arXiv AI By Avi Gupta, Nilotpal Sinha, Vishnu Raj, Sambuddha Saha, Pratik Joshi, Koteswar Rao Jerripothula, Tammam Tillo

Generalizing Multimodal Foundational Models in Context-Driven Class Incremental Learning

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arXiv AI
Sep 2

Taming Modality Entanglement in Continual Audio-Visual Segmentation

The paper introduces a new Continual Audio‑Visual Segmentation (CAVS) task that enables continuous segmentation of new classes guided by audio. It identifies two key challenges—multi‑modal semantic drift and co‑occurrence confusion—and proposes a Collision‑based Multi‑modal Rehearsal (CMR) framework with Multi‑modal Sample Selection (MSS) and Collision‑based Sample Rehearsal (CSR) strategies to address them. Experiments on three audio‑visual incremental scenarios show that CMR outperforms single‑modal continual learning methods.

By Yuyang Hong, Qi Yang, Tao Zhang, Zili Wang, Zhaojin Fu, Kun Ding, Bin Fan, Shiming Xiang
arXiv AI
Jul 10

Multimodal Unlearning Across Vision, Language, Video, and Audio: Survey of Methods, Datasets, and Benchmarks

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 Machine Learning
Sep 22

Generalized Multimodal Foundation Model

The paper introduces a generalized multimodal foundation model that can handle arbitrary combinations of modalities and prediction tasks. It trains on large-scale synthetic multimodal datasets with diverse causal structures to learn transferable multimodal correlations. Experiments on 18 real-world datasets across 12 modalities and 11 tasks show competitive performance compared to specialized models without task-specific adaptation.

By Huizi Cui, Zongbo Han, Chenggong Ding, Naichuan Xiao, Jialong Yang, Jingdong Chen, Guangyu Wang, Qinghua Hu, Changqing Zhang
Hugging Face Trending Papers
Aug 10

Omni2LoRA: Coherence-Preserving Parametric Memory for Efficient Omni Language Models

Omnimodal language models (OLMs) enable unified audio-visual understanding, but processing long joint token sequences makes inference computationally prohibitive. While recent token compression methods attempt to alleviate this burden, compressing modalities in isolation often destroys the temporal cross-modal anchors necessary for coherent reasoning.