ICCV 2017 Workshop: 7th IEEE International Workshop on Analysis and Modeling of Faces and Gestures (AMFG) Venice, Italy, October 28, 2017 |
Conference website | https://web.northeastern.edu/smilelab/AMFG2017/home.html |
Submission deadline | July 31, 2017 |
ICCV 2017 Workshop: 7th IEEE International Workshop on Analysis and Modeling of Faces and Gestures (AMFG)
Embracing Face and Gesture Analysis in Social Media with Deep Learning
This one-day serial workshop (AMFG2017) will provide a forum for researchers to review the recent progress of recognition, analysis and modeling of face, gesture, and body, and embrace the most advanced deep learning system to address face and gesture analysis particularly under unconstrained environment such as social media. The workshop will consist of one to two invited talks; one from industry, together with peer-reviewed regular papers (oral and poster). Original high-quality contributions are solicited on the following topics:
Submission Guidelines
https://web.northeastern.edu/smilelab/AMFG2017/submission.html
List of Topics
- 1. Deep learning methodology, theory, and its applications to social media analytics;
- 2. Novel deep learning model, deep learning survey, or comparative study for face/gesture recognition;
- 3. Deep learning for internet-scale soft biometrics and profiling: age, gender, ethnicity, personality, kinship, occupation, beauty, and fashion classification by facial and/or body descriptor;
- 4. Face, gait, and action recognition in low-quality (blurred for instance), or low-resolution video from fixed or mobile cameras;
- 5. Novel mathematical modeling and algorithms, sensors and modalities for face & body gesture/action representation, analysis and recognition for cross-domain social media;
- 6. Deep learning for detection and recognition of face and body in the wild with large 3D rotation, illumination change, partial occlusion, unknown/changing background, and aging; especially large 3D rotation robust face and gesture recognition;
- 7. Motion analysis, tracking and extraction of face and body models from image sequences captured by mobile devices;
- 8. Face, gait, and action recognition in low-quality (blurred for instance), or low-resolution video from fixed or mobile cameras;
- 9. Novel mathematical modeling and algorithms, sensors and modalities for face & body gesture/action representation, analysis, and recognition for cross-domain social media;
- 10. Social/Psychological studies that can assist in understanding computational modeling and building better automated face and gesture systems for interaction purposes;
- 11. Novel social applications based on the robust detection, tracking and recognition of face, body, and action;
- 12. Face and gesture analysis for sentiment analysis in social media;
- 13. Other applications of face and gesture analysis in social media content understanding.
Committees
Program Committee
- Ding Liu, UIUC, USA
- Xi Peng, A*STAR, Singapore
- Yu Kong, Northeastern University, USA
- Wei Chen, Amazon, USA
- Ejaz Ahmed, Amazon Go, USA
- Di Huang, Beihang University, China
- Dmitry Kit, Hitachi Insight Group, USA
- Tadas Baltrusaitis, CMU, USA
- Daniel McDuff, MIT, USA
- Xinchao Wang, UIUC, USA
Organizing committee
****Honorary General Chair****
- Thomas S. Huang, University of Illinois at Urbana-Champaign, USA
****General Chairs****
- Dimitris N. Metaxas, Rutgers, The State University of New Jersey, USA
- Yun Raymond Fu, Northeastern University, Boston, USA
****Workshop Co-Chairs****
- Mohammad Soleymani, Swiss Center for Affective Sciences, Switzerland
- Ming Shao, University of Massachusetts Dartmouth, USA
- Zhangyang (Atlas) Wang, Texas A&M University, USA
Invited Speakers
- Lei Zhang, Microsoft Research
- Tim K. Marks, MERL
- Xiaoming Liu, MSU
Contact
- Ming Shao, University of Massachusetts Dartmouth, USA