Effectively Leveraging CLIP for Generating Situational Summaries of Images and Videos

TL;DR

ClipSitu introduces a CLIP-based framework for generating structured situational summaries from images and videos by predicting verbs and semantic roles without fine-tuning the CLIP backbone. It bundles three noun-prediction architectures (Verb MLP, ClipSitu MLP, ClipSitu TF) and a cross-attention variant (ClipSitu XTF), augmented with a transformer decoder for video and a role-prediction module for end-to-end descriptions. Across imSitu and VidSitu, ClipSitu achieves state-of-the-art noun localization and strong semantic role labeling while remaining parameter-efficient compared to large vision-language models. The work demonstrates robust, grounded, action-centric descriptions for in-domain and out-of-domain imagery, with clear trade-offs relative to large VLMs and guidance for practical deployment and future extensions.

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.

Figures (12)

• Figure 1: Visual Understanding with Structured Situational Descriptions. Each situation structure contains a verb in bold followed by the roles in blue and the noun fulfilling that role. Structured situational descriptions provide a detailed and organized view of visual scenes, empowering assistive systems to understand, reason about, and interact with the world more effectively. From robot navigation liu2021robot, autonomous vehicles malla2020titan, video surveillance yuan2024towardsvishwakarma2013survey to virtual assistants chiba2021dialogue, structured representations enable a wide range of applications across diverse domains.
• Figure 2: Architecture of (a) ClipSitu MLP and (b) ClipSitu TF. We use pooled image embedding from the CLIP image encoder for ClipSitu MLP and TF.
• Figure 3: Architecture of ClipSitu XTF. We use embeddings from each patch of the image obtained from CLIP image encoder.
• Figure 4: Extending ClipSitu models (XTF, TF, and MLP) for Video Situation Recognition (VidSitu). For every event video (2s duration) from a 10s video, X-CLIP ma2022x provides the video embeddings. We use a transformer decoder (TxD) for generating the nouns playing different roles for the verb of an event. We generate nouns for all 5 events in a video together. For ClipSitu XTF, verb and role acts as query and unpooled X-CLIP embeddings as key and value. ClipSitu XTF uses event-aware masking i.e. verb $i$ (+roles) tokens attend only to event $i$ tokens. For ClipSitu MLP, we collect the output of all 5 events before sending to the transformer decoder.
• Figure 5: Effect of the number of MLP blocks and hidden dimensions on value and value-all. We train with very large hidden dimensions such as 8192, 16384, and 32768 to obtain state-of-the-art value and value-all results.