Visual Question Answering Research Articles

Advanced Visual and Textual Co-context Aware Attention Network with Dependent Multimodal Fusion Block for Visual Question Answering

Advanced Visual and Textual Co-context Aware Attention Network with Dependent Multimodal Fusion Block for Visual Question Answering

Meta-Interpretive LEarning with Reuse

Inductive Logic Programming (ILP) is a research field at the intersection between machine learning and logic programming, focusing on developing a formal framework for inductively learning relational descriptions in the form of logic programs from examples and background knowledge. As an emerging method of ILP, Meta-Interpretive Learning (MIL) leverages the specialization of a set of higher-order metarules to learn logic programs. In MIL, the input includes a set of examples, background knowledge, and a set of metarules, while the output is a logic program. MIL executes a depth-first traversal search, where its program search space expands polynomially with the number of predicates in the provided background knowledge and exponentially with the number of clauses in the program, sometimes even leading to search collapse. To address this challenge, this study introduces a strategy that employs the concept of reuse, specifically through the integration of auxiliary predicates, to reduce the number of clauses in programs and improve the learning efficiency. This approach focuses on the proactive identification and reuse of common program patterns. To operationalize this strategy, we introduce MILER, a novel method integrating a predicate generator, program learner, and program evaluator. MILER leverages frequent subgraph mining techniques to detect common patterns from a limited dataset of training samples, subsequently embedding these patterns as auxiliary predicates into the background knowledge. In our experiments involving two Visual Question Answering (VQA) tasks and one program synthesis task, we assessed MILER’s approach to utilizing reusable program patterns as auxiliary predicates. The results indicate that, by incorporating these patterns, MILER identifies reusable program patterns, reduces program clauses, and directly decreases the likelihood of timeouts compared to traditional MIL. This leads to improved learning success rates by optimizing computational efforts.

INTEGRATING IMAGE FEATURES WITH CONVOLUTIONAL SEQUENCE-TO-SEQUENCE NETWORK FOR MULTILINGUAL VISUAL QUESTION ANSWERING

Visual question answering is a task that requires computers to give correct answers for the input questions based on the images. This task can be solved by humans with ease, but it is a challenge for computers. The VLSP2022-EVJVQA shared task carries the Visual question answering task in the multilingual domain on a newly released dataset UIT-EVJVQA, in which the questions and answers are written in three different languages: English, Vietnamese, and Japanese. We approached the challenge as a sequence-to-sequence learning task, in which we integrated hints from pre-trained state-of-the-art VQA models and image features with a convolutional sequence-to-sequence network to generate the desired answers. Our results obtained up to 0.3442 by F1 score on the public test set and 0.4210 on the private test set.

Cascade transformers with dynamic attention for video question answering

Visual question answering (VQA) has become a hot study topic with challenging motivation of correctly answering the videos or images questions in recent years. However, the existing VQA model mostly aimed at answering questions about images and performed poorly in the video question answering (VideoQA) domain. VideoQA needs to simultaneously consider the correlations between video frames and the dynamic information of multiple objects in video. Therefore, we propose a novel Cascade Transformers with Dynamic Attention for Video Question Answering (CTDA-QA), which aims to simultaneously solve the above considerations. Specifically, the proposed CTDA-QA model utilizes multiple transformers structure to encode videos for reasoning complex spatial and temporal information, which is different from the previous recurrent neural network methods. Besides, in order to effectively capture the dynamic information from various scenarios in videos, a flexible attention module has been proposed to explore the essential relations between objects in a dynamic timeline. Finally, to avoid spurious answers and fully explore the cross-modal relationships, a mixed-supervised learning strategy is designed for optimizing the reasoning tasks. The experiments on several benchmark video question–answer datasets clearly verify the performance and effectiveness of CTDA-QA, which contains the results in contrast to the state-of-the-art methods. Besides, the provided ablation study and visualization results further reveal the potential of CTDA-QA.

