  Situation recognition refers to the ability of an agent to identify and
understand various situations or contexts based on available information and
sensory inputs. It involves the cognitive process of interpreting data from the
environment to determine what is happening, what factors are involved, and what
actions caused those situations. This interpretation of situations is
formulated as a semantic role labeling problem in computer vision-based
situation recognition. Situations depicted in images and videos hold pivotal
information, essential for various applications like image and video
captioning, multimedia retrieval, autonomous systems and event monitoring.
However, existing methods often struggle with ambiguity and lack of context in
generating meaningful and accurate predictions. Leveraging multimodal models
such as CLIP, we propose ClipSitu, which sidesteps the need for full
fine-tuning and achieves state-of-the-art results in situation recognition and
localization tasks. ClipSitu harnesses CLIP-based image, verb, and role
embeddings to predict nouns fulfilling all the roles associated with a verb,
providing a comprehensive understanding of depicted scenarios. Through a
cross-attention Transformer, ClipSitu XTF enhances the connection between
semantic role queries and visual token representations, leading to superior
performance in situation recognition. We also propose a verb-wise role
prediction model with near-perfect accuracy to create an end-to-end framework
for producing situational summaries for out-of-domain images. We show that
situational summaries empower our ClipSitu models to produce structured
descriptions with reduced ambiguity compared to generic captions. Finally, we
extend ClipSitu to video situation recognition to showcase its versatility and
produce comparable performance to state-of-the-art methods.
