Weakly Supervised Video Scene Graph Generation via Natural Language Supervision
Keywords: Video Scene Understanding, Weakly Supervised Learning, Large Language Model
TL;DR: We propose a weakly-supervised video scene graph generation framework that aims to relieve the annotation costs by training a model using natural language supervision.
Abstract: Existing Video Scene Graph Generation (VidSGG) studies are trained in a fully supervised manner, which requires all frames in a video to be annotated, thereby incurring high annotation cost compared to Image Scene Graph Generation (ImgSGG). Although the annotation cost of VidSGG can be alleviated by adopting a weakly supervised approach commonly used for ImgSGG (WS-ImgSGG) that uses image captions, there are two key reasons that hinder such a naive adoption: 1) Temporality within video captions, i.e., unlike image captions, video captions include temporal markers (e.g., before, while, then, after) that indicate time-related details, and 2) Variability in action duration, i.e., unlike human actions in image captions, human actions in video captions unfold over varying duration. To address these issues, we propose a weakly supervised VidSGG with Natural Language Supervision (VSNLS) framework that only utilizes the readily available video captions for training a VidSGG model. VSNLS consists of two key modules: Temporality-aware Caption Segmentation (TCS) module and Action Duration Variability-aware caption-frame alignment (ADV) module. Specifically, TCS segments the video captions into multiple sentences in a temporal order based on a Large Language Model (LLM), and ADV aligns each segmented sentence with appropriate frames considering the variability in action duration. Our approach leads to a significant enhancement in performance compared to simply applying the WS-ImgSGG pipeline to VidSGG on the Action Genome dataset. As a further benefit of utilizing the video captions as weak supervision, we show that the VidSGG model trained by VSNLS is able to predict a broader range of action classes that are not included in the training data, which makes our framework practical in reality.
Primary Area: applications to computer vision, audio, language, and other modalities
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Submission Number: 3326
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