The adoption of "human-in-the-loop" paradigms in computer vision and machine learning is leading to various applications where the actual data acquisition (e.g., human supervision) and the underlying inference algorithms are closely interwined. While classical work in active learning provides effective solutions when the learning module involves classification and regression tasks, many practical issues such as partially observed measurements, financial constraints and even additional distributional or structural aspects of the data typically fall outside the scope of this treatment. For instance, with sequential acquisition of partial measurements of data that manifest as a matrix (or tensor), novel strategies for completion (or collaborative filtering) of the remaining entries have only been studied recently. Motivated by vision problems where we seek to annotate a large dataset of images via a crowdsourced platform or alternatively, complement results from a state-ofthe-art object detector using human feedback, we study the "completion" problem defined on graphs, where requests for additional measurements must be made sequentially. We design the optimization model in the Fourier domain of the graph describing how ideas based on adaptive submodularity provide algorithms that work well in practice. On a large set of images collected from Imgur, we see promising results on images that are otherwise difficult to categorize. We also show applications to an experimental design problem in neuroimaging.
|Title of host publication||Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||9|
|Publication status||Published - 2017 Nov 6|
|Event||30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 - Honolulu, United States|
Duration: 2017 Jul 21 → 2017 Jul 26
|Name||Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017|
|Other||30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017|
|Period||17/7/21 → 17/7/26|
Bibliographical notePublisher Copyright:
© 2017 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition