Source-linked AI summary
Towards Unified Vision-Language Models with Incomplete Multi-Modal Inputs
Xiang Fang, Wanlong Fang, Changshuo Wang, Keke Tang, Daizong Liu, Siyi Wang, Wei Ji
TL;DR
VLMs typically assume complete video-text inputs, although real-world applications can produce incomplete modalities and expose limitations in existing alignment methods. The paper introduces a unified completeness network for incomplete video-language alignment, and experiments show it improves state-of-the-art methods across multiple multimodal tasks as a plug-and-play module.
Problem
Existing VLM-based methods rely on complete video-text pairs, while real-world missing frames and words create incomplete inputs that these methods cannot effectively align.
Source: Introduction · pp. 1–2Method
The paper proposes a unified completeness network combining multimodal feature approximation, multimodal knowledge distillation, and multi-granularity multimodal integration for incomplete video-language inputs.
Source: Introduction · p. 2, Multi-modal Knowledge Distillation · p. 5, Multi-granularity Multi-modal… · p. 5Results
The framework improves state-of-the-art VLM-based methods across video-text retrieval, video question answering, and video sentence grounding as a plug-and-play module.
Source: Introduction · p. 2, Performance Comparison · pp. 7–8, Conclusion · p. 8Takeaways & Limitations
A single completeness framework can be applied across multiple incomplete video-language tasks and existing task-specific models.
Source: Conclusion · p. 8
Abstract
from arXiv · showhide
Video-Language Models (VLMs) have demonstrated impressive multi-modal reasoning capabilities across diverse computer vision applications. However, these VLMs are task-specific and assume that both video and language inputs are complete. However, real-world VLM applications might face challenges due to deactivated sensors (e.g., cameras are unavailable due to data privacy), yielding modality-incomplete data and leading to inconsistency between training and testing data. While straightforward incomplete input can boast training generalization-ability and lead to training failure, its potential risks to VLMs regarding safety and trustworthiness have been largely neglected. To this end, we make the first attempt to propose a unified incomplete video-language model to process the incomplete multi-modal inputs. Extensive experimental results show that our method can serve as a plug-and-play module for previous works to improve their performance in various multi-modal tasks.
Introduction
VLMs perform well on complete video-language tasks but depend heavily on complete paired inputs, limiting their reliability when real-world data lose frames or words. The paper introduces an incomplete video-language setting and a unified completeness network with feature approximation, knowledge distillation, and multi-granularity integration.
- VLMs support downstream applications including text-to-video retrieval and video question answering.Source: Introduction · p. 1
- Network instability and data loss can produce incomplete multimodal inputs, including videos with missing frames and texts with missing words.Source: Introduction · p. 1, Figure 1 · p. 2
- Existing alignment methods rely on complete video-text pairs during training and inference, while missing frames prevent correct motion reconstruction and can degrade performance or cause model failure.Source: Introduction · pp. 1–2
- The paper defines incomplete video-text alignment for unbalanced incomplete inputs, where video and text have different incompleteness rates.Source: Introduction · pp. 1–2
- The unified completeness network combines multimodal feature approximation, multimodal knowledge distillation, and multi-granularity multimodal integration.Source: Introduction · p. 2
- The method is presented as a plug-and-play module that improves task-specific methods across video-text retrieval, video question answering, and video sentence grounding.Source: Introduction · p. 2
Related Works
Research on incomplete multimodal inputs addresses modality incompleteness across multiple tasks using strategies such as improved fusion and data augmentation.
- Prior work improves robustness to modality-incomplete data across various multimodal tasks.Source: Related Works · p. 2
- Existing approaches include optimizing multimodal fusion strategies and conducting data augmentation.Source: Related Works · p. 2
Our Proposed Method
The method is a unified pipeline for incomplete video-text pairs that approximates missing features, distills knowledge from complete inputs, and integrates modalities at multiple granularities. It is designed as a plug-and-play approach for downstream multi-modal tasks.
