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QTG-VQA: Question-Type-Guided Architectural for VideoQA Systems

Zhixian He, Pengcheng Zhao, Fuwei Zhang, Shujin Lin

TL;DR

This paper proposes QTG- VQA, a novel architecture that integrates question-type-guided attention mechanisms with an adaptive learning mechanism to address issues such as insufficient learning and model degradation caused by the uneven distribution of question types.

Abstract

In the domain of video question answering (VideoQA), the impact of question types on VQA systems, despite its critical importance, has been relatively under-explored to date. However, the richness of question types directly determines the range of concepts a model needs to learn, thereby affecting the upper limit of its learning capability. This paper focuses on exploring the significance of different question types for VQA systems and their impact on performance, revealing a series of issues such as insufficient learning and model degradation due to uneven distribution of question types. Particularly, considering the significant variation in dependency on temporal information across different question types, and given that the representation of such information coincidentally represents a principal challenge and difficulty for VideoQA as opposed to ImageQA. To address these challenges, we propose QTG-VQA, a novel architecture that incorporates question-type-guided attention and adaptive learning mechanism. Specifically, as to temporal-type questions, we design Masking Frame Modeling technique to enhance temporal modeling, aimed at encouraging the model to grasp richer visual-language relationships and manage more intricate temporal dependencies. Furthermore, a novel evaluation metric tailored to question types is introduced. Experimental results confirm the effectiveness of our approach.

QTG-VQA: Question-Type-Guided Architectural for VideoQA Systems

TL;DR

This paper proposes QTG- VQA, a novel architecture that integrates question-type-guided attention mechanisms with an adaptive learning mechanism to address issues such as insufficient learning and model degradation caused by the uneven distribution of question types.

Abstract

In the domain of video question answering (VideoQA), the impact of question types on VQA systems, despite its critical importance, has been relatively under-explored to date. However, the richness of question types directly determines the range of concepts a model needs to learn, thereby affecting the upper limit of its learning capability. This paper focuses on exploring the significance of different question types for VQA systems and their impact on performance, revealing a series of issues such as insufficient learning and model degradation due to uneven distribution of question types. Particularly, considering the significant variation in dependency on temporal information across different question types, and given that the representation of such information coincidentally represents a principal challenge and difficulty for VideoQA as opposed to ImageQA. To address these challenges, we propose QTG-VQA, a novel architecture that incorporates question-type-guided attention and adaptive learning mechanism. Specifically, as to temporal-type questions, we design Masking Frame Modeling technique to enhance temporal modeling, aimed at encouraging the model to grasp richer visual-language relationships and manage more intricate temporal dependencies. Furthermore, a novel evaluation metric tailored to question types is introduced. Experimental results confirm the effectiveness of our approach.
Paper Structure (21 sections, 14 equations, 6 figures, 8 tables)

This paper contains 21 sections, 14 equations, 6 figures, 8 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. VideoQA Datasets
  4. Learning Bias
  5. Method
  6. Overview
  7. Question Type Guided Model Architecture
  8. Loss Function Design
  9. Type-Specific Cost Evaluation
  10. Experiment
  11. Experimental Setup
  12. Main Experiments Results
  13. Question-Type Guided Attention Ablation
  14. Temporal Autoregression Ablation
  15. Generalization Across Question Types
  16. ...and 6 more sections

Figures (6)

  • Figure 1: Illustration of different problem types in video question answering. "Basic understanding" and "Event Forecasting" type problems have differing requirements for input sequence dependency and model capabilities.
  • Figure 2: The overall framework of QTG-VQA. The architecture is primarily composed of four core components: visual-text feature extractor, question type embedding module, weighted adaptive module, and temporal autoregression module
  • Figure 3: Sample counts across different question types in SUTD-TrafficQA dataset for training and validation sets. The x-axis shows letter abbreviations for each question type.
  • Figure 4: Comparative visualization of training loss across different question types without (up) and with (down) the implementation of question-type guided attention.
  • Figure 5: Generalization Results Heatmap.
  • ...and 1 more figures