Each participating group is responsible for adhering to the letter and spirit of these rules, the intent of which is to make the TRECVID evaluation realistic, fair, and maximally informative about system effectiveness as opposed to other confounding effects on performance. Submissions, which in the judgment of the coordinators and NIST do not comply, will not be accepted.
The test data cannot be used for system development and system developers should have no knowledge of it until after they have submitted their results for evaluation to NIST. Depending on the size of the team and tasks undertaken, this may mean isolating certain team members from certain information or operations, freezing system development early, etc.
Participants may use donated semantic indexing output from the test collection but incorporation of such features should be automatic so that system development is not affected by knowledge of the extracted features. Anyone doing searches must be isolated from knowledge of that output.
The development data is intended for the participants' use in developing their systems. It is up to the participants how the development data is used, e.g., divided into training and validation data, etc.
Participants may use other development resources not excluded in these guidelines. Such resources must be reported at the workshop. Note that use of other resources will change the submission's status with respect to system development type, which is described next.
In order to help isolate system development as a factor in system performance each semantic indexing task submission, known-item search task submission, or donation of extracted features must declare its training type:
As the name "no annotation" indicates, for the categories E and F, no manual annotation should be done on the automatically collected data; automatic processing is allowed and encouraged but data should be processed blindly.
We encourage groups to submit at least one pair of runs from their allowable total that helps the community understand how well systems trained on non-IACC data generalize to IACC test data. data.