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pith:NUBOPVYY

pith:2023:NUBOPVYYSPN4AUFUSGYPOFH2MT

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MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning

Deyao Zhu, Jun Chen, Mohamed Elhoseiny, Pengchuan Zhang, Raghuraman Krishnamoorthi, Vikas Chandra, Xiang Li, Xiaoqian Shen, Yunyang Xiong, Zechun Liu

MiniGPT-v2 uses unique task identifiers to let one large language model handle many vision-language tasks at once.

arxiv:2310.09478 v3 · 2023-10-14 · cs.CV

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3 Author claim open · sign in to claim

4 Citations open

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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

After the three-stage training, the experimental results show that MiniGPT-v2 achieves strong performance on many visual question-answering and visual grounding benchmarks compared to other vision-language generalist models.

C2weakest assumption

That assigning unique identifiers to tasks will let the model distinguish instructions and learn each task more efficiently without task interference or negative transfer, an assumption stated in the abstract but not quantified or ablated in the provided text.

C3one line summary

MiniGPT-v2 adds unique task identifiers to a large language model so one system can perform image description, visual question answering, and visual grounding after three-stage training.

References

61 extracted · 61 resolved · 22 Pith anchors

[1] Sharegpt. https://github.com/domeccleston/sharegpt, 2023 2023

[2] Flamingo: a visual language model for few-shot learning 2022

[3] Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond 2023 · arXiv:2308.12966

[4] Language models are few-shot learners 1901

[5] Visualgpt: Data-efficient adaptation of pretrained language models for image captioning 2022

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Cited by

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Receipt and verification

First computed	2026-05-17T23:38:48.724066Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

6d02e7d71893dbc050b491b0f714fa64ca0a86b45572b70df0b505457b86a0b5

Aliases

arxiv: 2310.09478 · arxiv_version: 2310.09478v3 · doi: 10.48550/arxiv.2310.09478 · pith_short_12: NUBOPVYYSPN4 · pith_short_16: NUBOPVYYSPN4AUFU · pith_short_8: NUBOPVYY

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/NUBOPVYYSPN4AUFUSGYPOFH2MT \
  | 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: 6d02e7d71893dbc050b491b0f714fa64ca0a86b45572b70df0b505457b86a0b5

Canonical record JSON

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    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2023-10-14T03:22:07Z",
    "title_canon_sha256": "1c8704a30ff6ec013f87f8385db67046decac01ecfb8bc5d6c6e8d8df56936a2"
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