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A Unified Semantic Embedding: Relating Taxonomies and Attributes

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Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
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Sung Ju Hwang, Leonid Sigal

Computer Vision and Pattern Recognition · Submitted 18 Nov 2014

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim By exploiting such a unified model for semantics, we enforce each category to be represented by a supercategory + sparse combination of attributes, with an additional exclusive regularization to learn discriminative composition.

how it is stated · asserted 1.00 read the record →

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arXiv ID1411.5879 (version 2)
Pith IDFCQTVI2O, Pith's permanent record of this paper and its review
CitationLeonid Sigal, Sung Ju Hwang. "A Unified Semantic Embedding: Relating Taxonomies and Attributes." arXiv:1411.5879 (2014). https://pith.science/paper/FCQTVI2O
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13-20260917 via OpenRouter, question bank abstract-p1-20260923, 2026-09-23: 1 request, 3,011 input tokens, $0.0001 reported.

What the abstract itself states, as probabilities: claims first 0.12 · states limitation 0.05 · human participants 0.03 · controlled comparison 0.11 · randomized assignment 0.01 · states quantitative result 0.03 · compares with prior results 0.51 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v2. Results and the main claim are underlined; .

We propose a method that learns a discriminative yet semantic space for object categorization, where we also embed auxiliary semantic entities such as supercategories and attributes. Contrary to prior work which only utilized them as side information, we explicitly embed the semantic entities into the same space where we embed categories, which enables us to represent a category as their linear combination. By exploiting such a unified model for semantics, we enforce each category to be represented by a supercategory + sparse combination of attributes, with an additional exclusive regularization to learn discriminative composition.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 We propose a method that learns a discriminative yet semantic space for object categorization, where we also embed auxiliary semantic entities such as supercategories and attributes. contribution 0.95 0.91 0.20 asserted 0.99
s1 Contrary to prior work which only utilized them as side information, we explicitly embed the semantic entities into the same space where we embed categories, which enables us to represent a category as their linear combination. contribution 0.85 0.85 0.20 asserted 1.00
s2 By exploiting such a unified model for semantics, we enforce each category to be represented by a supercategory + sparse combination of attributes, with an additional exclusive regularization to learn discriminative composition. method 0.97 0.91 0.29 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from argument 0.51.

What was examined, and what was not

sources read
  • arXiv listing abstract v2
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

We propose a method that learns a discriminative yet semantic space for object categorization, where we also embed auxiliary semantic entities such as supercategories and attributes. Contrary to prior work which only utilized them as side information, we explicitly embed the semantic entities into the same space where we embed categories, which enables us to represent a category as their linear combination. By exploiting such a unified model for semantics, we enforce each category to be represented by a supercategory + sparse combination of attributes, with an additional exclusive regularization to learn discriminative composition.

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