Temporal-Oriented Recipe for Transferring Large Vision-Language Model to Video Understanding
Thong Nguyen, Zhiyuan Hu, Xu Lin, Cong-Duy Nguyen, See-Kiong Ng, Luu Anh Tuan
TL;DR
This paper investigates the key factors affecting temporal understanding in large vision-language models and proposes a new training approach and interface enhancements to improve video understanding performance.
Contribution
It provides an empirical analysis of components influencing temporal understanding in LVLMs and introduces a novel temporal-oriented training recipe with interface improvements.
Findings
Enhanced performance on standard video understanding tasks
Identified the importance of the interface between visual encoder and language model
Proposed training schemes improve temporal comprehension in LVLMs
Abstract
Recent years have witnessed outstanding advances of large vision-language models (LVLMs). In order to tackle video understanding, most of them depend upon their implicit temporal understanding capacity. As such, they have not deciphered important components that contribute to temporal understanding ability, which might limit the potential of these LVLMs for video understanding. In this work, we conduct a thorough empirical study to demystify crucial components that influence the temporal understanding of LVLMs. Our empirical study reveals that significant impacts are centered around the intermediate interface between the visual encoder and the large language model. Building on these insights, we propose a temporal-oriented recipe that encompasses temporal-oriented training schemes and an upscaled interface. Our final model developed using our recipe significantly enhances previous LVLMs…
Peer Reviews
Decision·Submitted to ICLR 2026
**Addresses a Critical Problem**: The work tackles a fundamental and widely recognized weakness of temporal reasoning in video-language models. Improving performance on causality, action sequencing, and long-range dependencies is a crucial step for the field, and in general discovering better techniques for modeling. This makes the topic at hand very important. **Focus on Ablations**: The authors are very focused on performing detailed ablations, which is appreciated. Each step is compared aga
**Limited Novelty of Recipe Design and Ablations**: As is, the work reads more as an extensive ablation study than a conference ready publication, and that a few different ideas were tried and then stitched together with no cohesion. The paper's contribution lies in the novel combination and empirical validation of existing techniques rather than the introduction of a new method. Q-Formers, memory banks, MoE, and temporal captioning/grounding tasks are all established concepts. As such, the work
+ This paper is easy to follow. + The step-by-step empirical investigation is commendable. The ablation studies for each component are clear and provide a strong, reproducible blueprint for building temporally-aware LVLMs. + The paper validates its recipe across multiple tasks (VideoQA, Captioning, Temporal Grounding) and datasets, including both short-term and long-term video understanding benchmarks, demonstrating the generalizability of the approach.
1. Novelty: - The core contribution is the combination of existing components rather than the invention of new ones. Q-Former, Memory Banks, MoE, and the temporal training objectives (VC, MC, MG, DC) are all established techniques. The contribution is primarily the empirical finding that this specific combination works well. 2. Theoretical Analysis: - There is no deeper analysis of how the Q-Former, memory bank, and MoE interact. Does the memory bank alleviate a specific bottleneck in the Q
The work is well motivated as existing LVLMs often struggle on temporally dependent video understanding tasks. The ability to improve temporal modeling through a unified training recipe would be desirable to the community The authors provide a comprehensive evaluation of the LVLM components explored in the work. They also explore components like memory banks, which are relatively under explored in LVLMs The proposed training recipe outperforms existing pre-trained LVLMs
It isn’t well explored why the proposed recipe improves temporal modeling capabilities of LVLMs. A deeper analysis outside of purely empirical results would strengthen the argument (e.g., attention visualization) * Specifically the Q-Former and MOE augmented Q-Former, it is unintuitive to the reviewer why these components in particular improve temporal understanding. It could be the case that these components simply enhance general video understanding rather than temporal understanding The nove
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Taxonomy
TopicsMultimodal Machine Learning Applications · Domain Adaptation and Few-Shot Learning · Generative Adversarial Networks and Image Synthesis