Learning Multi-modal Representations by Watching Hundreds of Surgical Video Lectures
CoRR(2023)
摘要
Recent advancements in surgical computer vision applications have been driven
by fully-supervised methods, primarily using only visual data. These methods
rely on manually annotated surgical videos to predict a fixed set of object
categories, limiting their generalizability to unseen surgical procedures and
downstream tasks. In this work, we put forward the idea that the surgical video
lectures available through open surgical e-learning platforms can provide
effective supervisory signals for multi-modal representation learning without
relying on manual annotations. We address the surgery-specific linguistic
challenges present in surgical video lectures by employing multiple
complementary automatic speech recognition systems to generate text
transcriptions. We then present a novel method, SurgVLP - Surgical Vision
Language Pre-training, for multi-modal representation learning. SurgVLP
constructs a new contrastive learning objective to align video clip embeddings
with the corresponding multiple text embeddings by bringing them together
within a joint latent space. To effectively show the representation capability
of the learned joint latent space, we introduce several vision-and-language
tasks for surgery, such as text-based video retrieval, temporal activity
grounding, and video captioning, as benchmarks for evaluation. We further
demonstrate that without using any labeled ground truth, our approach can be
employed for traditional vision-only surgical downstream tasks, such as
surgical tool, phase, and triplet recognition. The code will be made available
at https://github.com/CAMMA-public/SurgVLP
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关键词
video lectures,representations,learning,multi-modal
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