arXiv:2602. 08597v3 Announce Type: replace Abstract: Robust multimodal systems must remain effective when some modalities are noisy, degraded, or unreliable.
By Roland Bertin-Johannet, Lara Scipio, Leopold Mayti\'e, Rufin VanRullen
arXiv:2607. 07907v1 Announce Type: cross Abstract: With the growing adoption of VLMs, DMs, LLMs, and AFMs, these multimodal foundation models can inadvertently encode sensitive, copyrighted, biased, or unsafe cross-modal associations that originate from their training data.
By Nobin Sarwar, Shubhashis Roy Dipta, Zheyuan Liu, Vaidehi Patil
arXiv:2608. 02769v1 Announce Type: cross Abstract: Multimodal supervised learning seeks to leverage multiple heterogeneous data sources to improve predictive performance.
By Sagnik Nandy, Samriddha Lahiry, Pragya Sur, Subhabrata Sen
arXiv:2608. 09122v1 Announce Type: cross Abstract: The localized depiction of perceptual quality has long been a crucial, yet underexplored, challenge in image quality assessment (IQA).
By Ziheng Jia, Yingji Liang, Jiaying Qian, Xiongkuo Min
arXiv:2608. 03475v1 Announce Type: cross Abstract: Multimodal intent recognition combines linguistic, acoustic, and visual evidence, but individual modalities may be noisy, missing, semantically conflicting, or disproportionately dominant.
By Suraj Kumar, Mohnish Raj, Soumi Chattopadhayay, Chandranath Adak, Ayan Dutta
arXiv:2602. 22568v2 Announce Type: replace-cross Abstract: Deep multi-view clustering has achieved remarkable progress but remains vulnerable to complex noise in real-world applications.
By Peihan Wu, Guanjie Cheng, Yufei Tong, Meng Xi, Shuiguang Deng