The paper proposes a resource allocation framework that intelligently distributes work between a human and an AI system in the context of foreground object segmentation. The advantages of using a mix of both humans and AI rather than either of them alone is demonstrated via the study conducted. The goal is to ensure that high-quality object segmentation results are produced while using considerably less human efforts involved. Two systems are implemented as part of this paper that automatically decide when to transfer control from the human to the AI component and vice versa, depending on the quality of segmentation encountered at each phase. The first system eliminates the need for human annotation effort by replacing human efforts with computers to generate coarse object segmentation which is refined by segmentation tools. The second system predicts the quality of the annotations and automatically identifies a subset of them that needs to be re-annotated by humans. Three diverse datasets were used to train and validate the system and these include datasets representing visible, phase contrast microscopy, and fluorescence microscopy images.
The paper explores leveraging the complementary strengths of humans and AI and allocates resources accordingly in order to reduce human involvement. I particularly liked the focus on quality throughout the paper. This particular system that employs a mixed approach mechanism ensures that the quality of the traditional systems which relied heavily on human involvement is met. The resultant system was successfully able to reduce significant hours of human effort and also maintain the quality of the resultant foreground object segmentation of images which is great.
Another aspect of the paper that I found impressive was the conscious effort to develop a single prediction model that is applicable across different domains. Three diverse datasets were employed as part of this initiative. The paper talks about the disadvantages of other systems that do not work well on multiple datasets. In such cases, only a domain expert or computer vision expert would be able to predict when the system would succeed. This paper claims that this is altogether avoided in this system. Also, the decision to intentionally include humans only once per image is good as opposed to the existing system where human effort is required multiple times during the initial segmentation phase of each image.
- This paper primarily focuses on reducing human involvement in the context of foreground object segmentation. What other applications can extend the principles of this system to achieve reduced involvement of humans in the loop while ensuring that quality is not affected?
- The system deals with predicting the quality of image segmentation outputs and involves the human to re-annotate only the lowest quality ones. What other ideas can be employed to ensure reduced human efforts in such a system?
- The paper implies that the system proposed can be applied across images from multiple domains. Were the three datasets described varied enough to ensure that this is a generalized solution?
I also appreciated the fact that the authors put in efforts to build a single model application to different domains and was happy to read that they had attempted to do so with varied datasets.
However, with respect to your question ‘Were the three datasets described varied enough to ensure that this is a generalized solution?’ I looked through a few images from the BU-BIL dataset as well as the Weizmann dataset and found that while the domain was different, they both had relatively clearly identifiable foreground images, which led me to believe that perhaps some more varied datasets (in terms of the segmentation task) might have given a better perspective on the generalizability of the solution.