Access to Data: Participants may freely use pre-trained weights obtained from open-source datasets or models to initialize their target models. This practice is allowed to facilitate faster development cycles and to encourage innovation in model architecture and algorithm design.
Use of Pre-trained Models: Participants may freely use pre-trained weights obtained from open-source datasets or models to initialize their target models. This practice is allowed to facilitate faster development cycles and to encourage innovation in model architecture and algorithm design.
Restrictions on Data Usage: To maintain the integrity of the challenge, participants are explicitly prohibited from using additional private or in-house datasets to pre-train their models. Compliance with this rule ensures that all participants are evaluated on an equal footing.
Submission Limitations: Participants are restricted to a maximum of five submission attempts per sub-challenge. Each submission will consist of the model’s predictions on the test partition, along with the corresponding model and checkpoints/weights.
Evaluation Metrics: The organizers will evaluate the submitted models using Mean Squared Error (MSE). The metric will be computed by comparing the participants’ predictions against the ground-truth labels.
Final Ranking: The final test results of each team will be used for the final ranking in the challenge. This ensures that the models are thoroughly tested and their performance is accurately assessed.
Reproducibility: All materials, including data, must be accessible to all participating teams to ensure that the models submitted by participants are fully reproducible.
Organizer’s Role: The organizers will not actively participate in the challenge but will undertake a re-evaluation of the findings from the best-performing systems in each sub-challenge to validate the results.
Eligibility: The challenge is open to any researcher from any organization based anywhere in the world, fostering a global collaborative environment in addressing the complex issue of asynchronous video interviews.