Claim · 2016-06-02 · publication · Stated in source · Supported

The paper addresses the task of semantic image segmentation with deep learning and states that it makes three main contributions.

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What it connects

DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs → states its contribution → The paper addresses the task of semantic image segmentation with deep learning and states that it makes three main contributions.

Where it appears

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Claim and evidence

DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFsstates its contribution ↓The paper addresses the task of semantic image segmentation with deep learning and states that it makes three main contributions.

Machine-checked, not reviewed by an editorStated in sourceSupported

Supporting evidence: primary · 1 source

Date: 2016-06-02 · publication Records dated 2016 →

Scope and limitsThe paper's own description of its contribution; not an assessment of it.

The evidence 01

primary sourceSupports the claim

DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs

arXiv · 2016-06-02

Cite the original source

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Source excerpt

“In this work we address the task of semantic image segmentation with Deep Learning and make three main contributions that are experimentally shown to have substantial practical merit.”

Location in source arXiv v1, PDF p. 1, abstract

Machine-checked · 2026-10-31

Source details and check notes

Authors: Liang-Chieh Chen; George Papandreou; Iasonas Kokkinos; Kevin Murphy; A. Yuille
Version: arXiv:1606.00915v1, submitted 2 June 2016 (UTC). Retrieved for the candidate pack on 2026-10-28 (SHA-256 0554ad33505ab4067960b692d6d07c05fca2495ef919f1287215f7fab6619724). The author list is the byline as printed on the first page.
Version date: 2016-06-02
Canonical address: Open the canonical source ↗
Accessed: 2026-10-28
Language: English (en)
Attribution: Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, A. Yuille, “DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs”, 2016-06-02.
Reuse: No open redistribution licence asserted. Bibliographic metadata, editorial paraphrases and short attributed quotations (at most 50 words each). Recheck terms before wider redistribution.
Check notes: Candidate pack passage, key papers (research/key-papers). The quotation (28 words) matched the text retrieved for the candidate pack on 2026-10-28 (research/key-papers/verification.json). Matching ignored differences of whitespace, case, quotation marks, dashes and line-break hyphens, so the source’s typography can differ. No human editorial review.

Status and meaning

Editorial status. Not reviewed by an editor. An exploratory record that no editor has accepted. This status does not say whether a person has read any linked passage. Each evidence record above shows how it was checked.

Relation. A paper or other publication describes its own contribution, quoted from the expression the record names, usually its abstract or introduction. The record is the document’s own description, not an assessment: a claim of priority or novelty it contains is the document’s claim, flagged as such, and says nothing about earlier or later work.

Date. Date of the arXiv version quoted, submitted 2 June 2016 (UTC); later versions may word it differently.

Record ID kp-states-deeplab-semantic-image-segmentation-deep-2016-1