Scene Graph-guided SegCaptioning Transformer with Fine-grained Alignment for Controllable Video Segmentation and Captioning

Abstract

Recent advancements in multimodal large models have significantly bridged the representation gap between diverse modalities, catalyzing the evolution of video multimodal interpretation, which enhances users' understanding of video content by generating correlated modalities. However, most existing video multimodal interpretation methods primarily concentrate on global comprehension with limited user interaction. To address this, we propose a novel task, Controllable Video Segmentation and Captioning (SegCaptioning), which empowers users to provide specific prompts, such as a bounding box around an object of interest, to simultaneously generate correlated masks and captions that precisely embody user intent. An innovative framework Scene Graph-guided Fine-grained SegCaptioning Transformer (SG-FSCFormer) is designed that integrates a Prompt-guided Temporal Graph Former to effectively captures and represents user intent through an adaptive prompt adaptor, ensuring that the generated content well aligns with the user's requirements. Furthermore, our model introduces a Fine-grained Mask-linguistic Decoder to collaboratively predict high-quality caption-mask pairs using a Multi-entity Contrastive loss, as well as provide fine-grained alignment between each mask and its corresponding caption tokens, thereby enhancing users' comprehension of videos. Comprehensive experiments conducted on two benchmark datasets demonstrate that SG-FSCFormer achieves remarkable performance, effectively capturing user intent and generating precise multimodal outputs tailored to user specifications. Our code is available at https://github.com/XuZhang1211/SG-FSCFormer.

Figures (6)

• Figure 1: An example to illustrate the difference between the segmentation and captioning method and our approach, which allows users to provide a bounding box to generate multimodal outputs that are tailored to the user's intent.
• Figure 2: Framework of our Scene Graph-guided Fine-grained SegCaptioning Transformer (SG-FSCFormer), consisting of three components: a Visual Encoder, a Prompt-guided Temporal Graph Former (PTGFormer), and a Fine-grained Mask-Linguistic Decoder (MLDecoder), where the mathematical symbols are explained in the corresponding modules.
• Figure 3: Prediction by the revised decoder which is able to output the position of caption words for each mask instance.
• Figure 4: An example illustrating our data annotation.
• Figure 5: Quantitative results by different approaches tested on the LV-VIS and OVIS datasets, where the color indicates the matching relationship between masks and words.