  Generating surgical reports aimed at surgical scene understanding in
robot-assisted surgery can contribute to documenting entry tasks and
post-operative analysis. Despite the impressive outcome, the deep learning
model degrades the performance when applied to different domains encountering
domain shifts. In addition, there are new instruments and variations in
surgical tissues appeared in robotic surgery. In this work, we propose
class-incremental domain adaptation (CIDA) with a multi-layer transformer-based
model to tackle the new classes and domain shift in the target domain to
generate surgical reports during robotic surgery. To adapt incremental classes
and extract domain invariant features, a class-incremental (CI) learning method
with supervised contrastive (SupCon) loss is incorporated with a feature
extractor. To generate caption from the extracted feature, curriculum by
one-dimensional gaussian smoothing (CBS) is integrated with a multi-layer
transformer-based caption prediction model. CBS smoothes the features embedding
using anti-aliasing and helps the model to learn domain invariant features. We
also adopt label smoothing (LS) to calibrate prediction probability and obtain
better feature representation with both feature extractor and captioning model.
The proposed techniques are empirically evaluated by using the datasets of two
surgical domains, such as nephrectomy operations and transoral robotic surgery.
We observe that domain invariant feature learning and the well-calibrated
network improves the surgical report generation performance in both source and
target domain under domain shift and unseen classes in the manners of one-shot
and few-shot learning. The code is publicly available at
https://github.com/XuMengyaAmy/CIDACaptioning.
