  The rapid growth of video on the internet has made searching for video
content using natural language queries a significant challenge. Human-generated
queries for video datasets `in the wild' vary a lot in terms of degree of
specificity, with some queries describing specific details such as the names of
famous identities, content from speech, or text available on the screen. Our
goal is to condense the multi-modal, extremely high dimensional information
from videos into a single, compact video representation for the task of video
retrieval using free-form text queries, where the degree of specificity is
open-ended.
  For this we exploit existing knowledge in the form of pre-trained semantic
embeddings which include 'general' features such as motion, appearance, and
scene features from visual content. We also explore the use of more 'specific'
cues from ASR and OCR which are intermittently available for videos and find
that these signals remain challenging to use effectively for retrieval. We
propose a collaborative experts model to aggregate information from these
different pre-trained experts and assess our approach empirically on five
retrieval benchmarks: MSR-VTT, LSMDC, MSVD, DiDeMo, and ActivityNet. Code and
data can be found at www.robots.ox.ac.uk/~vgg/research/collaborative-experts/.
This paper contains a correction to results reported in the previous version.

Computer Vision and Pattern Recognition