QTG-VQA: Question-Type-Guided Architectural for VideoQA Systems
- URL: http://arxiv.org/abs/2409.09348v1
- Date: Sat, 14 Sep 2024 07:42:41 GMT
- Title: QTG-VQA: Question-Type-Guided Architectural for VideoQA Systems
- Authors: Zhixian He, Pengcheng Zhao, Fuwei Zhang, Shujin Lin,
- Abstract summary: This paper explores the significance of different question types for VQA systems and their impact on performance.
We propose QTG-VQA, a novel architecture that incorporates question-type-guided attention and adaptive learning mechanism.
- Score: 3.486120902611884
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
Related papers
- UNK-VQA: A Dataset and a Probe into the Abstention Ability of Multi-modal Large Models [55.22048505787125]
This paper contributes a comprehensive dataset, called UNK-VQA.
We first augment the existing data via deliberate perturbations on either the image or question.
We then extensively evaluate the zero- and few-shot performance of several emerging multi-modal large models.
arXiv Detail & Related papers (2023-10-17T02:38:09Z) - Improving Visual Question Answering Models through Robustness Analysis
and In-Context Learning with a Chain of Basic Questions [70.70725223310401]
This work proposes a new method that utilizes semantically related questions, referred to as basic questions, acting as noise to evaluate the robustness of VQA models.
The experimental results demonstrate that the proposed evaluation method effectively analyzes the robustness of VQA models.
arXiv Detail & Related papers (2023-04-06T15:32:35Z) - Knowledge-Based Counterfactual Queries for Visual Question Answering [0.0]
We propose a systematic method for explaining the behavior and investigating the robustness of VQA models through counterfactual perturbations.
For this reason, we exploit structured knowledge bases to perform deterministic, optimal and controllable word-level replacements targeting the linguistic modality.
We then evaluate the model's response against such counterfactual inputs.
arXiv Detail & Related papers (2023-03-05T08:00:30Z) - On the Significance of Question Encoder Sequence Model in the
Out-of-Distribution Performance in Visual Question Answering [15.787663289343948]
Generalizing beyond the experiences has a significant role in developing practical AI systems.
Current Visual Question Answering (VQA) models are over-dependent on the language-priors.
This paper shows that the sequence model architecture used in the question-encoder has a significant role in the generalizability of VQA models.
arXiv Detail & Related papers (2021-08-28T05:51:27Z) - Latent Variable Models for Visual Question Answering [34.9601948665926]
We propose latent variable models for Visual Question Answering.
Extra information (e.g. captions and answer categories) are incorporated as latent variables to improve inference.
Experiments on the VQA v2.0 benchmarking dataset demonstrate the effectiveness of our proposed models.
arXiv Detail & Related papers (2021-01-16T08:21:43Z) - Learning from Lexical Perturbations for Consistent Visual Question
Answering [78.21912474223926]
Existing Visual Question Answering (VQA) models are often fragile and sensitive to input variations.
We propose a novel approach to address this issue based on modular networks, which creates two questions related by linguistic perturbations.
We also present VQA Perturbed Pairings (VQA P2), a new, low-cost benchmark and augmentation pipeline to create controllable linguistic variations.
arXiv Detail & Related papers (2020-11-26T17:38:03Z) - Loss re-scaling VQA: Revisiting the LanguagePrior Problem from a
Class-imbalance View [129.392671317356]
We propose to interpret the language prior problem in VQA from a class-imbalance view.
It explicitly reveals why the VQA model tends to produce a frequent yet obviously wrong answer.
We also justify the validity of the class imbalance interpretation scheme on other computer vision tasks, such as face recognition and image classification.
arXiv Detail & Related papers (2020-10-30T00:57:17Z) - C3VQG: Category Consistent Cyclic Visual Question Generation [51.339348810676896]
Visual Question Generation (VQG) is the task of generating natural questions based on an image.
In this paper, we try to exploit the different visual cues and concepts in an image to generate questions using a variational autoencoder (VAE) without ground-truth answers.
Our approach solves two major shortcomings of existing VQG systems: (i) minimize the level of supervision and (ii) replace generic questions with category relevant generations.
arXiv Detail & Related papers (2020-05-15T20:25:03Z) - Understanding Knowledge Gaps in Visual Question Answering: Implications
for Gap Identification and Testing [20.117014315684287]
We use a taxonomy of Knowledge Gaps (KGs) to tag questions with one or more types of KGs.
We then examine the skew in the distribution of questions for each KG.
These new questions can be added to existing VQA datasets to increase the diversity of questions and reduce the skew.
arXiv Detail & Related papers (2020-04-08T00:27:43Z) - SQuINTing at VQA Models: Introspecting VQA Models with Sub-Questions [66.86887670416193]
We show that state-of-the-art VQA models have comparable performance in answering perception and reasoning questions, but suffer from consistency problems.
To address this shortcoming, we propose an approach called Sub-Question-aware Network Tuning (SQuINT)
We show that SQuINT improves model consistency by 5%, also marginally improving performance on the Reasoning questions in VQA, while also displaying better attention maps.
arXiv Detail & Related papers (2020-01-20T01:02:36Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.