arXiv (All)
open access
Do More Modalities Always Help? A Geometric Perspective on Missing-Modality Robustness
Missing modality remains a longstanding challenge in multimodal learning. Existing methods typically address this issue through modality recovery or adaptive strategies. However, they overlook models' internal cross-modal dependencies formed during multimodal training, which later impair robustness.
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Win-Win Fusion: Multi-Modal Image Fusion with Visual-Semantic Collaboration#1296894
- A Multimodal Approach for Building Generalizable and Reliable Models of Longitudinal Experiential Data#442272
- Head pursuit: Probing attention specialization in multimodal transformers#442708
- Addressing Variable Heterogeneity in Distributed Multimodal Training with Entrain#238581
- One Geometry, Different Outcomes: Readout-Dependent Effects of the Modality Gap in Vision-Language Models#1130943
Record · ID 1200443
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive