Pith Number

pith:CWNRZTEB

pith:2024:CWNRZTEBKIWWDJ7DFBHOWNIMQJ

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Deep Multimodal Learning with Missing Modality: A Survey

Gustavo Carneiro, Hsiang-Ting Chen, Hu Wang, Renjie Wu

Multimodal deep learning models can maintain performance when some input types are missing by using dedicated robustness techniques.

arxiv:2409.07825 v4 · 2024-09-12 · cs.CV · cs.AI · cs.LG

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Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

It provides the first comprehensive survey that covers the motivation and distinctions between MLMM and standard multimodal learning setups, followed by a detailed analysis of current methods, applications, and datasets, concluding with challenges and future directions.

C2weakest assumption

The assumption that the body of literature selected for review is sufficiently complete and representative of the current state of deep multimodal learning with missing modalities without major omissions of recent or niche contributions.

C3one line summary

This survey provides the first comprehensive overview of deep multimodal learning methods designed to remain robust when some input modalities are absent.

References

83 extracted · 83 resolved · 9 Pith anchors

[1] Medical image segmentation on mri images with missing modalities: A review.arXiv preprint arXiv:2203.06217,

[2] Dealing with the effects of sensor displacement in wearable activity recognition.Sensors, 14(6):9995–10023, 2026

[3] Rohan Bavishi, Erich Elsen, Curtis Hawthorne, Maxwell Nye, Augustus Odena, Arushi Somani, and Sağnak Taşırlar · doi:10.24432/c5c59f

[4] Overcoming missing and incomplete modalities with generative adversarial networks for building footprint segmentation 2018

[5] Sparks of Artificial General Intelligence: Early experiments with GPT-4 · arXiv:2303.12712

Formal links

2 machine-checked theorem links

Cited by

17 papers in Pith

Calibrated Multimodal Representation Learning with Missing Modalities

Fusion or Confusion? Multimodal Complexity Is Not All You Need

Inference-Time Dynamic Modality Selection for Incomplete Multimodal Classification

Emotion Collider: Dual Hyperbolic Mirror Manifolds for Sentiment Recovery via Anti Emotion Reflection

ModalImmune: Immunity Driven Unlearning via Self Destructive Training

Receipt and verification

First computed	2026-05-17T23:38:12.836498Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

159b1ccc81522d61a7e3284eeb350c826a737a5c9fe83d06fc085ec705a0615e

Aliases

arxiv: 2409.07825 · arxiv_version: 2409.07825v4 · doi: 10.48550/arxiv.2409.07825 · pith_short_12: CWNRZTEBKIWW · pith_short_16: CWNRZTEBKIWWDJ7D · pith_short_8: CWNRZTEB

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/CWNRZTEBKIWWDJ7DFBHOWNIMQJ \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 159b1ccc81522d61a7e3284eeb350c826a737a5c9fe83d06fc085ec705a0615e

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "6fa6031b7706d3a4ad81ff54065cf302441618c58152a992913a1f70df345060",
    "cross_cats_sorted": [
      "cs.AI",
      "cs.LG"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2024-09-12T08:15:39Z",
    "title_canon_sha256": "c74c2012fa9a4f98b57808185868664a2192599e11bc9e8c24c759ceeb8565ab"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2409.07825",
    "kind": "arxiv",
    "version": 4
  }
}