A method of pixel-wise localization of an actor and an action in a sequence of frames (650, 654), comprising: receiving a natural language query describing the action and the actor; receiving the sequence of frames (650, 654); and localizing the action and the actor in the sequence of frames (650, 654) based on the natural language query, in which localizing the action and the actor comprises: generating a first set of dynamic convolutional filters based on the natural language query; convolving a visual representation of each frame of the sequence of frames (650, 654) with the first set of dynamic convolutional filters to generate a first response map; applying a label to each pixel in each frame of the sequence of frames (650, 654) based on the first response map; up-sampling the resolution of the visual representation of the sequence of frames (650, 654); generating a second set of dynamic convolutional filters based on the natural language query and the up-sampled resolution; convolving the up-sampled visual representation with the second set of dynamic convolutional filters to generate a second response map; applying a label to each pixel in each frame of the sequence of frames (650, 654) based on each of the response maps (818a, 818b); and localizing the action and the actor in each frame based on the labels. receiving a natural language query describing the action and the actor; receiving the sequence of frames (650, 654); and localizing the action and the actor in the sequence of frames (650, 654) based on the natural language query, in which localizing the action and the actor comprises: generating a first set of dynamic convolutional filters based on the natural language query; convolving a visual representation of each frame of the sequence of frames (650, 654) with the first set of dynamic convolutional filters to generate a first response map; applying a label to each pixel in each frame of the sequence of frames (650, 654) based on the first response map; up-sampling the resolution of the visual representation of the sequence of frames (650, 654); generating a second set of dynamic convolutional filters based on the natural language query and the up-sampled resolution; convolving the up-sampled visual representation with the second set of dynamic convolutional filters to generate a second response map; applying a label to each pixel in each frame of the sequence of frames (650, 654) based on each of the response maps (818a, 818b); and localizing the action and the actor in each frame based on the labels. generating a first set of dynamic convolutional filters based on the natural language query; convolving a visual representation of each frame of the sequence of frames (650, 654) with the first set of dynamic convolutional filters to generate a first response map; applying a label to each pixel in each frame of the sequence of frames (650, 654) based on the first response map; up-sampling the resolution of the visual representation of the sequence of frames (650, 654); generating a second set of dynamic convolutional filters based on the natural language query and the up-sampled resolution; convolving the up-sampled visual representation with the second set of dynamic convolutional filters to generate a second response map; applying a label to each pixel in each frame of the sequence of frames (650, 654) based on each of the response maps (818a, 818b); and localizing the action and the actor in each frame based on the labels.