arXiv:2509. 17446v3 Announce Type: replace-cross Abstract: Multimodal intent recognition (MMIR) suffers from weak semantic grounding and poor robustness under noisy or rare-class conditions.
arXiv:2606. 06249v1 Announce Type: cross Abstract: Transformer-based multimodal models rely on attention mechanisms to integrate information across heterogeneous modalities.
arXiv:2606. 02659v1 Announce Type: cross Abstract: Multimodal data fusion involves integrating and analyzing information from multiple modalities to uncover latent correlations and complementary patterns, thereby enhancing data processing and decision-making.
arXiv:2610.00576v1 Announce Type: new Abstract: In this paper, we propose Gestalt, a new paradigm of large multimodal model built around multimodal interplay. Despite rapid advances, large multimodal...
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,...
arXiv:2506. 03096v2 Announce Type: replace-cross Abstract: Contrastive language-image pre-training aligns features of text-image pairs in a common latent space via distinct encoders for each modality.
arXiv:2606. 00959v1 Announce Type: new Abstract: Understanding modality interaction in multimodal large language models (MLLMs) is central to reliable deployment.
arXiv:2607. 00293v1 Announce Type: cross Abstract: Achieving true artificial general intelligence requires foundation models capable of integrating new modalities without forgetting prior knowledge.
The paper introduces HRIL, a method for learning multimodal representations that explicitly models higher‑order interactions among modalities using a cross‑moment tensor and Tucker decomposition. It adds a synergy‑aware regularizer to preserve higher‑order coupling capacity, aiming to capture synergistic signals that only emerge from joint configurations of multiple modalities. Experiments on controlled synergy tasks and real‑world benchmarks show consistent improvements over existing multimodal contrastive methods, especially on tasks dominated by synergistic interactions.
arXiv:2610.07928v1 Announce Type: cross Abstract: Dynamic multimodal learning aims to learn robust representations by adaptively modeling information discrepancies across modalities. However, existin...
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.
arXiv:2607.28640v2 Announce Type: replace Abstract: Multimodal large language models (MLLMs) should exhibit cross-modal consistency, generating consistent responses given semantically equivalent inpu...
The paper introduces Inverted Asymmetric Fusion (IAF) to address strong-modality collapse in multimodal learning, where dominant modalities are degraded during fusion. IAF preserves the dominant modality by passing it unchanged and letting weaker modalities attend to it, while also strengthening weaker modalities via Modality-Aware Knowledge Distillation. Experiments on MultiHuSE, UR-FUNNY, and MUStARD show that IAF maintains unimodal performance and improves over the best unimodal baseline by up to 8.25%.