The absence of open-vocabulary objects in training datasets can significantly impact the generalization ability of perception models in Zero-Shot Object Navigation (ZSON) tasks. Open-vocabulary objects refer to items that may not be explicitly defined or categorized during training but are encountered during actual deployment or testing phases. Without exposure to a diverse range of open-vocabulary objects during training, perception models may struggle when faced with unfamiliar or untrained object categories during inference. This limitation hinders the model's capacity to recognize and localize novel objects outside its training data distribution accurately.
To address this challenge effectively, it is essential to incorporate open-vocabulary objects into ZSON datasets during training sessions so that perception models can learn robust feature representations capable of generalizing across various object categories seamlessly.