3 research outputs found
Do we really need temporal convolutions in action segmentation?
Action classification has made great progress, but segmenting and recognizing
actions from long untrimmed videos remains a challenging problem. Most
state-of-the-art methods focus on designing temporal convolution-based models,
but the inflexibility of temporal convolutions and the difficulties in modeling
long-term temporal dependencies restrict the potential of these models.
Transformer-based models with adaptable and sequence modeling capabilities have
recently been used in various tasks. However, the lack of inductive bias and
the inefficiency of handling long video sequences limit the application of
Transformer in action segmentation. In this paper, we design a pure
Transformer-based model without temporal convolutions by incorporating temporal
sampling, called Temporal U-Transformer (TUT). The U-Transformer architecture
reduces complexity while introducing an inductive bias that adjacent frames are
more likely to belong to the same class, but the introduction of coarse
resolutions results in the misclassification of boundaries. We observe that the
similarity distribution between a boundary frame and its neighboring frames
depends on whether the boundary frame is the start or end of an action segment.
Therefore, we further propose a boundary-aware loss based on the distribution
of similarity scores between frames from attention modules to enhance the
ability to recognize boundaries. Extensive experiments show the effectiveness
of our model