Reliability-aware Cross-sample Enhancement (RCE) is a framework for multimodal sentiment analysis that tackles noise and missing modalities by first applying an adaptive variational information bottleneck to compress unreliable modality information. It then retrieves high‑confidence, semantically consistent neighbors from a large candidate pool to enrich current representations, and finally fuses cross‑modal interactions through a multilevel reliability‑aware mechanism. Experiments show RCE consistently outperforms state‑of‑the‑art methods in full, noisy, and missing‑modality scenarios.
By Menghua Jiang, Haokai Gao, Xiangui Kang, Haifeng Hu, Sijie Mai
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
Multi-modality image fusion (MMIF) enhances scene representation by exploiting complementary cues from different modalities. Adverse weather, however, causes significant image degradation, disrupting feature representation and requiring simultaneous feature restoration and cross-modal complementarity.
arXiv:2609.39051v1 Announce Type: new
Abstract: Existing multimodal video highlight detectors typically assume that visual, audio, and textual streams are continuously available. In practice, however...
By Bo-Yuan Cheng, Kuan-Yu Chen, Po-Han Huang, Jeng-Lin Li, Jian-Jiun Ding
arXiv:2608. 03611v1 Announce Type: new Abstract: Multimodal Sentiment Analysis (MSA) integrates text, audio, and vision to infer human affect, yet real-world multimodal observations are often incomplete.
By Chunlei Meng, Jacqueline J. Pang, Pengbin Feng, Zhenyu Yu, Chun Ouyang, Zhongxue Gan
arXiv:2608.29139v1 Announce Type: new
Abstract: Learning effective multimodal entity representations is fundamental for reasoning tasks such as multimodal knowledge graph completion (MMKGC). However,...
By Chenyi Xiong, Yan Zhang, Jing Hu, Ziyue Qin, Kui Xiao, Xiaopan Lyu, Xiaoju Hou, Zhifei Li
As real-world prediction systems often face missing modalities at inference, incomplete multimodal learning (IML) remains a practical challenge. While prior methods aim to learn representations robust to missing inputs, representations from incomplete modalities inevitably deviate from their full-modality counterparts due to missing evidence.