  Large language models (LLMs) have shown their capabilities in understanding
contextual and semantic information regarding knowledge of instance
appearances. In this paper, we introduce a novel approach to utilize the
strengths of LLMs in understanding contextual appearance variations and to
leverage this knowledge into a vision model (here, pedestrian detection). While
pedestrian detection is considered one of the crucial tasks directly related to
our safety (e.g., intelligent driving systems), it is challenging because of
varying appearances and poses in diverse scenes. Therefore, we propose to
formulate language-derived appearance elements and incorporate them with visual
cues in pedestrian detection. To this end, we establish a description corpus
that includes numerous narratives describing various appearances of pedestrians
and other instances. By feeding them through an LLM, we extract appearance
knowledge sets that contain the representations of appearance variations.
Subsequently, we perform a task-prompting process to obtain appearance elements
which are guided representative appearance knowledge relevant to a downstream
pedestrian detection task. The obtained knowledge elements are adaptable to
various detection frameworks, so that we can provide plentiful appearance
information by integrating the language-derived appearance elements with visual
cues within a detector. Through comprehensive experiments with various
pedestrian detectors, we verify the adaptability and effectiveness of our
method showing noticeable performance gains and achieving state-of-the-art
detection performance on two public pedestrian detection benchmarks (i.e.,
CrowdHuman and WiderPedestrian).
