Bridge-Prompt: Towards Ordinal Action Understanding in Instructional Videos (2203.14104v1)

Abstract: Action recognition models have shown a promising capability to classify human actions in short video clips. In a real scenario, multiple correlated human actions commonly occur in particular orders, forming semantically meaningful human activities. Conventional action recognition approaches focus on analyzing single actions. However, they fail to fully reason about the contextual relations between adjacent actions, which provide potential temporal logic for understanding long videos. In this paper, we propose a prompt-based framework, Bridge-Prompt (Br-Prompt), to model the semantics across adjacent actions, so that it simultaneously exploits both out-of-context and contextual information from a series of ordinal actions in instructional videos. More specifically, we reformulate the individual action labels as integrated text prompts for supervision, which bridge the gap between individual action semantics. The generated text prompts are paired with corresponding video clips, and together co-train the text encoder and the video encoder via a contrastive approach. The learned vision encoder has a stronger capability for ordinal-action-related downstream tasks, e.g. action segmentation and human activity recognition. We evaluate the performances of our approach on several video datasets: Georgia Tech Egocentric Activities (GTEA), 50Salads, and the Breakfast dataset. Br-Prompt achieves state-of-the-art on multiple benchmarks. Code is available at https://github.com/ttlmh/Bridge-Prompt

• The paper presents the Bridge-Prompt framework, which uses text prompts to model ordinal relationships in instructional video actions.
• It employs a dual-encoder setup and contrastive learning to fuse visual and textual features for improved action segmentation.
• Experiments on GTEA, 50Salads, and Breakfast benchmarks demonstrate its state-of-the-art performance and practical relevance.

Bridge-Prompt: Towards Ordinal Action Understanding in Instructional Videos

The proliferation of video content, particularly within instructional domains, has called for enhanced frameworks capable of understanding human activities represented through sequential actions. Traditional action recognition models primarily cater to the analysis of short video segments, focusing on isolated actions without sufficiently capturing the inherent relationships between sequential actions. This paper presents the Bridge-Prompt framework, an innovative approach designed to model these semantic relationships through a prompt-based system, thereby improving the comprehension of instructional videos with chronological action sequences.

Methodology Overview

The proposed Bridge-Prompt framework employs text prompts to encapsulate the ordinal relationships and semantic meanings of actions occurring in a sequence. The framework restructures the traditional action classification labels into comprehensive text prompts that serve as an intermediate, contextual layer between the video encoder and the classic action output. This model capitalizes on the natural ability of language to describe and relate sequential actions.

The core mechanism involves a dual-encoder setup, using a vision encoder and a text encoder. The vision encoder extracts visual features from video clips, which are then paired with semantically rich text prompts generated for each sequence. These text prompts are crafted to contain ordinal information and descriptive semantics, thereby "bridging" individual action frames into a full understanding of the activity depicted.

Bridge-Prompt leverages contrastive learning to enhance the synergy between video and text encoders through co-training. This co-training is made possible via a specially designed video-text fusion module. This innovative module correlates different semantic aspects of the video and its accompanying text prompts by aligning video clips with the correct prompts.

Experimental Validation

The empirical efficacy of the Bridge-Prompt framework is demonstrated through experiments conducted on several benchmark datasets, including Georgia Tech Egocentric Activities (GTEA), 50Salads, and the Breakfast dataset. Bridge-Prompt displayed superior capabilities in recognizing complex action sequences, setting a new state-of-the-art across multiple benchmarks.

Particularly in action segmentation tasks, the framework achieved significant gains by considering the contextual continuity of actions rather than treating them as isolated events. This is crucial for applications in scenarios where understanding the progression of actions leads to a more comprehensive grasp of the activities.

Implications and Future Directions

Bridge-Prompt is notable for translating the prompt-based success observed in NLP tasks to video understanding contexts. By fostering better comprehension of sequences within instructional videos, it opens opportunities for practical applications in automated video summarization, enhancement of digital education tools, and other areas requiring robust human activity recognition.

Future research can explore the scalability of Bridge-Prompt by integrating large-scale, unlabelled video datasets. This could involve expanding the prompt diversity and enhancing the fusion model to accommodate more complex video scenarios. Additionally, investigating the few-shot and zero-shot learning potentials of this framework might offer further insights into its flexibility.

In conclusion, the Bridge-Prompt positions itself as a methodological advancement in video action understanding. Its integration of textual semantics with visual data presents a promising direction for addressing the multifaceted challenges inherent in modern video analysis.