Mitigating the detrimental effects of noisy labels on the training process has become increasingly critical, as obtaining entirely clean or human-annotated samples for large-scale pretraining tasks is often impractical.Nonetheless, existing noise mitigation methods often encounter limitations in practical applications due to their task-specific design, model dependency, and significant computational overhead.In this work, we exploit the properties of high-dimensional orthogonality to identify a robust and effective boundary in cone space for separating clean and noisy samples.Building on this, we propose One-Step Antinoise (OSA), a model-agnostic noisy label mitigation paradigm that employs an estimator model and a scoring function to assess the noise level of input pairs through just one-step inference.We empirically validate the superiority of OSA, demonstrating its enhanced training robustness, improved task transferability, streamlined deployment, and reduced computational overhead across diverse benchmarks, models, and tasks.Our code is released at https://github.com/leolee99/OSA. Clarify data Warm upRectify data

Vision-based tactile sensors have recently gained prominence due to their superior resolution and ability to capture multi-dimensional contact information. However, even when sensors share the same sensing principle, variations in production factors can lead to differences in the color patterns of tactile signals. Unlike common vision tasks, vision-based tactile perception depends on tracking light variation in colorful signals, making it more susceptible to lighting conditions and thus more prone to domain gaps. In this paper, we propose an Omni-hardness perception framework that enables adaptation across various vision-based tactile sensors. Firstly, in-depth analyses of the factors influencing the generalization of hardness perception are presented. Furthermore, the light balance module and the force scale module are coupled to regulate network learning of generalized representations. Experimental results across multiple sensors demonstrate the transferability of learned representations. Additionally, downstream tasks in natural object perception, tumor detection, and grasping stability prediction, are proposed to evaluate the potential applications. The framework's performance shows promise for advancing general tactile sensing and embodied tactile perception.

Prevailing embedding-based cross-domain recommendation (CDR) techniques produce embeddings individually or transfer the overall feature distribution from one domain to another. However, in real-world applications, they might be ineffective due to semantic gap across domains, which arises from divergent purposes and descriptive styles. In this work, we aim to address this challenge between Mini Program and content channel in Alipay, the largest mobile payment platform in China. To bridge utility-oriented Mini Programs and advertisement-oriented contents, we utilize side information of entities to make the entity relevance scores trustworthy. Then we introduce a knowledge graph-based model to reduce the impact of embedding vibrating from contrastive learning and the biases from the pretrained language models. Extensive experiments conducted on a large-scale Alipay offline dataset as well as an online environment demonstrated the effectiveness of our proposed framework.