  We present a method for learning word meanings from complex and realistic
video clips by discriminatively training (DT) positive sentential labels
against negative ones, and then use the trained word models to generate
sentential descriptions for new video. This new work is inspired by recent work
which adopts a maximum likelihood (ML) framework to address the same problem
using only positive sentential labels. The new method, like the ML-based one,
is able to automatically determine which words in the sentence correspond to
which concepts in the video (i.e., ground words to meanings) in a weakly
supervised fashion. While both DT and ML yield comparable results with
sufficient training data, DT outperforms ML significantly with smaller training
sets because it can exploit negative training labels to better constrain the
learning problem.
