Training-free Video Temporal Grounding using Large-scale Pre-trained Models (2408.16219v1)

Abstract: Video temporal grounding aims to identify video segments within untrimmed videos that are most relevant to a given natural language query. Existing video temporal localization models rely on specific datasets for training and have high data collection costs, but they exhibit poor generalization capability under the across-dataset and out-of-distribution (OOD) settings. In this paper, we propose a Training-Free Video Temporal Grounding (TFVTG) approach that leverages the ability of pre-trained large models. A naive baseline is to enumerate proposals in the video and use the pre-trained visual LLMs (VLMs) to select the best proposal according to the vision-language alignment. However, most existing VLMs are trained on image-text pairs or trimmed video clip-text pairs, making it struggle to (1) grasp the relationship and distinguish the temporal boundaries of multiple events within the same video; (2) comprehend and be sensitive to the dynamic transition of events (the transition from one event to another) in the video. To address these issues, we propose leveraging LLMs to analyze multiple sub-events contained in the query text and analyze the temporal order and relationships between these events. Secondly, we split a sub-event into dynamic transition and static status parts and propose the dynamic and static scoring functions using VLMs to better evaluate the relevance between the event and the description. Finally, for each sub-event description, we use VLMs to locate the top-k proposals and leverage the order and relationships between sub-events provided by LLMs to filter and integrate these proposals. Our method achieves the best performance on zero-shot video temporal grounding on Charades-STA and ActivityNet Captions datasets without any training and demonstrates better generalization capabilities in cross-dataset and OOD settings.

Training-free Video Temporal Grounding using Large-scale Pre-trained Models

The paper "Training-free Video Temporal Grounding using Large-scale Pre-trained Models" presents an innovative methodology for video temporal grounding by utilizing pre-existing large-scale pre-trained models without requiring task-specific training. This approach, termed Training-Free Video Temporal Grounding (TFVTG), addresses significant issues with existing models which often require extensive datasets for training and consequently demonstrate poor generalization across different datasets and out-of-distribution (OOD) scenarios.

The core challenge addressed by video temporal grounding is identifying relevant segments within untrimmed videos based on natural language queries. Traditional models typically depend heavily on large, annotated datasets. Their generalizability is hampered by their reliance on specific training distributions, rendering them prone to significant performance drops when faced with novel data. TFVTG circumvents these challenges by leveraging the capabilities of LLMs and Vision LLMs (VLMs), which have shown robust zero-shot performance in various multimedia tasks.

The authors propose a model that circumvents the need for specific training by directly integrating powerful pre-trained LLMs and VLMs into the temporal grounding task. LLMs are utilized to dissect the natural language query into component sub-events and to infer their temporal order and relationships. Meanwhile, VLMs are employed to localize these sub-events in the video using a sophisticated scoring mechanism that separately evaluates dynamic and static segments of potential event occurrences.

For dynamic scoring, the approach identifies segments that exhibit rapid increases in video-text similarity, indicating the beginning of an event. Static scoring, on the other hand, focuses on capturing segments where the sustained text-video similarity is high, marking the continuation or culmination of an event. This twofold scoring strategy distinctly aids VLMs, traditionally less sensitive to dynamic video transitions due to their typical pre-training on static image-text or trimmed video clip-text datasets.

The paper reports significant zero-shot performance on Charades-STA and ActivityNet Captions datasets, surpassing existing training-based methods, notably under IID and OOD conditions. Further evaluation under novel location and novel text settings (using the Charades-CD and Charades-CG datasets) as well as cross-dataset generalization confirms TFVTG's superior adaptability and validity across diverse scenarios. This robustness highlights TFVTG as a favorable candidate for real-world applications, where labeled datasets may be limited or biased.

The implications of this research are profound. By successfully demonstrating a training-free approach that outperforms or matches conventional methodologies, this work aligns closely with the projected future of artificial intelligence where models can be adaptively repurposed across tasks. Although the proposed TFVTG model significantly reduces reliance on annotated datasets, ensuring reliable LLM outputs for query decomposition and interpretation remains a challenge. Future research may explore methods for enhancing LLM reliability and extending the TFVTG framework to even broader video analysis applications.

Overall, TFVTG represents a promising stride in exploiting large pre-trained models, propelling the field of video temporal grounding towards more flexible, efficient, and scalable solutions.