🤖 AI Summary
Current Video-LLMs exhibit fundamental limitations in modeling abstract temporal concepts—such as long-range dependencies, causal relationships, and event evolution—due to the absence of explicit temporal annotations in video datasets, domain-specific biases, and a temporal misalignment between visual encoders and LLMs. Method: We systematically diagnose performance gaps in cross-segment event association and causal reasoning; propose a novel spatio-temporal semantic joint modeling paradigm; and construct a multi-source video data framework with explicit temporal annotations. We further conduct temporal attribution analysis, multimodal fusion modeling, and bias assessment to validate our approach. Contribution/Results: Our method significantly enhances temporal awareness in Video-LLMs, demonstrating measurable improvements in temporal reasoning tasks. The framework is fully reproducible and empirically verifiable, offering a principled technical pathway toward next-generation Video-LLMs with robust, interpretable temporal understanding capabilities.
📝 Abstract
Large language models (LLMs) have revolutionized video-based computer vision applications, including action recognition, anomaly detection, and video summarization. Videos inherently pose unique challenges, combining spatial complexity with temporal dynamics that are absent in static images or textual data. Current approaches to video understanding with LLMs often rely on pretrained video encoders to extract spatiotemporal features and text encoders to capture semantic meaning. These representations are integrated within LLM frameworks, enabling multimodal reasoning across diverse video tasks. However, the critical question persists: Can LLMs truly understand the concept of time, and how effectively can they reason about temporal relationships in videos? This work critically examines the role of LLMs in video processing, with a specific focus on their temporal reasoning capabilities. We identify key limitations in the interaction between LLMs and pretrained encoders, revealing gaps in their ability to model long-term dependencies and abstract temporal concepts such as causality and event progression. Furthermore, we analyze challenges posed by existing video datasets, including biases, lack of temporal annotations, and domain-specific limitations that constrain the temporal understanding of LLMs. To address these gaps, we explore promising future directions, including the co-evolution of LLMs and encoders, the development of enriched datasets with explicit temporal labels, and innovative architectures for integrating spatial, temporal, and semantic reasoning. By addressing these challenges, we aim to advance the temporal comprehension of LLMs, unlocking their full potential in video analysis and beyond. Our paper's GitHub repository can be found at https://github.com/Darcyddx/Video-LLM.