  Previous methods usually only extract the image modality's information to
recognize group activity. However, mining image information is approaching
saturation, making it difficult to extract richer information. Therefore,
extracting complementary information from other modalities to supplement image
information has become increasingly important. In fact, action labels provide
clear text information to express the action's semantics, which existing
methods often overlook. Thus, we propose ActivityCLIP, a plug-and-play method
for mining the text information contained in the action labels to supplement
the image information for enhancing group activity recognition. ActivityCLIP
consists of text and image branches, where the text branch is plugged into the
image branch (The off-the-shelf image-based method). The text branch includes
Image2Text and relation modeling modules. Specifically, we propose the
knowledge transfer module, Image2Text, which adapts image information into text
information extracted by CLIP via knowledge distillation. Further, to keep our
method convenient, we add fewer trainable parameters based on the relation
module of the image branch to model interaction relation in the text branch. To
show our method's generality, we replicate three representative methods by
ActivityCLIP, which adds only limited trainable parameters, achieving favorable
performance improvements for each method. We also conduct extensive ablation
studies and compare our method with state-of-the-art methods to demonstrate the
effectiveness of ActivityCLIP.
