Final Report Summary - VISCUL (Visual Culture for Image Understanding)
Some of the central outcomes of this project are:
- techniques for transferring visual knowledge from visual concepts which have location annotation for training, to classes that do not have them.
- techniques for training object class detectors with little human intervention. These schemes deliver high quality detectors and box annotations with substantially lower human annotation time than traditional drawing.
- several datasets with high quality annotations intended to push the community towards exploring new areas, namely: eye-tracking for learning computer vision models (POET dataset); contextual relations between object and background regions at a large-scale (COCO-Stuff dataset); modeling and learning articulated structures (TigDog dataset).
- pioneered the new area of learning object class models from video. The project explored exciting new avenues, including the highly challenging scenario of learning articulated object classes from weakly supervised video.
- several technical advances in semantic segmentation, object class detection, boundary detection, and semantic part detection.