplete correspondences and incomplete instances that commonly affect real-world multi-view applications. Existing approaches heavily rely on paired samples to realign or impute defective ones, but such preconditions cannot always be satisfied in practice due to the complexity of data collection and transmission. To address this problem, we present a novel framework called SeMantic Invariance LEarning (SMILE) for multi-view clustering with incomplete information that does not require any paired samples.

scBridge embraces cell heterogeneity in single-cell RNA-seq and ATAC-seq data integration.

Single-cell multi-omics data integration aims to reduce the omics difference while keeping the cell type difference. However, it is daunting to model and distinguish the two differences due to cell heterogeneity. Namely, even cells of the same omics and type would have various features, making the two differences less significant.

Robust Multi-View Clustering With Incomplete Information.

The success of existing multi-view clustering methods heavily relies on the assumption of view consistency and instance completeness, referred to as the complete information. However, these two assumptions would be inevitably violated in data collection and transmission, thus leading to the so-called Partially View-unaligned Problem (PVP) and Partially Sample-missing Problem (PSP). To overcome such incomplete information challenges, we propose a novel method, termed robuSt mUlti-view clusteRing with incomplEte information (SURE), which solves PVP and PSP under a unified framework.