Space speaks

Zen, Gloria; Lepri, Bruno; Ricci, Elisa; Lanz, Oswald

doi:10.1145/1878039.1878048

Cited by 79 publications

(16 citation statements)

References 18 publications

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“…Some existing works tackle the group FGCs analysis problem by creating real-life datasets. Hung et al [11], Zen et al [5], Cristani et al [12], and Farenzena et al [13] created visual datasets for F-formation detection. The data creation pipeline in those researches usually involves recording a controlled social interaction event, and experts (i.e.…”

Section: Datasetmentioning

confidence: 99%

“…To address the challenges of analyzing groups, a variety of benchmarks like SALSA [4], Cocktail Party [5] has been presented by researchers in the community. However, available datasets have significant drawbacks.…”

Section: Introductionmentioning

confidence: 99%

“…Most methods rely on hard-coded parameters whose values are derived by experiments or conventional methods from small-scale datasets, which may not be generalizable. For example, [8], [9] are experimented on SALSA dataset [4], [5], etc. These datasets are fixed at the number of people in the frame and camera setup as well as the environment.…”

Section: Introductionmentioning

confidence: 99%

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F2SD: a dataset for end-to-end group detection algorithms

Hoang

Nguyen

Cao

et al. 2023

Fifteenth International Conference on Machine Vision (ICMV 2022)

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The lack of large-scale datasets has been impeding the advance of deep learning approaches to the problem of F-formation detection. Moreover, most research on this problem rely on input sensor signals of object location and orientation rather than image signals. To address this, we develop a new, large-scale dataset of simulated images for F-formation detection, called F-formation Simulation Dataset (F2SD). F2SD contains nearly 60,000 images simulated from GTA-5, with bounding boxes and orientation information on images, making it useful for a wide variety of modelling approaches. It is also closer to practical scenarios, where three-dimensional location and orientation information are costly to record. It is challenging to construct such a large-scale simulated dataset while keeping it realistic. Furthermore, the available research utilizes conventional methods to detect groups. They do not detect groups directly from the image. In this work, we propose (1) a large-scale simulation dataset F2SD and a pipeline for F-formation simulation, (2) a first-ever end-to-end baseline model for the task, and experiments on our simulation dataset.

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Section: Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

F2SD: a dataset for end-to-end group detection algorithms

Hoang

Nguyen

Cao

et al. 2023

Fifteenth International Conference on Machine Vision (ICMV 2022)

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“…Finally, social features include measures of interpersonal events, such as social gaze (Lepri, Subramanian, et al, 2012), speaking time , or spatial proximity (Zen et al, 2010). In some cases, wearable devices have been employed to measure peoples' movements and proximities in space in group interactions .…”

Section: Features Extractionmentioning

confidence: 99%

“…Algorithms have been employed to detect personality in highly standardized settings (e.g., an automated structured clinical interview; Stratou et al, 2013), in constrained, but unstandardized environments (e.g., a customer assistant agent; Gilpin et al, 2018) or in unconstrained environments (e.g., visual surveillance; Zen et al, 2010).…”

Section: Example Of Information Moderator: Contextual Elementsmentioning

confidence: 99%

Toward an Integrative Approach to Nonverbal Personality Detection: Connecting Psychological and Artificial Intelligence Research.

Cannata¹,

Breil²,

Back³

et al. 2021

Preprint

Self Cite

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Our first impressions of the people we meet are the subject of considerable interest, academic and non-academic. Such initial estimates of another’s personality (e.g., their sociality or agreeableness) are vital, since they enable us to predict the outcomes of interactions (e.g., can we trust them?). Nonverbal behaviors are a key medium through which personality is expressed and detected. The character and reliability of these expression and detection processes have been investigated within two major fields: Psychological research on personality judgments accuracy and Artificial Intelligence research on personality computing. Communication between these fields has, however, been infrequent. In the present perspective, we summarize the contributions and open questions of both fields and propose an integrative approach to combine their strengths and overcome their limitations. The integrated framework will enable novel research programs, such as (i), identifying which detection tasks better suit humans or computers, (ii), harmonizing the nonverbal features extracted by humans and computers, and (iii), integrating human and artificial agents in hybrid systems.

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