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VideoLLM: Modeling Video Sequence with Large Language Models
Guo Chen, Yin-Dong Zheng, Jiahao Wang, Jilan Xu, Yifei Huang, Junting Pan, Yi Wang, Yali Wang, Yu Qiao, Tong Lu, Limin Wang
TL;DR
Video understanding models are often task-specific and lack comprehensive capability across diverse tasks, while LLMs have demonstrated sequence reasoning abilities. VideoLLM aligns video and language inputs into a unified token sequence for a decoder-only LLM and task heads. Experiments across eight tasks and four datasets support transferring LLM understanding and reasoning to video understanding.
Problem
Video understanding models are often task-specific and lack comprehensive capability across diverse tasks, creating demand for a method with fundamental video-sequence modeling capabilities.
Source: §1 · pp. 1–2Method
VideoLLM translates inputs from different modalities into a unified token sequence for a parameter-frozen, decoder-only LLM, with simple task heads producing task-specific outputs.
Source: §6 · p. 10Results
Experiments on eight tasks from four datasets demonstrate that LLM understanding and reasoning capabilities can be transferred to video understanding.
Source: §1 · p. 3, §6 · p. 10Takeaways & Limitations
The results establish LLMs as effective video reasoners and validate VideoLLM across multiple video sequence understanding tasks.
Source: §1 · p. 3The decoder-only reasoner reflects the design assumption that real-world video processors should use unidirectional visual data flow.
Source: §4.3 · p. 6
Abstract
from arXiv · showhide
With the exponential growth of video data, there is an urgent need for automated technology to analyze and comprehend video content. However, existing video understanding models are often task-specific and lack a comprehensive capability of handling diverse tasks. The success of large language models (LLMs) like GPT has demonstrated their impressive abilities in sequence causal reasoning. Building upon this insight, we propose a novel framework called VideoLLM that leverages the sequence reasoning capabilities of pre-trained LLMs from natural language processing (NLP) for video sequence understanding. VideoLLM incorporates a carefully designed Modality Encoder and Semantic Translator, which convert inputs from various modalities into a unified token sequence. This token sequence is then fed into a decoder-only LLM. Subsequently, with the aid of a simple task head, our VideoLLM yields an effective unified framework for different kinds of video understanding tasks. To evaluate the efficacy of VideoLLM, we conduct extensive experiments using multiple LLMs and fine-tuning methods. We evaluate our VideoLLM on eight tasks sourced from four different datasets. The experimental results demonstrate that the understanding and reasoning capabilities of LLMs can be effectively transferred to video understanding tasks. We release the code at https://github.com/cg1177/VideoLLM.
1 Introduction
VideoLLM addresses the gap between powerful LLM sequence reasoning and video understanding, where task-specific models incur tailored costs and do not scale seamlessly across tasks. It aligns video and language sequences for LLM-based reasoning, and reports effective video reasoning across multiple tasks.
1 Introduction
- Video sequence understanding lacks a scalable model that adapts seamlessly across tasks, while task-specific designs bring additional tailoring costs.Large-scale video self-supervision is challenging because temporal visual annotation and video data processing are expensive and time-consuming.Source: §1 · pp. 1–2
- VideoLLM aligns video and language sequences, using temporal-wise unitization, a semantic translator, and a decoder-only LLM for diverse video tasks.The framework integrates visual-only and visual-language tasks, including temporal action detection, action anticipation, temporal grounding, and highlight detection.Source: §1 · p. 2, Figure 1 · p. 2
- VideoLLM learns short-term visual token representations to integrate frozen LLMs, contrasting with the long-term temporal post-fusion approach in [3].The paper describes an end-to-end system-level alternative to API- or ensemble-based visual understanding applications [12] [97] [68] [54] [45].Source: §1 · p. 2
- The proposed plug-and-play adaptation scheme combines off-the-shelf visual encoders and advanced LLMs without task-specific customization.The framework’s unified modeling aligns video with language to reason about language logic and evolving real-world states.Source: §1 · p. 2
- Experiments validate VideoLLM as an effective video reasoner across multiple video sequence understanding tasks.The introduction presents these findings as evidence for the framework’s efficacy.Source: §1 · p. 3
2 Related Work
Related work covers video sequence tasks, visual representation models, language models, and multimodal alignment approaches. The paper positions its study at the intersection of video understanding and LLM sequence reasoning.
2.1 Video Sequence Understanding
- Video sequence tasks span timestamp-level prediction and segment-level boundary prediction, with examples including action detection, anticipation, highlight detection, and language-based localization.The paper tests multiple tasks to assess LLM video reasoning from different perspectives.Source: §2.1 · p. 3
2.2 Vision Models
- Vision models represent spatial or space-time information using convolutional, transformer, or hybrid architectures.The passage describes convolutional and transformer approaches, as well as hybrids combining attention and convolution.Source: §2.2 · p. 3
2.3 Large Language Models
- LLMs use large text corpora and varied objectives; generative models [7] show broad language benchmark results, while multimodal capabilities remain less explored.The paper examines whether LLMs’ long-range modeling capacity can improve video understanding.Source: §2.3 · p. 3
2.4 Multimodal Models
- Multimodal models align vision and language, using end-to-end image/video-text training or frozen encoders with learnable modules.BLIP-2 [42] compresses visual tokens with a lightweight Transformer, while Flamingo [1] adds intermediate cross-attention layers to LLMs.Source: §2.4 · p. 3
3 Preliminary
The preliminary section distinguishes language-model architectures and summarizes the scope of video tasks and datasets used for evaluation. It frames the study around varied LLM reasoners and task settings.
