Siamese Learning for Weakly-Supervised Video Paragraph Grounding

Core Concepts

Introducing a novel Siamese Grounding TRansformer (SiamGTR) for efficient weakly-supervised video paragraph grounding.

Abstract

The article introduces Weakly-Supervised Video Paragraph Grounding (WSVPG) to eliminate the need for temporal annotations. It proposes a Siamese Grounding TRansformer (SiamGTR) that jointly learns cross-modal feature alignment and temporal coordinate regression without timestamp labels. The framework consists of an Augmentation Branch for pseudo video boundary regression and an Inference Branch for order-guided feature alignment in normal videos. Extensive experiments show superior performance compared to state-of-the-art methods under the same or weaker supervision.

Stats

Video lasts 117.60 seconds on average. Charades-CD-OOD dataset has 4,564 training pairs. TACoS dataset has 127 videos with 1,107 training pairs. Model trained with Adam optimizer, learning rate of 0.0001, and batch sizes of 32 or 16.

Quotes

"We introduce the task of Weakly-Supervised Video Paragraph Grounding (WSVPG), which aims to train a model for localizing multiple events indicated by queries without the supervision of timestamp labels." "Our paradigm has superior practicability and flexibility to achieve efficient weakly-supervised or semi-supervised learning." "Our contributions include introducing WSVPG, proposing SiamGTR for one-stage localization, and outperforming state-of-the-art methods."

Key Insights Distilled From

Siamese Learning with Joint Alignment and Regression for Weakly-Supervised Video Paragraph Grounding

by Chaolei Tan,... at arxiv.org 03-19-2024

https://arxiv.org/pdf/2403.11463.pdf

Deeper Inquiries

How can the proposed siamese framework be applied to other video-language understanding tasks

The proposed siamese framework can be applied to other video-language understanding tasks by adapting the model architecture and loss functions to suit the specific requirements of different tasks. For instance, in video question answering, the siamese structure can be used to align visual features with textual queries for accurate responses. Similarly, in video summarization, the framework can learn to localize key events or scenes mentioned in a textual summary within a video. By adjusting the input modalities and fine-tuning the training process, the siamese framework can effectively handle various video-language understanding tasks.

What are potential limitations or challenges in implementing weakly-supervised learning in video grounding

Implementing weakly-supervised learning in video grounding poses several limitations and challenges. One major challenge is obtaining high-quality pseudo labels for training without relying on ground-truth annotations. The quality of these pseudo labels directly impacts model performance and generalization capabilities. Additionally, weak supervision may lead to noisy or ambiguous signals during training, making it challenging for models to learn accurate temporal localization without explicit guidance. Another limitation is scalability; as weakly-supervised methods often require more complex architectures or additional constraints to compensate for the lack of supervision, which may increase computational costs and training time.

How does the concept of self-consistent boundary regression contribute to improving model performance

Self-consistent boundary regression plays a crucial role in improving model performance by selectively optimizing regression losses based on attention weights over pseudo ground-truth intervals. This approach ensures that only samples with high attention weights are used for refining boundary predictions during training, leading to more precise localization results. By focusing on self-consistent samples that have strong correlations between predicted boundaries and actual locations of events described in sentences, the model learns from reliable supervisory signals and enhances its ability to accurately predict temporal intervals without relying solely on coordinate-level supervision.