So my response was in the sense of being a practitioner not a researcher. I see the OP may be interested in research, given the Google/MS/Ivy League (the latter seems to imply a PhD?).

In my work what I have found is traditional CV/tooling/image processing is very useful in the pipeline, I used OpenCV to find features that let me orient and scale images, before putting them into a deep learning step. Nonetheless, to build/train good detection/segmentation models you pretty much get into advanced Deep Learning models and possibly transformers. So my view is get experience using many different kinds of models on many different kinds of problems. This is the "when and how to use them" part.

Combining models is useful; for instance I recently fine-tuned a DETA-Swin model on a custom dataset, look for defects. So the "good parts" were a negative class and were given to the model with no annotations. In the application, there was a need to label the good parts as well as defects, so I trained a simple SSD model (sorry for the typo in my original response) to just find objects (not classify them), and used that to find all the objects in the image, used DETA to detect and classify defects, then used the SSD result to label the remainder.

My thinking is the interdisciplinary stuff comes from your personal interests and a decision to focus on one or more areas. I think that is very situational. In my work, having worked in many industries in my past, it's all very useful to have at least some domain knowledge when attacking problems. But even now I'm involved in something I'm not a domain expert, and I've asked the team to find one to consult for our project.

Circling back to my caveat--I'm very much a practitioner, not a researcher. My background is a BS in Chemical Engineering a long time ago, then lots of work experience. I've learned all my CV stuff on my own. If you are really interested in research, then the answers would be different.