Pipeline
- The pipeline encodes incomplete videos frame-by-frame and texts word-by-word before completing missing modality features.Missing frames or words are represented as null entries, and feature encoders extract the available visual and textual representations.Source: Our Proposed Method · p. 2, Multi-modal Feature Approximation · p. 4
Multi-modal Feature Approximation
- The Multi-modal Feature Approximation module uses cross-modal and intra-modal nearest neighbors to estimate missing frame and word features.K-reciprocal neighbors and Jaccard distances identify semantically relevant neighbors, whose features are combined through affinity matrices.Source: Multi-modal Feature Approximation · p. 4
- A pre-trained large multi-modal model refines the approximated video and text memories after graph-based feature completion.The approximation constructs graph relationships that transmit information among samples, then applies the large multi-modal model to refine the resulting memories.Source: Multi-modal Feature Approximation · p. 4
Multi-modal Knowledge Distillation
- The Multi-modal Knowledge Distillation module trains a student on incomplete pairs against a teacher trained on complete video-text pairs.This addresses the computational cost of completing every missing frame and word while reducing reliance on the complete modality.Source: Multi-modal Knowledge Distillation · pp. 4–5
Multi-granularity Multi-modal Integration
- The Multi-granularity Multi-modal Integration module uses shared prototypes and matching-probability weights to align video-text pairs at task-appropriate granularities.Prototype cross-attention reconstructs modality features, while noun-phrase overlap weights support fine-grained alignment for tasks requiring instance-level correspondence.Source: Multi-modal Feature Approximation · p. 4, Multi-granularity Multi-modal… · p. 5
Experiment
Experiments evaluate the framework across retrieval, question answering, and grounding tasks under incomplete video-text inputs. The framework improves prior methods as a plug-and-play module, while ablations and efficiency comparisons examine its components and practical performance.
Experiment
- The evaluation covers video-text retrieval, video sentence grounding, and VideoQA using open-source datasets, with incomplete pairs generally defined by a 30% missing rate.Metrics include retrieval recall, grounding at specified IoU thresholds, and VideoQA categories.Source: Experiment · p. 6
Performance Comparison
- Incomplete video-text inputs cause substantial degradation for prior retrieval and VideoQA methods because missing frames and words are directly concatenated or insufficiently understood.The reported comparisons include text-to-video and video-to-text retrieval, as well as VideoQA on incomplete pairs.Source: Performance Comparison · p. 7
- The framework improves state-of-the-art VSG methods across all reported metrics when used as a plug-and-play module.The comparison includes VSG results summarized in Table 4, Table 5, and Figure 4.Source: Performance Comparison · pp. 7–8
- Visualization results show poor performance from state-of-the-art methods on incomplete multi-modal datasets across three downstream tasks.Figure 5 presents visualizations for different downstream tasks and incomplete datasets.Source: Figure 5 · p. 7, Performance Comparison · p. 8
Efficiency Comparison
- The framework achieves faster processing speeds with relatively fewer learnable parameters than most compared methods in the VSG efficiency comparison.The efficiency comparison uses 2D-TAN as the base model and is reported for VSG on TACoS.Source: Table 5 · p. 7, Performance Comparison · p. 8
Ablation Study
- Ablation studies remove individual components, including multi-modal feature approximation, to evaluate their contributions to the VSG framework.The ablation study uses G2L as the base model and investigates key modules individually.Source: Table 6 · p. 7, Ablation Study and Analysis · p. 8
Conclusion
The paper targets incomplete video-text alignment and proposes a unified completeness network for incomplete multi-modal inputs. Experiments across retrieval, VideoQA, and grounding show that the framework can improve state-of-the-art VLM-based methods as a plug-and-play module.
Conclusion
- The paper proposes incomplete video-text alignment and a unified completeness network for modality-incomplete challenges across downstream multi-modal tasks.The targeted tasks include video-text retrieval, video question answering, and video sentence grounding.Source: Conclusion · p. 8
- Extensive experiments on incomplete multi-modal datasets show that the framework improves state-of-the-art VLM-based works as a plug-and-play module.The reported scope spans multiple downstream multi-modal tasks.Source: Conclusion · p. 8