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Effective Combination of Language and Vision Through Model Composition and the R-CCA Method

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Hagar Loeub, Roi Reichart

Computation and Language · Submitted 28 Sep 2016

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

The authors' main claim On five standard semantic benchmarks our sequential models outperform recent multimodal representation learning alternatives, including ones that rely on joint representation learning.

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arXiv ID1609.08810 (version 2)
Pith IDMYFDUVT3, Pith's permanent record of this paper and its review
CitationHagar Loeub, Roi Reichart. "Effective Combination of Language and Vision Through Model Composition and the R-CCA Method." arXiv:1609.08810 (2016). https://pith.science/paper/MYFDUVT3
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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,296 input tokens.

What the abstract itself states, as probabilities: claims first 0.08 · states limitation 0.10 · human participants 0.04 · controlled comparison 0.58 · randomized assignment 0.01 · states quantitative result 0.40 · compares with prior results 0.96 · mentions code or data availability 0.02.

The abstract, sentence by sentence

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

We address the problem of integrating textual and visual information in vector space models for word meaning representation. We first present the Residual CCA (R-CCA) method, that complements the standard CCA method by representing, for each modality, the difference between the original signal and the signal projected to the shared, max correlation, space. We then show that constructing visual and textual representations and then post-processing them through composition of common modeling motifs such as PCA, CCA, R-CCA and linear interpolation (a.k.a sequential modeling) yields high quality models. On five standard semantic benchmarks our sequential models outperform recent multimodal representation learning alternatives, including ones that rely on joint representation learning. For two of these benchmarks our R-CCA method is part of the Best configuration our algorithm yields.

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 address the problem of integrating textual and visual information in vector space models for word meaning representation. background 0.99 0.31 0.03 asserted 0.56
s1 We first present the Residual CCA (R-CCA) method, that complements the standard CCA method by representing, for each modality, the difference between the original signal and the signal projected to the shared, max correlation, space. method 0.63 0.85 0.05 asserted 0.98
s2 We then show that constructing visual and textual representations and then post-processing them through composition of common modeling motifs such as PCA, CCA, R-CCA and linear interpolation (a.k.a sequential modeling) yields high quality models. method 0.70 0.87 0.36 asserted 1.00
s3 On five standard semantic benchmarks our sequential models outperform recent multimodal representation learning alternatives, including ones that rely on joint representation learning. result 1.00 0.95 0.59 asserted 1.00
s4 For two of these benchmarks our R-CCA method is part of the Best configuration our algorithm yields. result 1.00 0.94 0.12 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.72.

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  • T body sentences
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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 address the problem of integrating textual and visual information in vector space models for word meaning representation. We first present the Residual CCA (R-CCA) method, that complements the standard CCA method by representing, for each modality, the difference between the original signal and the signal projected to the shared, max correlation, space. We then show that constructing visual and textual representations and then post-processing them through composition of common modeling motifs such as PCA, CCA, R-CCA and linear interpolation (a.k.a sequential modeling) yields high quality models. On five standard semantic benchmarks our sequential models outperform recent multimodal representation learning alternatives, including ones that rely on joint representation learning. For two of these benchmarks our R-CCA method is part of the Best configuration our algorithm yields.

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