What Do You Do? Occupation Recognition in a Photo via Social Context

Shao, Ming; Li, Liangyue; Fu, Yun

doi:10.1109/iccv.2013.451

Cited by 30 publications

(18 citation statements)

References 40 publications

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“…Gallagher and Chen (2009) extract features describing group structure to aid demographic recognition. Shao et al (2013) use social context for occupation recognition in photos. Qin and Shelton (2016) exploit social grouping for multi-target tracking.…”

Section: Social Relationshipmentioning

confidence: 99%

Visual Social Relationship Recognition

Wong

Zhao

et al. 2020

Int J Comput Vis

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Social relationships form the basis of social structure of humans. Developing computational models to understand social relationships from visual data is essential for building intelligent machines that can better interact with humans in a social environment. In this work, we study the problem of visual social relationship recognition in images. We propose a Dual-Glance model for social relationship recognition, where the first glance fixates at the person of interest and the second glance deploys attention mechanism to exploit contextual cues. To enable this study, we curated a large scale People in Social Context (PISC) dataset, which comprises of 23,311 images and 79,244 person pairs with annotated social relationships. Since visually identifying social relationship bears certain degree of uncertainty, we further propose an Adaptive Focal Loss to leverage the ambiguous annotations for more effective learning. We conduct extensive experiments to quantitatively and qualitatively demonstrate the efficacy of our proposed method, which yields state-of-the-art performance on social relationship recognition.

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Section: Social Relationshipmentioning

confidence: 99%

Visual Social Relationship Recognition

Wong

Zhao

et al. 2020

Int J Comput Vis

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“…In this direction, there has been recent work on clothing recognition for occupation recognition [25], [26], fashion style recognition [27], or social tribe prediction in group photos [28], [29]. In the inverse direction, Liu et al propose a system to recommend outfits (sets of clothing items) according to the occasion or event [4].…”

Section: Clothing and Person Identificationmentioning

confidence: 99%

Retrieving Similar Styles to Parse Clothing

Yamaguchi

Kiapour

Ortiz

et al. 2015

IEEE Trans. Pattern Anal. Mach. Intell.

138

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Abstract-Clothing recognition is a societally and commercially important yet extremely challenging problem due to large variations in clothing appearance, layering, style, and body shape and pose. In this paper, we tackle the clothing parsing problem using a retrieval-based approach. For a query image, we find similar styles from a large database of tagged fashion images and use these examples to recognize clothing items in the query. Our approach combines parsing from: pre-trained global clothing models, local clothing models learned on the fly from retrieved examples, and transferred parse-masks (Paper Doll item transfer) from retrieved examples. We evaluate our approach extensively and show significant improvements over previous state-of-the-art for both localization (clothing parsing given weak supervision in the form of tags) and detection (general clothing parsing). Our experimental results also indicate that the general pose estimation problem can benefit from clothing parsing.

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“…Various databases are built to demonstrate the concept of the stateof-art feature extraction and classification algorithms, such as the Caltech101/256 [1], [2], traffic sign recognition [3], occupation recognition [4], the CAS-PEAL large scale Chinese face database [5] and many others [6], [7]. These image databases are either created to help the physical hardware system design (e.g., advanced driver assistant system (ADAS) in [3]) or the evaluation of the advanced machine learning algorithms (i.e., the one-shot learning/incremental learning of Caltech101 in [8], [9], [10]).…”

Section: Introductionmentioning

confidence: 99%

Experimental studies on indoor sign recognition and classification

Tang

et al. 2014

2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)

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Previous works on outdoor traffic sign recognition and classification have been demonstrated useful to the driver assistant system and the possibility to the autonomous vehicles. This motivates our research on the assistance for visual impairment or visual disabled pedestrians in the indoor environment. In this paper, we build an indoor sign database and investigate the recognition and classification for the indoor sign problem. We adopt the classical techniques on extracting the features, including the principle component analysis (PCA), dense scale invariant feature transform (DSIFT), histogram of oriented gradients (HOG), and conduct the state-of-art classification techniques, such as the neural network (NN), support vector machine (SVM) and knearest neighbors (KNN). We provide the experimental results on this newly built database and also discuss the insight for the possibility of indoor navigation for the blind or visual-disabled people.

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What Do You Do? Occupation Recognition in a Photo via Social Context

Cited by 30 publications

References 40 publications

Visual Social Relationship Recognition

Visual Social Relationship Recognition

Retrieving Similar Styles to Parse Clothing

Experimental studies on indoor sign recognition and classification

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