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

Discriminative Training: Learning to Describe Video with Sentences, from Video Described with Sentences