- Investigating whether Capsule Networks can provide useful formalism and structure for Class-Angostic Counting networks.
- Fergus Steel
- 2542391s
- Dr. Paul Siebert
- Capsule Networks will increase the quality of the representations of visual concepts in Class Agnostic Counting
- Class-Agnostic counting theoretically allows for us to count objects in any object domain.
- Capsule Networks are a relatively new area of reserach in Artificial Intelligence, and by demonstrating their use in a novel domain, we can make a valuable contribution to the literature by expanding the use cases of CapsNets.
- Is a Class-Agnostic Capsule Network Architecture capable of predicting the density maps of visual concepts? – (1) representational general utility
- Can Capsule Networks be used to count in unseen distributions/domains in Class Agnostic Counting Tasks? - (2) representational adaptability
- Can the use of Encapsulated Representaions improve density-map estimation in Class Agnostic Counting? – (3) representational specific utility
- Building a Capsule Network that system that attempts to show the above concepts,
- (1) - To do this the network must be able to create density maps on trained distributions
- (2) - To do this the network must be able to create density maps on object distributions that have not been seen
- (3) - To do this the network must be able to demonstrate that it is an improvement on existing seminal research
- FSC-147 dataset, with similiar method in Ranjan paper for passing in bounding boxes (i.e. a text file that is generated by GUI.)