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Found 7 papers, 1 papers with code
Defending Multimodal Backdoored Models by Repulsive Visual Prompt Tuning
Multimodal contrastive learning models (e. g., CLIP) can learn high-quality representations from large-scale image-text datasets, yet they exhibit significant vulnerabilities to backdoor attacks, raising serious safety concerns.
BDetCLIP: Multimodal Prompting Contrastive Test-Time Backdoor Detection
In this paper, we provide the first attempt at a computationally efficient backdoor detection method to defend against backdoored CLIP in the inference stage.
Partial-label Learning with Mixed Closed-set and Open-set Out-of-candidate Examples
In this paper, we term the examples whose true label is outside the candidate label set OOC (out-of-candidate) examples, and pioneer a new PLL study to learn with OOC examples.
A Generalized Unbiased Risk Estimator for Learning with Augmented Classes
In this paper, we propose a generalized URE that can be equipped with arbitrary loss functions while maintaining the theoretical guarantees, given unlabeled data for LAC.
Candidate-aware Selective Disambiguation Based On Normalized Entropy for Instance-dependent Partial-label Learning
In this paper, we start with an empirical study of the dynamics of label disambiguation in both II-PLL and ID-PLL.
Incorporating Multiple Cluster Centers for Multi-Label Learning
In this article, we propose to leverage the data augmentation technique to improve the performance of multi-label learning.
Collaboration based Multi-Label Learning
It is well-known that exploiting label correlations is crucially important to multi-label learning.
