Real-World Computer Vision from Inputs with Limited Quality (RLQ)

In conjunction with ICCV 2021

Info for RLQ 2021 will be updated soon. Check our YouTube Recordings for RLQ 2020 @ ECCV.


How is the robustness of the current state-of-the-art for recognition and detection algorithms in non-ideal visual environments? While the visual recognition research has made tremendous progress in recent years, most models are trained, applied, and evaluated on high-quality (HQ) visual data. However, in many emerging applications such as robotics and autonomous driving, the performances of visual sensing and analytics are largely jeopardized by low-quality(LQ) visual data acquired from unconstrained environments, suffering from various types of degradation such as low resolution, noise, occlusion, motion blur, contrast, brightness, sharpness, out-of-focus etc. We are organizing the 3rd RLQ workshop in conjunction with ICCV 2021 to provide an integrated forum for both low-level and high-level vision researchers to review the recent progress of robust recognition models from LQ visual data and the novel image restoration algorithms. You could contribute to our workshop in three aspects:

For inquiry, please send emails to one of the following addresses:

  • General Inquiry: Humphrey Shi