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Interpolating Video-LLMs: Toward Longer-sequence LMMs in a Training-free Manner

Yuzhang Shang, Bingxin Xu, Weitai Kang, Mu Cai, Yuheng Li, Zehao Wen, Zhen Dong, Kurt Keutzer, Yong Jae Lee, Yan Yan

TL;DR

Video-LLMs are limited by fixed encoders and short context windows, hindering long-video understanding and costly retraining. The paper introduces INTP-Video-LLMs, a training-free interpolation strategy that combines video token rearrangement to exploit fixed encoders with a RoPE-based context-extension (NTK-aware) to scale the LLM window, plus post-training KV-cache compression for memory efficiency. Empirically, INTP-Video-LLMs yield improvements on zero-shot VQA benchmarks and open-ended/multiple-choice tasks without additional training, and ablations show gains with more frames up to a plateau around 64 frames. This work offers a practical plug-in pathway to extend Video-LLMs to longer videos while keeping computational and data costs low.

Abstract

Advancements in Large Language Models (LLMs) inspire various strategies for integrating video modalities. A key approach is Video-LLMs, which incorporate an optimizable interface linking sophisticated video encoders to LLMs. However, due to computation and data limitations, these Video-LLMs are typically pre-trained to process only short videos, limiting their broader application for understanding longer video content. Additionally, fine-tuning Video-LLMs to handle longer videos is cost-prohibitive. Consequently, it becomes essential to explore the interpolation of Video-LLMs under a completely training-free setting. In this paper, we first identify the primary challenges in interpolating Video-LLMs: (1) the video encoder and modality alignment projector are fixed, preventing the integration of additional frames into Video-LLMs, and (2) the LLM backbone is limited in its content length capabilities, which complicates the processing of an increased number of video tokens. To address these challenges, we propose a specific INTerPolation method for Video-LLMs (INTP-Video-LLMs). We introduce an alternative video token rearrangement technique that circumvents limitations imposed by the fixed video encoder and alignment projector. Furthermore, we introduce a training-free LLM context window extension method to enable Video-LLMs to understand a correspondingly increased number of visual tokens.

Interpolating Video-LLMs: Toward Longer-sequence LMMs in a Training-free Manner

TL;DR

Video-LLMs are limited by fixed encoders and short context windows, hindering long-video understanding and costly retraining. The paper introduces INTP-Video-LLMs, a training-free interpolation strategy that combines video token rearrangement to exploit fixed encoders with a RoPE-based context-extension (NTK-aware) to scale the LLM window, plus post-training KV-cache compression for memory efficiency. Empirically, INTP-Video-LLMs yield improvements on zero-shot VQA benchmarks and open-ended/multiple-choice tasks without additional training, and ablations show gains with more frames up to a plateau around 64 frames. This work offers a practical plug-in pathway to extend Video-LLMs to longer videos while keeping computational and data costs low.

Abstract

Advancements in Large Language Models (LLMs) inspire various strategies for integrating video modalities. A key approach is Video-LLMs, which incorporate an optimizable interface linking sophisticated video encoders to LLMs. However, due to computation and data limitations, these Video-LLMs are typically pre-trained to process only short videos, limiting their broader application for understanding longer video content. Additionally, fine-tuning Video-LLMs to handle longer videos is cost-prohibitive. Consequently, it becomes essential to explore the interpolation of Video-LLMs under a completely training-free setting. In this paper, we first identify the primary challenges in interpolating Video-LLMs: (1) the video encoder and modality alignment projector are fixed, preventing the integration of additional frames into Video-LLMs, and (2) the LLM backbone is limited in its content length capabilities, which complicates the processing of an increased number of video tokens. To address these challenges, we propose a specific INTerPolation method for Video-LLMs (INTP-Video-LLMs). We introduce an alternative video token rearrangement technique that circumvents limitations imposed by the fixed video encoder and alignment projector. Furthermore, we introduce a training-free LLM context window extension method to enable Video-LLMs to understand a correspondingly increased number of visual tokens.
Paper Structure (23 sections, 7 equations, 3 figures, 4 tables)

This paper contains 23 sections, 7 equations, 3 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Video Language Models
  4. Long-context LLMs
  5. Post-training Compression for KV Cache
  6. Method: Interpolating Video-LLMs
  7. Preliminaries: Video Large Language Models (Video-LLMs)
  8. Alternative Video Token Rearrangement
  9. Interpolating Video-LLM backbone
  10. RoPE Fundamentals
  11. Context Window Scaling
  12. NTK-aware Interpolation
  13. Efficiency Bottleneck Analysis and a Post-training Solution
  14. Inference Cost Analysis
  15. Experiments
  16. ...and 8 more sections

Figures (3)

  • Figure 1: (Left) Video-LLMs consist of three main components: a video encoder, an alignment projector layer, and a fine-tuned LLM backbone. The process begins with the video encoder transforming video frames into a series of visual tokens. A projector then maps these tokens, aligning visual features with text features. The resulting aligned features, along with text prompts, are subsequently fed into the LLM backbone for visual understanding. (Right) I NTP, a training-free Video-LLM interpolation technique, addresses the existing constraints of Video-LLMs. We employ a video token rgb]0.8, 0.8, 1.0rearrangement that bypasses the limitations set by the fixed video encoder and alignment projector. Additionally, we implement a training-free LLM context window rgb]0.6667, 0.8863, 0.4510interpolation method to allow Video-LLMs to process an increased number of visual tokens effectively.
  • Figure 2: Alternative Video Token Rearrangement. (Left) A video input sampled with fewer frames, such as 2 frames $\mathbf{X}_v$, is processed through a video encoder and projector to produce visual tokens $\mathbf{Z}_v$ and transformed features $\mathbf{H}_v$. (Right) Increasing the number of sampled frames, for example to 4 (Frame #1 - #4), results in a richer video input $\mathbf{X}_v'$. By pairing Frames #1 with #3 and #2 with #4, we obtain two subsequences, $\mathbf{X}_{v,1}$ and $\mathbf{X}_{v,1}$, each processed by the same frozen encoder and projector. This results in new sets of tokens ($\mathbf{Z}_{v,1}$, $\mathbf{H}_{v,1}$; $\mathbf{Z}_{v,2}$, $\mathbf{H}_{v,2}$). These tokens are then correspondingly integrated into an extended sequence of features $\mathbf{H}_v'$.
  • Figure 3: Video content and questions from ActivityNet yu2019activitynet. The standard Video-LLaVA model exhibits limitations in accurately answering the questions, primarily due to its inability to process a sufficient number of frames (only the pink labeled frames are fed into Video-LLaVA). This constraint significantly hinders its effectiveness in managing complex video question-answering tasks. With our proposed I NTP, the enhanced Video-LLM can process extended sequences of video frames. This not only addresses the frame limitations but also substantially enhances the model's understanding in complex video question-answering scenarios.