Effectively Leveraging CLIP for Generating Situational Summaries of Images and Videos

TL;DR

This paper introduces ClipSitu, a multimodal approach leveraging CLIP for accurate situation recognition in images and videos, providing structured, less ambiguous summaries without extensive fine-tuning.

Contribution

The paper presents ClipSitu, a novel method that uses CLIP embeddings and a cross-attention Transformer to improve situation recognition and generate structured summaries for images and videos.

Findings

Achieves state-of-the-art results in situation recognition and localization.

Provides structured, less ambiguous situational summaries.

Extends to video situation recognition with competitive performance.

Abstract

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.