10 accepted papers
Despite multi-view clustering has achieved great progress in past decades, it is still a challenge to balance the effectiveness, efficiency, completeness and consistency of nonlinearly separable clustering for the data from different views. To address this challenge, we show that multi-view clusteri…
Large-scale multi-view clustering aims to explore the complementary and consistent information among different views in efficient manner. Despite the impressive performance gained by the existing methods, they just perform anchor learning in a single space with the orthogonal or some other constrain
We address hallucination detection in Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) by framing the problem through the lens of dynamical systems stability theory. Rather than treating hallucination as a straightforward classification task, we conceptualize (M)LLMs as dyna
Learning on multi-view data is a fundamental task, which integrates the information from different views to improve the final performance. It is also a basic task for learning on the long-tailed data in real applications, followed by the downstream tasks, i.e., classification. The existing works for
Multi-view clustering aims to segment the view-specific data into the corresponding clusters. There have been a large number of works for multi-view clustering in recent years. As representive methods in multi-view clustering, works built on the graph make use of a view-consistent and discriminative…
Concept erasure serves as a vital safety mechanism for removing unwanted concepts from text-to-image (T2I) models. While extensively studied in U-Net and dual-stream architectures (e.g., Flux), this task remains under-explored in the recent emerging paradigm of single-stream diffusion transformers (…
Multi-view clustering aims to improve the final performance by taking advantages of complementary and consistent information of all views. In real world, data samples with partially available information are common and the issue regarding the clustering for incomplete multi-view data is inevitably r…
As a leading unsupervised classification algorithm in artificial intelligence, multi-view subspace clustering segments unlabeled data from different subspaces. Recent works based on the anchor have been proposed to decrease the computation complexity for the datasets with large scales in multi-view…
Multi-view clustering has gained increasing attention by utilizing the complementary and consensus information across views. To alleviate the computation cost for the existing multi-view clustering approaches on datasets with large scales, studies based on anchor have been presented. Although extens…
Generalized category discovery (GCD) aims at grouping unlabeled samples from known and unknown classes given labeled data of known classes. To meet the recent decentralization trend in the community we introduce a practical yet challenging task Federated GCD (Fed-GCD) where the training data are dis…