Titlebook: Computer Vision – ECCV 2020 Workshops; Glasgow, UK, August Adrien Bartoli,Andrea Fusiello Conference proceedings 2020 Springer Nature Swit

modish

Detecting Faces, Visual Medium Types, and Gender in Historical Advertisements, 1950–1995ptimization of scaling might solve the latter issue, while the former might be ameliorated using upscaling. We show how computer vision can produce meta-data information, which can enrich historical collections. This information can be used for further analysis of the historical representation of gender.

1分開

邊緣

A Dataset and Baselines for Visual Question Answering on Artare handled independently. We extensively compare our baseline model against the state-of-the-art models for question answering, and we provide a comprehensive study about the challenges and potential future directions for visual question answering on art.

EXUDE

舔食

Demographic Influences on Contemporary Art with Unsupervised Style Embeddingsat the beginning of their career. We evaluate three methods suited for generating unsupervised style embeddings of images and correlate them with the remaining data. We find no connections between visual style on the one hand and social proximity, gender, and nationality on the other.

cornucopia

Geolocating Time: Digitisation and Reverse Engineering of a Roman Sundiald the Sun positions during daytime are considered to obtain the optimal configuration. The complete 3D model of the object is used to get all the geometrical information needed to validate the results of computations.

Amendment

Object Retrieval and Localization in Large Art Collections Using Deep Multi-style Feature Fusion and labelled data or curated image collections. Our region-based voting with GPU-accelerated approximate nearest-neighbour search [.] allows us to find and localize even small motifs within an extensive dataset in a few seconds. We obtain state-of-the-art results on the Brueghel dataset [., .] and demo

ANNUL

Recognition of Affective and Grammatical Facial Expressions: A Study for Brazilian Sign Languagetion for sign language. Brazilian Sign Language (Libras) is used as a case study. In our approach, we code Libras’ facial expression using the Facial Action Coding System (FACS). In the paper, we evaluate two convolutional neural networks, a standard CNN and hybrid CNN+LSTM, for AU recognition. We e

BRACE

0302-9743 or data-efficient deep learning; 3D poses in the wild challenge; map-based localization for autonomous driving; recovering 6D object pose; and shape recovery from partial textured 3D scans..978-3-030-66095-6978-3-030-66096-3Series ISSN 0302-9743 Series E-ISSN 1611-3349

APEX

https://doi.org/10.1057/9780230112018isting state-of-the-art models for visual grounding, in addition to detecting potential failure cases by evaluating on carefully selected subsets. Finally, we discuss several possibilities for future work.