  Object proposal generation serves as a standard pre-processing step in
Vision-Language (VL) tasks (image captioning, visual question answering, etc.).
The performance of object proposals generated for VL tasks is currently
evaluated across all available annotations, a protocol that we show is
misaligned - higher scores do not necessarily correspond to improved
performance on downstream VL tasks. Our work serves as a study of this
phenomenon and explores the effectiveness of semantic grounding to mitigate its
effects. To this end, we propose evaluating object proposals against only a
subset of available annotations, selected by thresholding an annotation
importance score. Importance of object annotations to VL tasks is quantified by
extracting relevant semantic information from text describing the image. We
show that our method is consistent and demonstrates greatly improved alignment
with annotations selected by image captioning metrics and human annotation when
compared against existing techniques. Lastly, we compare current detectors used
in the Scene Graph Generation (SGG) benchmark as a use case, which serves as an
example of when traditional object proposal evaluation techniques are
misaligned.
