  Surgical action localization is a challenging computer vision problem. While
it has promising applications including automated training of surgery
procedures, surgical workflow optimization, etc., appropriate model design is
pivotal to accomplishing this task. Moreover, the lack of suitable medical
datasets adds an additional layer of complexity. To that effect, we introduce a
new complex dataset of nephrectomy surgeries called UroSlice. To perform the
action localization from these videos, we propose a novel model termed as
`ViTALS' (Vision Transformer for Action Localization in Surgical Nephrectomy).
Our model incorporates hierarchical dilated temporal convolution layers and
inter-layer residual connections to capture the temporal correlations at finer
as well as coarser granularities. The proposed approach achieves
state-of-the-art performance on Cholec80 and UroSlice datasets (89.8% and 66.1%
accuracy, respectively), validating its effectiveness.
