This work presents VisiBlends, a system for creating visual blends by providing a flexible workflow that follows an iterative design process. The paper explores the feasibility of decomposing the creative task of designing a visual blend into microtasks that can be completed collaboratively. The paper also aims to explore if this system is capable of enabling novices to create visual blends easily. The workflow proposed decomposes the task process of creating a visual blend into computation techniques and human tasks. The main tasks involved in the workflow are brainstorming, finding images for each concept, annotating images for shape and coverage, synthesis, evaluation, and iteration. Three studies were conducted as part of this paper. The first was to test the feasibility of decentralized collaboration and involved 7 individuals taking part in only one of the above-mentioned tasks. The results demonstrated that although they were working only on parts of the workflow, together they were able to successfully create visual blends. The second study was to test group collaborations and involved groups of 2-3 people working together to create visual blends. The third study involved testing the ability of VisiBlends in helping novice users create visual blends. Overall, the results showed that due to the flexible, iterative nature of the system, the complex task of creating visual blends was successfully decomposed into microtasks.
I feel that human cognition and AI capabilities have been leveraged innovatively to decompose a complex, creative problem into distributable microtasks. VisiBlends breaks down the task into microtasks that involve both human cognition as well as artificial intelligence. This paper addresses the fundamental elements involved in the creative task of visual blend designing and introduces the concept of the Single Shape Mapping design pattern.
I feel that VisiBlends is a great tool that allows novice users to create exciting visual content to promote their idea/product. It provides a platform that abstracts the complexities of creating a visual blend away. The user is tasked with finding the relevant images and annotating them for shapes and coverage. The rest of the magic happens on its own wherein VisiBlends runs a matching algorithm and synthesizes various potential blends from the user’s images. The iterative design flow allows users to modify the suggestions as required. I really liked the resue functionality of VisiBlends as well. This would definitely help with the brainstorming phase and would save a lot of time and effort.
- It was interesting to note that each participant was trained on the entire workflow although they were made to contribute only to one step. This helped them understand the big picture and they were able to function more efficiently. Can this finding be applied to other systems to improve their performance?
- Can similar techniques of decomposition be applied to other creative domains?
- One challenge mentioned in the paper was regarding the difficulty faced during the search for relevant images where users were not able to find appropriate images. What are some techniques that could be employed to overcome this?
I believe similar techniques of decomposition can be applied to other creative domains due to their exertion. I instead think the decomposition created within VisiBlends is adopted from other domains to use for this project. This kind of decomposition is similar to the method stories are broken down within the Agile lifecycle. Similar to breaking down stories, the image search can similarly be iterated upon. Since users had difficulties searching for relevant images, providing the users with pre-loaded URLs through Google Dorking can help. Google Dorking is a technique typically used within the security domain to fully utilize the Google Search Engine (by type of extensions, quality of photo, keywords, etc…). This technique could help prepare certain results are returned to start the creative process.