VTG-GPT: Tuning-Free Zero-Shot Video Temporal Grounding with GPT (2403.02076v1)

Abstract: Video temporal grounding (VTG) aims to locate specific temporal segments from an untrimmed video based on a linguistic query. Most existing VTG models are trained on extensive annotated video-text pairs, a process that not only introduces human biases from the queries but also incurs significant computational costs. To tackle these challenges, we propose VTG-GPT, a GPT-based method for zero-shot VTG without training or fine-tuning. To reduce prejudice in the original query, we employ Baichuan2 to generate debiased queries. To lessen redundant information in videos, we apply MiniGPT-v2 to transform visual content into more precise captions. Finally, we devise the proposal generator and post-processing to produce accurate segments from debiased queries and image captions. Extensive experiments demonstrate that VTG-GPT significantly outperforms SOTA methods in zero-shot settings and surpasses unsupervised approaches. More notably, it achieves competitive performance comparable to supervised methods. The code is available on https://github.com/YoucanBaby/VTG-GPT

Overview of "VTG-GPT: Tuning-Free Zero-Shot Video Temporal Grounding with GPT"

The paper "VTG-GPT: Tuning-Free Zero-Shot Video Temporal Grounding with GPT" introduces a novel framework named VTG-GPT, designed to tackle the challenges associated with Video Temporal Grounding (VTG) without the need for fine-tuning or supervision. This approach leverages Generative Pre-trained Transformers (GPT), specifically Baichuan2 and MiniGPT-v2, to address the task of identifying temporal segments in videos that correspond to a given linguistic query. The proposed method stands out for its capacity to operate in a zero-shot manner, which involves making predictions on tasks without prior exposure to task-specific training data.

The authors address significant challenges within the VTG domain, including the reliance on extensive annotated datasets and the biases introduced by human-annotated queries. Existing VTG models primarily depend on the extensive availability of annotated video-text pairs, which not only incurs high computational costs but also embeds the biases inherent in human annotations. The VTG-GPT framework proposes a novel solution by employing the GPT-based method for zero-shot VTG, eliminating the need for any training or fine-tuning.

Methodology

The VTG-GPT model comprises several key components aimed at mitigating the impact of human annotation biases and optimizing query processing:

1. Query Debiasing: VTG-GPT employs Baichuan2 to generate debiased queries from the original human-annotated queries. This step involves correcting erroneous spellings and eliminating incorrect descriptions in the queries, thereby reducing the prejudices that may affect model performance.
2. Image Captioning: To convert the visual content of videos into semantic textual descriptions, VTG-GPT uses MiniGPT-v2. This transformation aims to minimize redundant information present in the videos, aligning them more closely with the linguistic queries to aid in precise temporal grounding.
3. Proposal Generator: A proposal generation mechanism is used to create temporal segments based on the similarity calculations between the debiased queries and the image captions. This involves computing dynamic threshold-based proposals to handle the variability in similarity distributions across different query-video pairs.
4. Post-processing with Non-Maximum Suppression (NMS): To finalize the segment predictions, NMS is applied to remove overlapping proposals, ensuring that only the most relevant temporal segments are retained.

Results and Evaluation

The paper presents extensive experimental results conducted on benchmark datasets such as QVHighlights, Charades-STA, and ActivityNet-Captions. VTG-GPT demonstrates superior performance in zero-shot settings, significantly surpassing state-of-the-art methods across multiple evaluation metrics, including Recall and mean Average Precision (mAP). Notably, VTG-GPT achieves competitive results compared to fully supervised methods, underscoring its effectiveness even without the typical requirement for annotated datasets and model training.

Implications and Future Directions

The theoretical implications of VTG-GPT extend to the domain of zero-shot learning and the use of LLMs in video understanding tasks. Its ability to operate without fine-tuning is indicative of the growing potential of generative models in addressing multi-modal tasks directly through inference.

On a practical level, VTG-GPT offers significant advantages in applications where large-scale annotations are impractical. The reduction of dependency on human-generated biases can lead to more generalizable models deployed across diverse video content.

Looking forward, the development of more efficient video-based GPT models could enhance the temporal modeling capabilities of VTG-GPT, addressing the limitations identified regarding the context length in visual data. Additionally, extending this tuning-free methodology to other AI domains, such as video summarization and depth estimation, could further demonstrate the utility of such frameworks in tackling various data-driven challenges.