Effectively Leveraging CLIP for Generating Situational Summaries of Images and Videos

2024-07-30

arXiv.org

AI Summary

Existing image and video situation recognition methods suffer from semantic ambiguity and imprecise localization due to insufficient contextual modeling. To address this, we propose ClipSitu—a lightweight CLIP-adapted framework that enables situation understanding without full-parameter fine-tuning. ClipSitu introduces a novel verb-guided role prediction paradigm, integrating cross-attention via the eXtended Token Fusion (XTF) mechanism and explicit visual token–semantic role alignment to decouple verb and role joint modeling. The method supports zero-shot out-of-domain structured summarization, achieving state-of-the-art performance in both situation localization accuracy and semantic completeness. Experiments demonstrate that structured summaries generated by ClipSitu reduce ambiguity by 42% compared to generic captions; moreover, its video extension matches or exceeds the performance of leading contemporary approaches while maintaining computational efficiency.

Technology Category

• Computer Vision: Video Understanding & Activity Analysis
• Natural Language Processing: Summarization
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Improves situation recognition in images and videos.
• Reduces ambiguity in generating situational summaries.
• Enhances performance in semantic role labeling tasks.

Innovation

• ClipSitu uses CLIP embeddings for situation recognition.
• ClipSitu XTF enhances visual-semantic connections via Transformer.
• Verb-wise role prediction achieves near-perfect accuracy.