  Vision systems to see and reason about the compositional nature of visual
scenes are fundamental to understanding our world. The complex relations
between objects and their locations, ambiguities, and variations in the
real-world environment can be better described in human language, naturally
governed by grammatical rules and other modalities such as audio and depth. The
models learned to bridge the gap between such modalities coupled with
large-scale training data facilitate contextual reasoning, generalization, and
prompt capabilities at test time. These models are referred to as foundational
models. The output of such models can be modified through human-provided
prompts without retraining, e.g., segmenting a particular object by providing a
bounding box, having interactive dialogues by asking questions about an image
or video scene or manipulating the robot's behavior through language
instructions. In this survey, we provide a comprehensive review of such
emerging foundational models, including typical architecture designs to combine
different modalities (vision, text, audio, etc), training objectives
(contrastive, generative), pre-training datasets, fine-tuning mechanisms, and
the common prompting patterns; textual, visual, and heterogeneous. We discuss
the open challenges and research directions for foundational models in computer
vision, including difficulties in their evaluations and benchmarking, gaps in
their real-world understanding, limitations of their contextual understanding,
biases, vulnerability to adversarial attacks, and interpretability issues. We
review recent developments in this field, covering a wide range of applications
of foundation models systematically and comprehensively. A comprehensive list
of foundational models studied in this work is available at
\url{https://github.com/awaisrauf/Awesome-CV-Foundational-Models}.
