Zero-Shot Learning (ZSL) in computer vision is a supervisory learning framework that allows machines to recognize objects or actions that they have not encountered during the training phase. ZSL's unique ability to classify 'unseen' aspects overcomes the limitation of traditional machine learning models that can only recognize object types they have been trained on.

The salient features of ZSL include:

• Extensive Application: ZSL is widely applied in multiple fields, including image classification, object detection, and semantic segmentation, given its capacity to handle unseen cases.
• Future Potential: With meticulous attention to exploration and implementation, ZSL holds significant potential in addressing the challenges of limited annotated data and high classification costs.
• Semantic Space: ZSL operates in a semantic space, allowing it to map objects to an accessible semantic or feature space, and leverage this mapping for categorization.
• Integration: ZSL can effectively collaborate with other machine learning techniques to enhance overall system performance.
• Future Proof: As the technology for artificial intelligence and machine learning rapidly advances, ZSL provides a scalable solution for applications with evolving requirements.
  Zero-Shot Learning in computer vision is increasingly becoming an essential part of many industries and research areas due to its capabilities for handling unseen data objects with scalability and future-proof characteristics.

The deployment of ZSL in computer vision necessitates a meticulous approach that includes an in-depth analysis of specific system requirements, along with careful model selection based on dataset structure and content. A successful ZSL implementation lies on a thoughtful selection of semantic space or attributes, effective algorithm design, and the proper computational methods to map images onto this semantic space. The deployment of ZSL should be closely monitored to ensure it meets the dynamic needs of the system while addressing the inherent challenges of machine learning with scalability, flexibility, and future-proof features.

Zero-Shot Learning in computer vision presents several advantages:

• Scalability: ZSL offers scalable solutions to handle a growing number of categories in an evolving environment, permitting it to cater to continuously changing demands.
• Future Proof: ZSL's ability to handle unseen objects or cases establishes it as a future-oriented learning framework. With rapid technological advancements and growth of data, ZSL-bolstered systems are designed for longevity.
• Cost-effective: Training machine learning models traditionally require labeled data, the procurement and annotation of which can be time-consuming and costly. ZSL's utilization of auxiliary data to recognize unknown objects eliminates this cost.
• Dynamic Application Scope: The potential for ZSL extends beyond pure object identification. It can integrate with other recognition activities to enhance system performance in many fields, from medical imaging to e-commerce recommendation systems.

Conversely, there are certain limitations of Zero-Shot Learning that need to be evaluated:

• Constraints in Semantic Representation: ZSL relies on the semantic representation of categories to connect seen and unseen instances. If these representations are not accurately defined, the performance may be compromised.
• Dataset Bias: ZSL models often suffer from dataset bias, as the model can be biased towards classes with more occurrences in the training data, leading to an unbalanced classification.
• Potentially Limited Generalization: ZSL's unique ability to handle 'unseen' objects relies on the assumption that the unseen classes have some semantic or attribute-based relationship with seen classes. In practical settings, this assumption may not always hold.