Video LLMs for Temporal Reasoning in Long Videos (2412.02930v1)

Abstract: This paper introduces TemporalVLM, a video LLM capable of effective temporal reasoning and fine-grained understanding in long videos. At the core, our approach includes a visual encoder for mapping a long-term input video into features which are time-aware and contain both local and global cues. In particular, it first divides the input video into short-term clips, which are jointly encoded with their timestamps into time-sensitive local features. Next, the local features are passed through a bidirectional long short-term memory module for global feature aggregation. The extracted time-aware and multi-level features are important for accurate temporal reasoning and fine-grained understanding in long videos. Moreover, to facilitate the evaluation of TemporalVLM, we present a large-scale long video dataset of industry assembly processes, namely IndustryASM, which consists of videos recorded on factory floors with actions and timestamps annotated by industrial engineers for time and motion studies and temporal action segmentation evaluation. Finally, extensive experiments on datasets of long videos, including TimeIT and IndustryASM, show that TemporalVLM achieves superior performance than previous methods across temporal reasoning and fine-grained understanding tasks, namely dense video captioning, temporal video grounding, video highlight detection, and temporal action segmentation.

TemporalVLM: Advancements in Temporal Reasoning for Long Video Comprehension

The paper "Video LLMs for Temporal Reasoning in Long Videos" introduces TemporalVLM, an innovative approach for tackling the intricacies of temporal reasoning in extensive video content. Traditional video models have struggled with understanding long videos due to their inability to efficiently capture temporal nuances and fine-grained details. This paper addresses these challenges head-on, offering an enhanced framework that incorporates LLMs in conjunction with video processing techniques.

Methodology and Innovation

TemporalVLM diverges from earlier methodologies by integrating a time-aware clip encoder and a bidirectional long short-term memory (BiLSTM) module. These components aim to address the limitations of prior approaches that typically treated long videos as singular, undivided entities. The time-aware clip encoder intelligently segments a long video into multiple, short-term clips, enriching each segment with time-sensitive local features. By associating visual data with precise timestamps, the encoder ensures that temporal context is preserved and emphasized throughout the analysis.

The BiLSTM module processes these time-aware features to compute global representations, capturing both immediate fine-grained details and expansive, long-range temporal dependencies. By processing information bidirectionally, BiLSTM is particularly well-suited for tasks demanding nuanced understanding of event sequences and temporal contexts.

Evaluation and Dataset

To rigorously assess TemporalVLM's efficacy, the paper introduces IndustryASM, a robust dataset curated for evaluating temporal reasoning in industrial assembly processes. With over 4,800 videos averaging 119 seconds in duration, the dataset is meticulously annotated with action labels and timestamps, providing a critical testing ground for models aimed at industrial applications.

TemporalVLM's performance is evaluated across a spectrum of tasks: dense video captioning, temporal video grounding, video highlight detection, and temporal action segmentation. On benchmark datasets like TimeIT and the newly introduced IndustryASM, TemporalVLM consistently outperforms current state-of-the-art methods, demonstrating superior aptitude in both zero-shot and supervised scenarios.

Results and Implications

The numerical results validate the hypothesis that integrating a time-aware clip encoder with a BiLSTM module enables significant improvements in handling long videos. For instance, TemporalVLM surpasses competitors in metrics such as CIDEr for video captioning and recall metrics for temporal video grounding. This advancement underscores the model's capacity to deliver enhanced performance in extracting temporal semantics, a crucial capability for both practical applications in industrial settings and theoretical explorations in video understanding.

The implications of this work are profound, especially in sectors where precise temporal analysis of video content is necessary, such as surveillance, automated assembly, and other time-intensive processes. By potentially reducing the computational overhead and improving the fidelity of temporal representations, TemporalVLM can lead to more robust AI systems capable of interacting with environments in temporally informed manners.

Future Directions

While the paper establishes a compelling case for TemporalVLM, it also hints at future explorations. Enhancing model scalability to handle even longer videos or integrating additional modalities, such as audio, could provide even richer context and improve overall temporal reasoning. Further, exploring the application of TemporalVLM in real-time scenarios might offer new insights into its operational efficacy across diverse domains.

In conclusion, "Video LLMs for Temporal Reasoning in Long Videos" presents a sophisticated approach to understanding long video content, marking a significant step forward in the development of multimedia comprehension frameworks. As the demands for intelligent systems that can interpret nuanced temporal data grow, innovations like TemporalVLM will be central to bridging existing gaps in AI video comprehension.