  In this report, we present our approach for the Natural Language Query track
and Goal Step track of the Ego4D Episodic Memory Benchmark at CVPR 2024. Both
challenges require the localization of actions within long video sequences
using textual queries. To enhance localization accuracy, our method not only
processes the temporal information of videos but also identifies fine-grained
objects spatially within the frames. To this end, we introduce a novel
approach, termed ObjectNLQ, which incorporates an object branch to augment the
video representation with detailed object information, thereby improving
grounding efficiency. ObjectNLQ achieves a mean R@1 of 23.15, ranking 2nd in
the Natural Language Queries Challenge, and gains 33.00 in terms of the metric
R@1, IoU=0.3, ranking 3rd in the Goal Step Challenge. Our code will be released
at https://github.com/Yisen-Feng/ObjectNLQ.
