  The query-based moment retrieval is a problem of localising a specific clip
from an untrimmed video according a query sentence. This is a challenging task
that requires interpretation of both the natural language query and the video
content. Like in many other areas in computer vision and machine learning, the
progress in query-based moment retrieval is heavily driven by the benchmark
datasets and, therefore, their quality has significant impact on the field. In
this paper, we present a series of experiments assessing how well the benchmark
results reflect the true progress in solving the moment retrieval task. Our
results indicate substantial biases in the popular datasets and unexpected
behaviour of the state-of-the-art models. Moreover, we present new sanity check
experiments and approaches for visualising the results. Finally, we suggest
possible directions to improve the temporal sentence grounding in the future.
Our code for this paper is available at
https://mayu-ot.github.io/hidden-challenges-MR .
