Unless you’re working at Google, then maybe you use JAX.

I think the only thing that could have saved TensorFlow at that point would have been some sort of enormous performance boost that would only work with their computation model. I'm assuming Google's plan was make it easy to run the same TensorFlow code on GPUs and TPUs, and then swoop in with TPUs that massively outperformed GPUs (at least on a performance per dollar basis). But that never really happened.

I think people have continued to work on it. There’s no single lab or developer, it mostly appears that the metrics for comparison are usually focused on the speed/MAP plane.

One nice thing is that even with modest hardware, it’s low enough latency to process video in real time.

But the exciting new research is moving beyond the narrow task of segmentation. It's not just about having new models that get better scores but building larger multimodal systems, broader task definitions etc.