Learning Cross-Modal Affinity for Referring Video Object Segmentation Targeting Limited Samples

Referring video object segmentation (RVOS), as a supervised learning task, relies on sufficient annotated data for a given scene. However, in more realistic scenarios, only minimal annotations are available for a new scene, which poses significant challenges to existing RVOS methods. With this in mind, we propose a simple yet effective model with a newly designed cross-modal affinity (CMA) module based on a Transformer architecture. The CMA module builds multimodal affinity with a few samples, thus quickly learning new semantic information, and enabling the model to adapt to different scenarios. Since the proposed method targets limited samples for new scenes, we generalize the problem as - few-shot referring video object segmentation (FS-RVOS). To foster research in this direction, we build up a new FS-RVOS benchmark based on currently available datasets. The benchmark covers a wide range and includes multiple situations, which can maximally simulate real-world scenarios. Extensive experiments show that our model adapts well to different scenarios with only a few samples, reaching state-of-the-art performance on the benchmark. On Mini-Ref-YouTube-VOS, our model achieves an average performance of 53.1 J and 54.8 F, which are 10% better than the baselines. Furthermore, we show impressive results of 77.7 J and 74.8 F on Mini-Ref-SAIL-VOS, which are significantly better than the baselines. Code is publicly available at https://github.com/hengliusky/Few_shot_RVOS.Comment: Accepted by ICCV202

Membrane glycomics reveal heterogeneity and quantitative distribution of cell surface sialylation.

Given that unnatural sugar expression is metabolically achieved, the kinetics and disposition of incorporation can lend insight into the temporal and localization preferences of sialylation across the cell surface. However, common detection schemes lack the ability to detail the molecular diversity and distribution of target moieties. Here we employed a mass spectrometric approach to trace the placement of azido sialic acids on membrane glycoconjugates, which revealed substantial variations in incorporation efficiencies between N-/O-glycans, glycosites, and glycosphingolipids. To further explore the propensity for sialylation, we subsequently mapped the native glycome of model epithelial cell surfaces and illustrate that while glycosylation sites span broadly across the extracellular region, a higher number of heterogeneous glycoforms occur on sialylated sites closest to the transmembrane domain. Beyond imaging techniques, this integrative approach provides unprecedented details about the frequency and structure-specific distribution of cell surface sialylation, a critical feature that regulates cellular interactions and homeostatic pathways

Nanomedicine strategies to counteract cancer stemness and chemoresistance

Cancer stem-like cells (CSCs) identified by self-renewal ability and tumor-initiating potential are responsible for tumor recurrence and metastasis in many cancers. Conventional chemotherapy fails to eradicate CSCs that hold a state of dormancy and possess multi-drug resistance. Spurred by the progress of nanotechnology for drug delivery and biomedical applications, nanomedicine has been increasingly developed to tackle stemness-associated chemotherapeutic resistance for cancer therapy. This review focuses on advances in nanomedicine-mediated therapeutic strategies to overcome chemoresistance by specifically targeting CSCs, the combination of chemotherapeutics with chemopotentiators, and programmable controlled drug release. Perspectives from materials and formulations at the nano-scales are specifically surveyed. Future opportunities and challenges are also discussed