COCO Attributes: Attributes for People, Animals, and Objects

Patterson, Geneviève; Hays, James

doi:10.1007/978-3-319-46466-4_6

Cited by 63 publications

(41 citation statements)

References 32 publications

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“…Such efforts include Pascal-Context [22], NYU Depth V2 [23], SUN database [34], SUN RGB-D dataset [31], CityScapes dataset [7], and OpenSurfaces [2,3]. Recently COCO stuff dataset [4] provides additional stuff segmentation complementary to the 80 object categories in COCO dataset, while COCO attributes dataset [26] annotates attributes for some objects in COCO dataset. Such a dataset with progressive enhancement of diverse annotations over the years makes great progress to the modern development of image dataset.…”

Section: Related Workmentioning

confidence: 99%

Semantic Understanding of Scenes Through the ADE20K Dataset

et al. 2018

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Semantic understanding of visual scenes is one of the holy grails of computer vision. Despite efforts of the community in data collection, there are still few image datasets covering a wide range of scenes and object categories with pixel-wise annotations for scene understanding. In this work, we present a densely annotated dataset ADE20K, which spans diverse annotations of scenes, objects, parts of objects, and in some cases even parts of parts. Totally there are 25k images of the complex everyday scenes containing a variety of objects in their natural spatial context. On average there are 19.5 instances and 10.5 object classes per image. Based on ADE20K, we construct benchmarks for scene parsing and instance segmentation. We provide baseline performances on both of the benchmarks and re-implement the state-ofthe-art models for open source. We further evaluate the effect of synchronized batch normalization and find that a reasonably large batch size is crucial for the semantic segmentation performance. We show that the networks trained on ADE20K are able to segment a wide variety of scenes and objects 1 .

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Section: Related Workmentioning

confidence: 99%

Semantic Understanding of Scenes Through the ADE20K Dataset

et al. 2018

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“…FER-Wild [26] database contains 24,000 images that are obtained by querying emotion-related terms from search engines. MS-COCO database [28] has been recently annotated with object attributes, including some emotion categories for human, but the attributes are not intended to be exhaustive for emotion recognition, and not all people are annotated with emotion attributes. Some studies [29,30] built the database consisting of a spontaneous subset acquired under a restrictive setting to establish the relationship between emotion and body posture.…”

Section: Related Workmentioning

confidence: 99%

Context-Aware Emotion Recognition Networks

Lee

Kim

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

170

134

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Traditional techniques for emotion recognition have focused on the facial expression analysis only, thus providing limited ability to encode context that comprehensively represents the emotional responses. We present deep networks for context-aware emotion recognition, called CAER-Net, that exploit not only human facial expression but also context information in a joint and boosting manner. The key idea is to hide human faces in a visual scene and seek other contexts based on an attention mechanism. Our networks consist of two sub-networks, including two-stream encoding networks to separately extract the features of face and context regions, and adaptive fusion networks to fuse such features in an adaptive fashion. We also introduce a novel benchmark for context-aware emotion recognition, called CAER, that is more appropriate than existing benchmarks both qualitatively and quantitatively. On several benchmarks, CAER-Net proves the effect of context for emotion recognition. Our dataset is available at

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“…Datasets & attributes. Since 2014, there has been an explosion in large-scale, 'in the wild' surveillance [24], [25], [26], [30], [31], [32] and people [33] datasets. We opt for the PETA dataset [30], amalgamating 10 prominent benchmark re-id datasets.…”

Section: Categorical Human Attribute Recognitionmentioning

confidence: 99%

Super-Fine Attributes with Crowd Prototyping

Martinho-Corbishley

Nixon

Carter

2019

IEEE Trans. Pattern Anal. Mach. Intell.

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Recognising human attributes from surveillance footage is widely studied for attribute-based re-identification. However, most works assume coarse, expertly-defined categories, ineffective in describing challenging images. Such brittle representations are limited in descriminitive power and hamper the efficacy of learnt estimators. We aim to discover more relevant and precise subject descriptions, improving image retrieval and closing the semantic gap. Inspired by fine-grained and relative attributes, we introduce super-fine attributes, which now describe multiple, integral concepts of a single trait as multi-dimensional perceptual coordinates. Crowd prototyping facilitates efficient crowdsourcing of super-fine labels by pre-discovering salient perceptual concepts for prototype matching. We re-annotate gender, age and ethnicity traits from PETA, a highly diverse (19K instances, 8.7K identities) amalgamation of 10 re-id datasets including VIPER, CUHK and TownCentre. Employing joint attribute regression with the ResNet-152 CNN, we demonstrate substantially improved ranked retrieval performance with super-fine attributes in direct comparison to conventional binary labels, reporting up to a 11.2% and 14.8% mAP improvement for gender and age, further surpassed by ethnicity. We also find our 3 super-fine traits to outperform 35 binary attributes by 6.5% mAP for subject retrieval in a challenging zero-shot identification scenario.

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COCO Attributes: Attributes for People, Animals, and Objects

Cited by 63 publications

References 32 publications

Semantic Understanding of Scenes Through the ADE20K Dataset

Semantic Understanding of Scenes Through the ADE20K Dataset

Context-Aware Emotion Recognition Networks

Super-Fine Attributes with Crowd Prototyping

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