SCT: Summary Caption Technique for Retrieving Relevant Images in Alignment with Multimodal Abstractive Summary

This work proposes an efficient Summary Caption Technique that considers the multimodal summary and image captions as input to retrieve the correspondence images from the captions that are highly influential to the multimodal summary. Matching a multimodal summary with an appropriate image is a challenging task in computer vision and natural language processing. Merging in these fields is tedious, though the research community has steadily focused on cross-modal retrieval. These issues include the visual question-answering, matching queries with the images, and semantic relationship matching between two modalities for retrieving the corresponding image. Relevant works consider questions to match the relationship of visual information and object detection and to match the text with visual information and employing structural-level representation to align the images with the text. However, these techniques are primarily focused on retrieving the images to text or for image captioning. But less effort has been spent on retrieving relevant images for the multimodal summary. Hence, our proposed technique extracts and merge features in the Hybrid Image Text layer and captions in the semantic embeddings with word2vec where the contextual features and semantic relationships are compared and matched with each vector between the modalities, with cosine semantic similarity. In cross-modal retrieval, we achieve top five related images and align the relevant images to the multimodal summary that achieves the highest cosine score among the retrieved images. The model has been trained with seq-to-seq modal with 100 epochs, besides reducing the information loss by the sparse categorical cross entropy. Further, experimenting with the multimodal summarization with multimodal output dataset, in cross-modal retrieval, helps to evaluate the quality of image alignment with an image-precision metric that demonstrate the best results.

Prompting large language model with context and pre-answer for knowledge-based VQA

Existing studies apply Large Language Model (LLM) to knowledge-based Visual Question Answering (VQA) with encouraging results. Due to the insufficient input information, the previous methods still have shortcomings in constructing the prompt for LLM, and cannot fully activate the capacity of LLM. In addition, previous works adopt GPT-3 for inference, which has expensive costs. In this paper, we propose PCPA: a framework that Prompts LLM with Context and Pre-Answer for VQA. Specifically, we adopt a vanilla VQA model to generate in-context examples and candidate answers, and add a pre-answer selection layer to generate pre-answers. We integrate in-context examples and pre-answers into the prompt to inspire the LLM. In addition, we choose LLaMA instead of GPT-3, which is an open and free model. We build a small dataset to fine-tune the LLM. Compared to existing baselines, the PCPA improves accuracy by more than 2.1 and 1.5 on OK-VQA and A-OKVQA, respectively.

Bridging the Cross-Modality Semantic Gap in Visual Question Answering.

The objective of visual question answering (VQA) is to adequately comprehend a question and identify relevant contents in an image that can provide an answer. Existing approaches in VQA often combine visual and question features directly to create a unified cross-modality representation for answer inference. However, this kind of approach fails to bridge the semantic gap between visual and text modalities, resulting in a lack of alignment in cross-modality semantics and the inability to match key visual content accurately. In this article, we propose a model called the caption bridge-based cross-modality alignment and contrastive learning model (CBAC) to address the issue. The CBAC model aims to reduce the semantic gap between different modalities. It consists of a caption-based cross-modality alignment module and a visual-caption (V-C) contrastive learning module. By utilizing an auxiliary caption that shares the same modality as the question and has closer semantic associations with the visual, we are able to effectively reduce the semantic gap by separately matching the caption with both the question and the visual to generate pre-alignment features for each, which are then used in the subsequent fusion process. We also leverage the fact that V-C pairs exhibit stronger semantic connections compared to question-visual (Q-V) pairs to employ a contrastive learning mechanism on visual and caption pairs to further enhance the semantic alignment capabilities of single-modality encoders. Extensive experiments conducted on three benchmark datasets demonstrate that the proposed model outperforms previous state-of-the-art VQA models. Additionally, ablation experiments confirm the effectiveness of each module in our model. Furthermore, we conduct a qualitative analysis by visualizing the attention matrices to assess the reasoning reliability of the proposed model.

