Source-linked AI summary
Rethinking Video-Language Model from the Language Input Perspective
Xiang Fang, Wanlong Fang, Changshuo Wang, Xiaoye Qu, Daizong Liu
TL;DR
VLMs commonly depend on predefined language templates, despite semantically similar user inputs producing different results. The paper introduces a plug-and-play framework that generates varied texts, reasons over their attributes, and uses video-guided self-weighting; experiments report improved performance across video-language tasks.
Problem
Existing VLMs rely on predefined or correctly aligned text inputs, limiting robustness to semantically similar texts expressed with different templates.
Method
The framework generates positive and negative text variants, selects fine-grained attributes, and applies video-guided self-weighted cross-modal bridging.
Results
The framework improves state-of-the-art VLM performance across various video-language tasks as a plug-and-play module.
Takeaways & Limitations
Template-free language inputs can be handled through a plug-and-play module designed to improve VLM text understanding across video-language tasks.
Abstract
from arXiv · showhide
Driven by the wave of large language models, Video-Language Models (VLMs) have become a significant yet challenging technology to bridge the gap between videos and texts. Although previous VLM works have made significant progress, almost all of them implicitly assume that all the texts are predefined by the specific template. In real-world applications, such a strict assumption is impossible to satisfy since 1) predefining all the texts is extremely time-consuming and labor-intensive. 2) these predefined text inputs are too restrictive and user-unfriendly, limiting their applications. It is observed that given a video input, texts with similar semantics but different templates lead to various performances. To this end, in this paper, we propose a novel plug-and-play framework for various VLM-based methods to fully bridge videos and texts. Specifically, we first generate positive and negative texts from the original ones to target specific text components. Then, we propose an attribute-based text reasoning strategy to mine fine-grained textual semantics of generated texts. Finally, we utilize videos as guidance to conduct cross-modal bridging by designing a self-weighted loss. Extensive experiments show that the proposed method can serve as the plug-and-play module to effectively improve the performance of state-of-the-art VLMs.
Introduction
VLMs rely heavily on language inputs, but predefined templates can be restrictive and produce inconsistent results for semantically similar texts. The proposed plug-and-play framework augments texts, reasons over their attributes, and uses video-guided bridging to improve VLM robustness across tasks.
- Motivation: VLMs combine visual and language understanding across VideoQA, VSG, and VTR, with sentence text serving as a key human-friendly input.VideoQA requires matching visual cues with a natural-language question to generate or select an answer.
- Scope and Contributions: The method targets template-free, user-friendly text inputs and functions as a plug-and-play module for VSG, VideoQA, and VTR.Experiments are conducted across three representative downstream tasks and multiple datasets.
- Motivation: Semantically equivalent texts with different structures can produce dissimilar grounding results because existing methods rely on predefined texts and weak text encoders.The paper contrasts “Person pours water into a glass” with “Water is poured into a glass by person.”
- Proposed Framework: The framework generates diverse positive and negative text variants, including hard negatives created by modifying specific sentence parts while preserving the remainder.Generated variants diversify video-text datasets and target different text components.
- Proposed Framework: Attribute-based text reasoning mines fine-grained semantics, while video-guided self-weighted cross-modal bridging adaptively integrates sentence components.The framework selects and weights textual information to connect generated texts with their corresponding videos.
Related Works
Existing VLM methods depend on correctly aligned multimodal datasets and have not adequately addressed robustness to semantically similar texts expressed with different templates. This work frames that gap as a text-understanding problem and proposes eliminating the negative impact of template variation.
- Research Gap: Prior VLM approaches rely heavily on correctly aligned multimodal datasets.This dependence motivates robustness to textual variation.
- Research Gap: Robustness to different texts with similar semantics had not been studied in the cited VLM literature.The paper identifies this as a text-understanding problem in VLM tasks.
- Research Direction: The proposed direction aims to eliminate the negative impact of different text templates on VLM performance.The paper presents this as its first attempt to address the identified gap.
Methodology
The method augments text inputs at multiple levels, reasons over fine-grained attributes, and uses video-guided weighting to improve cross-modal text understanding. It is designed as a plug-and-play framework for downstream video-language models.
- Multi-level Text Augmentation: LLMs generate positive and negative text variants by changing words and sentence structure while preserving or altering semantics.The framework uses human-based, chatbot-based, and open-source LLM-based augmentation to create variation-origin pairs.
- Multi-level Text Augmentation: Word-level and structure-level rewrites provide coarse-grained language alignment across diverse text inputs.The rewriting process changes specific sentence parts while keeping the remaining content unchanged.
- Attribute-based Text Reasoning: Attribute reasoning extracts and selects representative, non-redundant attributes that are semantically relevant to video features.Attributes are clustered by feature similarity, ranked by visual similarity, and filtered to remove non-visual or incorrect visual attributes.
- Attribute-based Text Reasoning: NLI-, format-, and related selection criteria filter attribute candidates using semantic entailment and text dissimilarity constraints.The updated attribute pool retains candidates satisfying the defined critics jointly.
- Video-guided Self-weighted Cross-modal Bridging: The cross-modal bridging loss decomposes supervision across sentence components and uses the maximum loss to identify the most clearly distinguished component.Generated positive and negative samples supervise the model to distinguish differences among words in each sentence part.
- Video-guided Self-weighted Cross-modal Bridging: The final self-weighted loss incorporates component significance scores while remaining compatible with existing cross-modal fusion modules.The framework is plug-and-play and borrows the fusion module from an open-source method.
Experiments
Experiments evaluate the framework across VTR, VideoQA, and VSG, including comparisons, ablations, and augmentation analyses. Results indicate improved performance across tasks and show contributions from the framework’s components.
- Evaluation Setup: The evaluation covers VTR, VideoQA, and VSG using multiple datasets and task-specific metrics.VTR uses retrieval metrics, VSG uses IoU-based grounding metrics, and VideoQA uses seven question-type metrics.
- Performance Comparison: The framework improves previous VLM methods on VTR, VideoQA, and VSG, with VSG gains reported across three datasets and all metrics.On VTR, augmented text alone degrades performance, whereas the plug-and-play framework produces significant improvement.
- Ablation Study: All three ablated components contribute to final VSG performance, and the full model outperforms ablations during training while converging faster.The compared components are multi-level text augmentation, attribute-based text reasoning, and video-guided self-weighted cross-modal bridging.
- Augmentation Analysis: The proposed augmentation method outperforms back-translation, paraphrasing, and LLaMA-7B augmentation in the reported comparison.The passage attributes weaknesses to hallucinated information from LLaMA-7B and equal treatment of word importance by back-translation and paraphrasing.
- Loss Analysis: The self-weighted cross-modal bridging loss improves performance over variants without S1 or S2, supporting the use of text-input semantic signals.The comparison states that the full model better understands text inputs based on S1 and S2.
Conclusion
The paper reframes VLMs around user-friendly language inputs because semantically similar texts can produce different results. Its plug-and-play framework improves state-of-the-art VLM performance across video-language tasks, with future extensions proposed for image-language and video-audio models.
- The paper examines VLMs from a user-friendly language-input perspective.
- The framework addresses differing outputs from semantically similar texts by improving generation across varied text templates.
- Experiments show that the framework improves state-of-the-art VLM performance across various video-language tasks.
- Future work proposes extending the model to image-language and video-audio settings.