The field of game vision and domain adaptation is moving towards more generalizable and transferable models. Researchers are focusing on developing methods that can learn game-invariant features, which can be applied to new games with minimal fine-tuning. This is achieved through techniques such as contrastive learning and domain-adversarial training, which encourage the model to learn features that are not specific to a particular game. Another area of research is unsupervised domain adaptation, where the goal is to transfer knowledge from a labeled source domain to an unlabeled target domain. Recent studies have highlighted the importance of considering both transferability and discriminability of features in domain adaptation. Noteworthy papers include:

• Game-invariant Features Through Contrastive and Domain-adversarial Learning, which proposes a method for learning game-invariant visual features.
• On the Transferability and Discriminability of Representation Learning in Unsupervised Domain Adaptation, which introduces a novel framework for unsupervised domain adaptation that integrates domain alignment with discriminability-enhancing constraints.
• Universal Domain Adaptation for Semantic Segmentation, which proposes a framework for universal domain adaptation in semantic segmentation that can handle unknown category settings.