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Deep Attentional Structured Representation Learning for Visual Recognition

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Krishna Kanth Nakka, Mathieu Salzmann

Computer Vision and Pattern Recognition · Submitted 14 May 2018

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

The authors' main claim As evidenced by our experiments, this consistently outperforms attention-less structured representation learning and yields state-of-the-art results on standard scene recognition and fine-grained categorization benchmarks.

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arXiv ID1805.05389 (version 1)
Pith ID3MLRKRSF, Pith's permanent record of this paper and its review
CitationKrishna Kanth Nakka, Mathieu Salzmann. "Deep Attentional Structured Representation Learning for Visual Recognition." arXiv:1805.05389 (2018). https://pith.science/paper/3MLRKRSF
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What the abstract itself states, as probabilities: claims first 0.06 · states limitation 0.08 · human participants 0.02 · controlled comparison 0.87 · randomized assignment 0.01 · states quantitative result 0.05 · compares with prior results 0.95 · mentions code or data availability 0.02.

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arXiv listing abstract v1. Results and the main claim are underlined; .

Structured representations, such as Bags of Words, VLAD and Fisher Vectors, have proven highly effective to tackle complex visual recognition tasks. As such, they have recently been incorporated into deep architectures. However, while effective, the resulting deep structured representation learning strategies typically aggregate local features from the entire image, ignoring the fact that, in complex recognition tasks, some regions provide much more discriminative information than others. In this paper, we introduce an attentional structured representation learning framework that incorporates an image-specific attention mechanism within the aggregation process. Our framework learns to predict jointly the image class label and an attention map in an end-to-end fashion and without any other supervision than the target label. As evidenced by our experiments, this consistently outperforms attention-less structured representation learning and yields state-of-the-art results on standard scene recognition and fine-grained categorization benchmarks.

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#sentencerolethe authors' own workstates the main conclusionstrength
s0 Structured representations, such as Bags of Words, VLAD and Fisher Vectors, have proven highly effective to tackle complex visual recognition tasks. background 1.00 0.04 0.02 asserted 1.00
s1 As such, they have recently been incorporated into deep architectures. background 1.00 0.05 0.02 asserted 1.00
s2 However, while effective, the resulting deep structured representation learning strategies typically aggregate local features from the entire image, ignoring the fact that, in complex recognition tasks, some regions provide much more discriminative information than others. gap 0.92 0.08 0.04 asserted 0.99
s3 In this paper, we introduce an attentional structured representation learning framework that incorporates an image-specific attention mechanism within the aggregation process. contribution 0.89 0.90 0.11 asserted 0.99
s4 Our framework learns to predict jointly the image class label and an attention map in an end-to-end fashion and without any other supervision than the target label. method 0.92 0.92 0.09 asserted 1.00
s5 As evidenced by our experiments, this consistently outperforms attention-less structured representation learning and yields state-of-the-art results on standard scene recognition and fine-grained categorization benchmarks. result 1.00 0.95 0.87 asserted 0.99

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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

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Original abstract

Structured representations, such as Bags of Words, VLAD and Fisher Vectors, have proven highly effective to tackle complex visual recognition tasks. As such, they have recently been incorporated into deep architectures. However, while effective, the resulting deep structured representation learning strategies typically aggregate local features from the entire image, ignoring the fact that, in complex recognition tasks, some regions provide much more discriminative information than others. In this paper, we introduce an attentional structured representation learning framework that incorporates an image-specific attention mechanism within the aggregation process. Our framework learns to predict jointly the image class label and an attention map in an end-to-end fashion and without any other supervision than the target label. As evidenced by our experiments, this consistently outperforms attention-less structured representation learning and yields state-of-the-art results on standard scene recognition and fine-grained categorization benchmarks.

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Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

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  • Training: We use the ADAM optimizer [ref] with parameter $ϵ=10^-4$, batch size of $16$ and a weight decay of $0.0005$ for all experiments.

    Adam: A Method for Stochastic Optimization · Implementation Details · 1.00

Uses data or values from 1 paper
  • We demonstrate the benefits of our approach on four challenging visual recognition tasks, including scene recognition on MIT-Indoor [ref] and fine-grained categorization on the UCSD Birds [ref] , FGVC Aircrafts [ref] and Stanford Cars [ref] datasets.

    Fine-Grained Visual Classification of Aircraft · Introduction · 0.88

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  • Deep FisherNet [ref] formulates an approximate fisher vector encoding with multi-scale patch extraction achieving $76.5%$ accuracy.

    Deep FisherNet for Object Classification · Comparison to State of art · 0.89

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  • While the previous methods all rely on histogram-based descriptors, in the context of fine-grained categorization and texture recognition, other structured representations, in the form of covariance matrices have been used [ref] .

    Statistically Motivated Second Order Pooling · Related Work · 0.93

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