Research map / Vision-language and multimodal / Cross-modal learning
What Makes Training Multi-Modal Classification Networks Hard?
Check your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use multiple input modalities instead of the best single modality | End-to-end multi-modal classification networks · Multi-modal classification tasks | classification performance | Hurt |
Closest papers by meaning
- ReconBoost: Boosting Can Achieve Modality Reconcilement (ICML 2024 · similarity 0.86)
- Beyond Modality Fusion: Deep Ensembles for Multimodal Classification (NeurIPS 2026 · similarity 0.85)
- Fusion or Confusion? Multimodal Complexity Is Not All You Need (NeurIPS 2026 · similarity 0.85)
- On the Comparison between Multi-modal and Single-modal Contrastive Learning (NeurIPS 2024 · similarity 0.85)
- A Closer Look at Multimodal Representation Collapse (ICML 2025 · similarity 0.84)
- Connect, Collapse, Corrupt: Learning Cross-Modal Tasks with Uni-Modal Data (ICLR 2024 · similarity 0.84)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.