Last CFP AMFG Workshop at 2021 CVPR

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Joseph Robinson

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Mar 16, 2021, 11:26:05 PM3/16/21
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Deadline Extended: Last CFP

2021 CVPR Workshop: The 10th IEEE International Workshop on Analysis and Modeling of Faces and Gestures (AMFG2021)

-- A deeper understanding of face, gestures, and higher-level attributes and semantics to further social analysis and enhance HCI

WEB: https://web.northeastern.edu/smilelab/amfg2021/ 

Call for papers

We have experienced rapid advances in the face, gesture, and cross-modality (e.g., voice and face) technologies. This is thanks to deep learning (i.e., dating back to 2012, AlexNet) and large-scale, labeled datasets. The progress in deep learning continues to push renowned public databases to near saturation which, thus, calls for evermore challenging image collections to be compiled as databases. In practice, and even widely in applied research, using off-the-shelf deep learning models has become the norm, as numerous pre-trained networks are available for download and are readily deployed to new, unseen data (e.g., VGG-Face, ResNet, amongst other types). We have almost grown “spoiled” from such luxury, which, in all actuality, has enabled us to stay hidden from many truths. Theoretically, the truth behind what makes neural networks more discriminant than ever before is still, in all fairness, unclear—rather, they act as a sort of black box to most practitioners and even researchers, alike. More troublesome is the absence of tools to quantitatively and qualitatively characterize existing deep models, which, in itself, could yield greater insights about these all so familiar black boxes. With the frontier moving forward at rates incomparable to any spurt of the past, challenges such as high variations in illuminations, pose, age, etc., now confront us. However, state-of-the-art deep learning models often fail when faced with such challenges owing to the difficulties in modeling structured data and visual dynamics.

Alongside the effort spent on conventional face recognition is the research is done across modality learning, such as face and voice, gestures in imagery, and motion in videos, along with several other tasks. This line of work has attracted attention from industry and academic researchers from all sorts of domains. Additionally, and in some cases with this, there has been a push to advance these technologies for social media-based applications. Regardless of the exact domain and purpose, the following capabilities must be satisfied: face and body tracking (e.g., facial expression analysis, face detection, gesture recognition), lip reading and voice understanding, face and body characterization (e.g., behavioral understanding, emotion recognition), face, body and gesture characteristic analysis (e.g., gait, age, gender, ethnicity recognition), group understanding via social cues (e.g., kinship, non-blood relationships, personality), and visual sentiment analysis (e.g., temperament, arrangement). Thus, needing to be able to create effective models for visual certainty has significant value in both the scientific communities and the commercial market, with applications that span topics of human-computer interaction, social media analytics, video indexing, visual surveillance, and the internet vision. Currently, researchers have made significant progress addressing many of these problems, and especially when considering off-the-shelf and cost-efficient vision HW products available these days, e.g. Intel RealSense, Magic Leap, SHORE, and Affdex. Nonetheless, serious challenges still remain, which only amplifies when considering the unconstrained imaging conditions captured by different sources focused on non-cooperative subjects. It is these latter challenges that especially grabs our interest, as we sought out to bring together the cutting-edge techniques and recent advances of deep learning to solve the challenges in the wild.

This one-day serial workshop (AMFG2021) provides a forum for researchers to review the recent progress of recognition, analysis, and modeling of face, body, and gesture, while embracing the most advanced deep learning systems available for face and gesture analysis, particularly, under an unconstrained environment like social media and across modalities like face to a voice. The workshop includes up to 3 keynotes and peer-reviewed papers (oral and poster). Original high-quality contributions are solicited on the following topics:

  • Novel deep model, deep learning survey, or comparative study for face/gesture recognition;

  • Deep learning methodology, theory, as applied to social media analytics;

  • Data-driven or physics-based generative models for faces, poses, and gestures; Deep learning for internet-scale soft biometrics and profiling: age, gender, ethnicity, personality, kinship, occupation, beauty ranking, and fashion classification by facial or body descriptor;

  • Deep learning for detection and recognition of faces and bodies with large 3D rotation, illumination change, partial occlusion, unknown/changing background, and aging (i.e., in the wild); especially large 3D rotation robust face and gesture recognition;

  • Motion analysis, tracking, and extraction of face and body models captured from several non-overlapping views;

  • Face, gait, and action recognition in low-quality (e.g., blurred), or low-resolution video from fixed or mobile device cameras;

  • AutoML for face and gesture analysis;

  • Mathematical models and algorithms, sensors and modalities for face & body gesture and action representation, analysis, and recognition for cross-domain social media;

  • Social/psychological based studies that aid in understanding computational modeling and building better-automated face and gesture systems with interactive features;

  • Multimedia learning models involving faces and gestures (e.g., voice, wearable IMUs, and face);

  • Social applications involving detection, tracking & recognition of face, body, and action;

  • Face and gesture analysis for sentiment analysis in a social context;

  • Other applications involving face and gesture analysis in social media content.

Submissions are handled via the workshop's CMT website:    https://cmt3.research.microsoft.com/BAMFG2021/Submission/Index  

Following the guideline of CVPR2021: http://cvpr2021.thecvf.com/node/33#submission-guidelines

Tentative Program Outlines

  • 8:30 AM – 8:45 AM Chairs’ opening remarks

  • 8:45 AM – 9:30 AM Invited talk I

  • 9:30 AM – 10:00 AM Coffee break I

  • 10:00 AM – 12:30 PM Oral Session I

  • 12:30 PM – 2:00 PM Lunch break

  • 2:00 PM – 2:45 PM Invited talk IIw

  • 2:45 PM – 3:15 PM Coffee break II

  • 3:15 PM – 5:00 PM Oral session II

  • 5:00 PM Best Paper Announcement and Conclusion

Previous AMFG Workshops

The first AMFG was held in conjunction with the 2003 ICCV in Nice, France. So far, it has been successfully held NINE times. The homepages of previous AMFG workshops are as follows:

Organization

Honorary General ChairGeneral Co-Chairs

  • Y. Raymond Fu, Northeastern University, Boston, USA

  • Mike Jones, Mitsubishi Electric Research Laboratories (MERL), Cambridge, USA 

Workshop Co-Chairs




Important Dates (Tentative)

Date: the whole day of June 16th, 17th, or 21st, 2021

Estimated Attendance: ~100 attend (from academia and industry). Most with basic knowledge of computer vision and pattern recognition (estimated based on previous AMFG workshops)

**Submission Deadline: March 13th, 2021 March 28th, 2021

Notification: March 30th, 2021 April 8th, 2021

Camera-Ready: April 20th, 2021


Logistics

Estimated submissions and acceptance rate (estimated based on previous AMFG workshops):

  • Estimated submissions: > 60 submissions

  • Estimated acceptance rate: 30%-40%

  • Estimated paper breakdown: Oral (40%) Poster (60%)

 

Best paper award: Like previous AMFG workshops, the best paper award shall be sponsored by a computer vision-related company.


--

Joseph Robinson

PhD Email: robin...@northeastern.edu

Website: www.jrobsvision.com

Cell: +1 (978) 764-0003


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