  Videos contain rich spatio-temporal information. Traditional methods for
extracting motion, used in tasks such as action recognition, often rely on
visual contents rather than precise motion features. This phenomenon is
referred to as 'blind motion extraction' behavior, which proves inefficient in
capturing motions of interest due to a lack of motion-guided cues. Recently,
attention mechanisms have enhanced many computer vision tasks by effectively
highlighting salient visual areas. Inspired by this, we propose using a
modified Sigmoid function with learnable slope and shift parameters as an
attention mechanism to activate and modulate motion signals derived from frame
differencing maps. This approach generates a sequence of attention maps that
enhance the processing of motion-related video content. To ensure temporally
continuity and smoothness of the attention maps, we apply pair-wise temporal
attention variation regularization to remove unwanted motions (e.g., noise)
while preserving important ones. We then perform Hadamard product between each
pair of attention maps and the original video frames to highlight the evolving
motions of interest over time. These highlighted motions, termed video motion
prompts, are subsequently used as inputs to the model instead of the original
video frames. We formalize this process as a motion prompt layer and
incorporate the regularization term into the loss function to learn better
motion prompts. This layer serves as an adapter between the model and the video
data, bridging the gap between traditional 'blind motion extraction' and the
extraction of relevant motions of interest.
