Cross-X Learning for Fine-Grained Visual Categorization Article Swipe

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Wei Luo , Xitong Yang , Xianjie Mo , Yuheng Lu , Larry S. Davis , Jun Li , Jian Yang , Ser-Nam Lim ·

YOU? · · 2019 · Open Access · · DOI: https://doi.org/10.48550/arxiv.1909.04412

Recognizing objects from subcategories with very subtle differences remains a challenging task due to the large intra-class and small inter-class variation. Recent work tackles this problem in a weakly-supervised manner: object parts are first detected and the corresponding part-specific features are extracted for fine-grained classification. However, these methods typically treat the part-specific features of each image in isolation while neglecting their relationships between different images. In this paper, we propose Cross-X learning, a simple yet effective approach that exploits the relationships between different images and between different network layers for robust multi-scale feature learning. Our approach involves two novel components: (i) a cross-category cross-semantic regularizer that guides the extracted features to represent semantic parts and, (ii) a cross-layer regularizer that improves the robustness of multi-scale features by matching the prediction distribution across multiple layers. Our approach can be easily trained end-to-end and is scalable to large datasets like NABirds. We empirically analyze the contributions of different components of our approach and demonstrate its robustness, effectiveness and state-of-the-art performance on five benchmark datasets. Code is available at \url{https://github.com/cswluo/CrossX}.

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Computer Science

Artificial Intelligence

Benchmark (Surveying)

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Computer science Robustness (evolution) Artificial intelligence Scalability Benchmark (surveying) Machine learning Categorization Pattern recognition (psychology) Exploit Feature learning Matching (statistics) Feature extraction Mathematics Gene Biochemistry Statistics Chemistry Computer security Geography Geodesy Database

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/1909.04412
PDF: https://arxiv.org/pdf/1909.04412
OA Status: green
Cited By: 17
References: 41
Related Works: 10
OpenAlex ID: https://openalex.org/W2972571843

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DOI: https://doi.org/10.48550/arxiv.1909.04412

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Title: Cross-X Learning for Fine-Grained Visual Categorization

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Type: preprint

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Language: en

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Publication year: 2019

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Publication date: 2019-09-10

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Authors: Wei Luo, Xitong Yang, Xianjie Mo, Yuheng Lu, Larry S. Davis, Jun Li, Jian Yang, Ser-Nam Lim

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Landing page: https://arxiv.org/abs/1909.04412

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Concepts: Computer science, Robustness (evolution), Artificial intelligence, Scalability, Benchmark (surveying), Machine learning, Categorization, Pattern recognition (psychology), Exploit, Feature learning, Matching (statistics), Feature extraction, Mathematics, Gene, Biochemistry, Statistics, Chemistry, Computer security, Geography, Geodesy, Database

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Cited by: 17

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Citations by year (recent): 2025: 2, 2024: 3, 2023: 4, 2022: 3, 2021: 4

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abstract_inverted_index.large	15, 145
abstract_inverted_index.novel	98
abstract_inverted_index.parts	31, 113
abstract_inverted_index.small	18
abstract_inverted_index.their	60
abstract_inverted_index.these	46
abstract_inverted_index.treat	49
abstract_inverted_index.while	58
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abstract_inverted_index.across	131
abstract_inverted_index.easily	138
abstract_inverted_index.guides	106
abstract_inverted_index.images	83
abstract_inverted_index.layers	88
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abstract_inverted_index.paper,	67
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abstract_inverted_index.Cross-X	70
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abstract_inverted_index.between	62, 81, 85
abstract_inverted_index.feature	92
abstract_inverted_index.images.	64
abstract_inverted_index.layers.	133
abstract_inverted_index.manner:	29
abstract_inverted_index.methods	47
abstract_inverted_index.network	87
abstract_inverted_index.objects	1
abstract_inverted_index.problem	25
abstract_inverted_index.propose	69
abstract_inverted_index.remains	8
abstract_inverted_index.tackles	23
abstract_inverted_index.trained	139
abstract_inverted_index.However,	45
abstract_inverted_index.NABirds.	148
abstract_inverted_index.approach	76, 95, 135, 159
abstract_inverted_index.datasets	146
abstract_inverted_index.detected	34
abstract_inverted_index.exploits	78
abstract_inverted_index.features	39, 52, 109, 125
abstract_inverted_index.improves	120
abstract_inverted_index.involves	96
abstract_inverted_index.matching	127
abstract_inverted_index.multiple	132
abstract_inverted_index.scalable	143
abstract_inverted_index.semantic	112
abstract_inverted_index.available	174
abstract_inverted_index.benchmark	170
abstract_inverted_index.datasets.	171
abstract_inverted_index.different	63, 82, 86, 155
abstract_inverted_index.effective	75
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abstract_inverted_index.isolation	57
abstract_inverted_index.learning,	71
abstract_inverted_index.learning.	93
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abstract_inverted_index.neglecting	59
abstract_inverted_index.prediction	129
abstract_inverted_index.robustness	122
abstract_inverted_index.variation.	20
abstract_inverted_index.Recognizing	0
abstract_inverted_index.challenging	10
abstract_inverted_index.components:	99
abstract_inverted_index.cross-layer	117
abstract_inverted_index.demonstrate	161
abstract_inverted_index.differences	7
abstract_inverted_index.empirically	150
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abstract_inverted_index.intra-class	16
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abstract_inverted_index.robustness,	163
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abstract_inverted_index.contributions	153
abstract_inverted_index.corresponding	37
abstract_inverted_index.effectiveness	164
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abstract_inverted_index.cross-category	102
abstract_inverted_index.cross-semantic	103
abstract_inverted_index.classification.	44
abstract_inverted_index.state-of-the-art	166
abstract_inverted_index.weakly-supervised	28
abstract_inverted_index.\url{https://github.com/cswluo/CrossX}.	176
cited_by_percentile_year
countries_distinct_count	0
institutions_distinct_count	8
citation_normalized_percentile