CVPR'22 workshop and challenges on Visual Perception and Learning in an Open World

[Shu Kong]

#challenges  #workshop  #computervision  #cvpr

========================================================

     Visual Perception and Learning in an Open World @ CVPR2022

                http://www.cs.cmu.edu/~shuk/vplow.html

========================================================

---------------------------------
            Overview
---------------------------------

Visual perception is indispensable for numerous applications, spanning transportation, healthcare, security, commerce, entertainment, and interdisciplinary research. Currently, visual perception algorithms are often developed in a closed-world paradigm, which assumes the data distribution and categorical labels are fixed a priori. This is unrealistic in the real open world, which contains situations that are dynamic, vast, and unpredictable. Algorithms developed in a closed world appear brittle once exposed to the complexity of the open world, where they are unable to properly adapt or robustly generalize to new scenarios. We are motivated to invite researchers to the workshop on Visual Perception and Learning in an Open World, where we have multiple speakers and three challenge competitions to cover a variety of topics. We hope our workshop stimulates fruitful discussions on open-world research.

---------------------------------
              Topics
---------------------------------

Topics of interest include, but are not limited to:

• Open-world data: long-tailed distribution, open-set, unknowns, streaming data, biased data, unlabeled data, anomaly, multi-modality, etc.   
• Learning/problems: X-shot learning, Y-supervised learning, lifelong/continual learning, domain adaptation/generalization, open-world learning, etc.   
• Social Impact: safety, fairness, real-world applications, inter-disciplinary research, etc.
• Misc: datasets, benchmarks, interpretability, robustness, generalization, etc.
  

---------------------------------
            Challenges
---------------------------------

We are organizing three challenge competitions this year.

1. CLEAR (https://clear-benchmark.github.io): Continual LEArning on Real-World Imagery. It is the first continual image classification benchmark dataset with a natural temporal evolution of visual concepts in the real world that spans a decade (2004-2014).
2. GRIT (https://grit-benchmark.org): the General Robust Image Task Benchmark. It evaluates the performance and robustness of a vision system across a variety of image prediction tasks, concepts, and data sources.
3. ObjCLsDisc (https://github.com/learn2phoenix/cvpr22_vplow_ow): In-the-Wild Object Discovery Challenge. It studies a system’s capabilities to discover and group novel object categories in a large unlabeled dataset.

---------------------------------
             Speakers
---------------------------------

More speakers will confirm to speak in addition to 

• Thomas G. Dietterich, Oregon State University
• Judy Hoffman, Georgia Tech
• Shu Kong, CMU
• Kristen Grauman, University of Texas at Austin
• Deepak Pathak, CMU
• Tomaso Poggio, MIT
• Walter J. Scheirer, University of Notre Dame
  
• Rahul Sukthankar, Google
• Tinne Tuytelaars, K.U. Leuven

---------------------------------
   Workshop Organizers
---------------------------------

• Shu Kong, CMU
• Deva Ramanan, CMU
• Terrance Boult, University of Colorado Colorado Springs
• Andrew Owens, University of Michigan
• Yu-Xiong Wang, UIUC
• Abhinav Shrivastava, UMD
• Carl Vondrick, Columbia University

---------------------------------
   Challenge Organizers
---------------------------------

• Tanmay Gupta, Allen Institute for AI (AI2)
• Derek Hoiem, UIUC
• Aniruddha (Ani) Kembhavi, Allen Institute for AI (AI2)
  
• Amita Kamath, Allen Institute for AI (AI2)
  
• Yuqun Wu, UIUC
  
• Ryan Marten, UIUC
• Zhiqiu Lin, CMU
  
• Jia Shi, CMU
  
• Pulkit Kumar, UMD
  
• Anubav, UMD