3.1 Large Language Model
- Language models include encoder-decoder architectures such as BERT [17] and T5 [64], and decoder-only models such as GPT and OPT [101].The former use bidirectional masked language modeling; the latter use forward autoregressive factorization, and Table 1 lists model parameter and pre-training token scales.Source: §3.1 · p. 4, Table 1 · p. 4
3.2 Tasks
- VideoLLM is evaluated on eight tasks across four datasets, covering online reasoning, future prediction, memory retrieval, and dense prediction.The tasks include action detection and segmentation, captioning, highlight detection, anticipation, and moment or natural-language queries; Table 2 presents dataset statistics.Source: §3.2 · p. 4, Table 2 · p. 4
4 VideoLLM
VideoLLM adapts pre-trained LLMs for video sequence understanding by aligning visual and language sequences for unified reasoning. Its architecture combines modality encoders, a semantic translator, a decoder-only reasoner, and simple task heads.
4 VideoLLM
- VideoLLM applies pre-trained LLM sequence modeling to video reasoning through parameter-efficient transfer learning and a unified video-language framework.The framework is designed for video sequence understanding tasks and integrates textual conditions.Source: §4 · p. 4
4.1 Modality Encoder
- Short-term encoders divide video into space-time units, while text is tokenized or encoded into features for text-conditioned reasoning.Each visual unit is encoded independently; text tokens or encoded text features can condition the reasoner.Source: §4.1 · p. 5, Figure 2 · p. 5
4.2 Semantic Translator
- The semantic translator pools each visual unit into a temporal token and projects it into the LLM’s hidden dimension.The visual encoder is frozen, and a linear projector translates visual features into language semantics.Source: §4.2 · p. 5
4.3 Decoder-only Reasoner
- A decoder-only LLM serves as the reasoner, matching unidirectional video flow and supporting causal sequence processing.For online reasoning, it processes the latest translated token against historical context without recalculating the entire sequence.Source: §4.3 · p. 6
- The reasoner adapts to future prediction, memory retrieval, and dense prediction as well as online reasoning.It predicts future tokens or events, retrieves segments from seen-token memory, and predicts token attributes or highlights text-matched tokens.Source: §4.3 · pp. 6–7, Figure 3 · p. 6
- Task outputs use final reasoner states and linear layers, without multi-stage supervision, prior operators, or intricate task-specific heads.The stated principles focus supervision on the reasoner’s final output and use linear layers to produce task results.Source: §4.3 · p. 7
4.4 Model Training
- Training offers basic tuning, partial tuning of selected LLM parameters, and PEFT methods including LoRA, Prompt Tuning, and Prefix Tuning.Basic tuning focuses on semantic translation and output layers when the language model is frozen.Source: §4.4 · p. 7
5 Experiments
Experiments evaluate VideoLLM across four datasets and eight diverse tasks, using multiple language models and tuning methods. Results show task-dependent model strengths and generally competitive performance against task-specific methods, with additional comparisons of tuning, scale, and reasoner direction.
5.1 Experimental Setup
- Experiments span four datasets and eight tasks, covering online reasoning, future prediction, memory retrieval, and dense prediction across varied access, prediction, and modality settings.Evaluation uses task-specific metrics and pretrained visual encoders selected to align with community settings.Source: §5.1 · p. 7
5.2 Main Results and Analysis
- GPT-2, OPT, and T5 show task-dependent strengths: GPT-2 and OPT lead future prediction, OPT leads online action detection, and T5 excels in dense prediction.GPT-2 leads online captioning and moment retrieval, while GPT-2 and T5 are roughly tied on average and OPT is slightly worse.Source: §5.2 · p. 8, Figure 4 · p. 8
- On causal online action detection, prefix tuning at r=4 reaches Action Top-5 Recall of 21.4, 1.3 points above basic tuning.LoRA lowers performance, while first- or last-block fine-tuning uses substantially more trainable parameters than the other tuning methods.Source: §5.2 · p. 8, Table 3 · p. 8
- Across seven evaluated tasks, VideoLLM matches or exceeds tailored methods, including stronger unseen-action recall, state-of-the-art action segmentation, and superiority on visual-language tasks.Online captioning is excluded because comparable sequence-level methods are unavailable.Source: §5.2 · p. 9, Table 4 · p. 9
- Below 2B parameters, larger language models generally improve video reasoning; OPT-1.3B reaches 23.4 Action Top-5 Recall, but performance declines for larger models.The authors suggest projector overfitting as one possible explanation; T5 and OPT at larger scales continue the declining trend, while LLaMA-7B approximates OPT.Source: §5.2 · pp. 9–10, Figure 5 · p. 9, Table 5 · p. 10
- The bidirectional T5 encoder outperforms the unidirectional T5 decoder in most tested cases, especially action segmentation, while visual-language task differences are smaller.For NLQ Rank1@0.3, OPT and the T5 encoder are comparable at 7.3 and 7.4, respectively.Source: §5.2 · p. 10
6 Conclusion and Future Work
VideoLLM transfers pretrained LLM sequence reasoning to video through modality encoding, semantic translation, and task-specific heads. Experiments support applying LLM comprehension and reasoning to video understanding; future work will serialize frame-appearance information for broader spatiotemporal understanding.
6 Conclusion and Future Work
- VideoLLM converts multimodal inputs into a unified token sequence for a frozen decoder-only LLM, with simple task heads mapping its output to video tasks.The framework combines a Modality Encoder and Semantic Translator.Source: §6 · p. 10
- Experiments across eight tasks and four datasets indicate that LLM comprehension and reasoning abilities can be applied to video understanding.Source: §6 · p. 10
- Future work will serialize frame-appearance information to pursue more comprehensive understanding across the spatiotemporal dimension.Source: §6 · p. 10