Pith Number

pith:WTMT4C4F

pith:2022:WTMT4C4F4AV4EMNMGBZJ5M7KEO

not attested not anchored not stored refs resolved

Language-driven Semantic Segmentation

Boyi Li, Kilian Q. Weinberger, Ren\'e Ranftl, Serge Belongie, Vladlen Koltun

LSeg aligns per-pixel image embeddings contrastively with text label embeddings to enable zero-shot semantic segmentation.

arxiv:2201.03546 v2 · 2022-01-10 · cs.CV · cs.CL · cs.LG

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Record completeness

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3 Author claim open · sign in to claim

4 Citations open

5 Replications 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

We demonstrate that our approach achieves highly competitive zero-shot performance compared to existing zero- and few-shot semantic segmentation methods, and even matches the accuracy of traditional segmentation algorithms when a fixed label set is provided.

C2weakest assumption

That the contrastive alignment learned on seen classes will transfer to arbitrary unseen text labels without retraining or additional samples, relying on the semantic structure already present in the pre-trained text encoder.

C3one line summary

LSeg achieves competitive zero-shot semantic segmentation by contrastively aligning dense pixel embeddings from a transformer with text embeddings of class labels.

References

13 extracted · 13 resolved · 4 Pith anchors

[1] Rethinking Atrous Convolution for Semantic Image Segmentation · arXiv:1706.05587

[2] Imagenet: A large-scale hierarchical image database 2009

[3] Recent advances in open set recognition: A survey 2022

[4] Open-vocabulary Object Detection via Vision and Language Knowledge Distillation · arXiv:2104.13921

[5] Few-shot open-set recognition using meta-learning 2013

Formal links

3 machine-checked theorem links

Cited by

30 papers in Pith

OpenGaFF: Open-Vocabulary Gaussian Feature Field with Codebook Attention

LangFlash: Feed-forward 3D Language Gaussian Splatting from Sparse Unposed Images

SpikingMoE: SDPrompt-Guided Dynamic Expert Fusion in Spiking Neural Networks

LPT: Less-overfitting Prompt Tuning for Vision-Language Model

SegRAG: Training-Free Retrieval-Augmented Semantic Segmentation

Receipt and verification

First computed	2026-05-17T23:38:15.264219Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

b4d93e0b85e02bc231ac30729eb3ea23b1eea10f3756730feba603c40ea6fdb5

Aliases

arxiv: 2201.03546 · arxiv_version: 2201.03546v2 · doi: 10.48550/arxiv.2201.03546 · pith_short_12: WTMT4C4F4AV4 · pith_short_16: WTMT4C4F4AV4EMNM · pith_short_8: WTMT4C4F

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Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/WTMT4C4F4AV4EMNMGBZJ5M7KEO \
  | 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: b4d93e0b85e02bc231ac30729eb3ea23b1eea10f3756730feba603c40ea6fdb5

Canonical record JSON

{
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    "abstract_canon_sha256": "dd8a4b1bc995781d61149e8a1705ae427fa55a3819a7bcb530e6a8e2812d504c",
    "cross_cats_sorted": [
      "cs.CL",
      "cs.LG"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2022-01-10T18:59:10Z",
    "title_canon_sha256": "82551c1a4c2bacd13df1784abb5b15249ffe375494248a240a31a918cd10e528"
  },
  "schema_version": "1.0",
  "source": {
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    "kind": "arxiv",
    "version": 2
  }
}