  Video captioning aims to automatically generate natural language sentences
that can describe the visual contents of a given video. Existing generative
models like encoder-decoder frameworks cannot explicitly explore the
object-level interactions and frame-level information from complex
spatio-temporal data to generate semantic-rich captions. Our main contribution
is to identify three key problems in a joint framework for future video
summarization tasks. 1) Enhanced Object Proposal: we propose a novel
Conditional Graph that can fuse spatio-temporal information into latent object
proposal. 2) Visual Knowledge: Latent Proposal Aggregation is proposed to
dynamically extract visual words with higher semantic levels. 3) Sentence
Validation: A novel Discriminative Language Validator is proposed to verify
generated captions so that key semantic concepts can be effectively preserved.
Our experiments on two public datasets (MVSD and MSR-VTT) manifest significant
improvements over state-of-the-art approaches on all metrics, especially for
BLEU-4 and CIDEr. Our code is available at
https://github.com/baiyang4/D-LSG-Video-Caption.