ZVQAF: Zero-shot visual question answering with feedback from large language models

Due to the prominent zero-shot generalization in new language tasks shown by large language models (LLMs), applying LLMs for zero-shot visual question answering (VQA) has been a new trend. However, most prior approaches directly use off-the-shelf captioning models to generate captions that compose in-context examples for LLMs, and such generated captions may be uninformative, thus leading the LLMs to give false predictions. To address this, we propose zero-shot VQA with feedback from LLMs (ZVQAF), a framework that applies LLMs to discriminate the quality of generated captions and leverages this feedback to train the captioning model. ZVQAF consists of two stages: the first stage is the training with feedback, which enables the captioning model to recognize the task objective and information requirements from the LLM, and the second stage is utilizing the optimized captioning model and LLM for inference. Extensive experiments show that ZVQAF achieves zero-shot VQA performance that is comparable or even superior to those previous zero-shot, few-shot, and end-to-end training approaches. For example, on VQAv2 test dataset, ZVQAF outperforms Flamingo (Alayrac et al., 2022) which employs end-to-end training by a large margin of 8.0%. In addition, on A-OKVQA dataset, ZVQAF outperforms zero-shot method Img2LLM (Guo et al., 2023) by 3.8% when employing LLMs with similar scales.

3D Question Answering.

Visual question answering (VQA) has experienced tremendous progress in recent years. However, most efforts have only focused on 2D image question-answering tasks. In this article, we extend VQA to its 3D counterpart, 3D question answering (3DQA), which can facilitate a machine's perception of 3D real-world scenarios. Unlike 2D image VQA, 3DQA takes the color point cloud as input and requires both appearance and 3D geometrical comprehension to answer the 3D-related questions. To this end, we propose a novel transformer-based 3DQA framework "3DQA-TR", which consists of two encoders to exploit the appearance and geometry information, respectively. Finally, the multi-modal information about the appearance, geometry, and linguistic question can attend to each other via a 3D-linguistic Bert to predict the target answers. To verify the effectiveness of our proposed 3DQA framework, we further develop the first 3DQA dataset "ScanQA", which builds on the ScanNet dataset and contains over 10 K question-answer pairs for 806 scenes. To the best of our knowledge, ScanQA is the first large-scale dataset with natural-language questions and free-form answers in 3D environments that is fully human-annotated. We also use several visualizations and experiments to investigate the astonishing diversity of the collected questions and the significant differences between this task from 2D VQA and 3D captioning. Extensive experiments on this dataset demonstrate the obvious superiority of our proposed 3DQA framework over state-of-the-art VQA frameworks and the effectiveness of our major designs. Our code and dataset will be made publicly available to facilitate research in this direction. The code and data are available at http://shuquanye.com/3DQA_website/.

Relational reasoning and adaptive fusion for visual question answering

Relational reasoning and adaptive fusion for visual question answering

IMCN: Improved modular co-attention networks for visual question answering

IMCN: Improved modular co-attention networks for visual question answering

Simple contrastive learning in a self-supervised manner for robust visual question answering

Recent observations have revealed that Visual Question Answering models are susceptible to learning the spurious correlations formed by dataset biases, i.e., the language priors, instead of the intended solution. For instance, given a question and a relative image, some VQA systems are prone to provide the frequently occurring answer in the dataset while disregarding the image content. Such a preferred tendency has caused them to be brittle in real-world settings, harming the robustness of VQA models. We experimentally found that conventional VQA methods often confuse negative samples that with identical questions but different images, which results in the generation of linguistic bias. In this paper, we propose a simple contrastive learning scheme, namely SCLSM, to mitigate the above issues in a self-supervised manner. We construct several special negative samples and introduce a debiasing-aware contrastive learning approach to help the model learn more discriminative multimodal features, thus improving the ability of debiasing. The SCLSM is compatible with numerous VQA baselines. Experimental results on the widely-used public datasets VQA-CP v2 and VQA v2 validate the effectiveness of our proposed model.

Collaborative Modality Fusion for Mitigating Language Bias in Visual Question Answering.

Language bias stands as a noteworthy concern in visual question answering (VQA), wherein models tend to rely on spurious correlations between questions and answers for prediction. This prevents the models from effectively generalizing, leading to a decrease in performance. In order to address this bias, we propose a novel modality fusion collaborative de-biasing algorithm (CoD). In our approach, bias is considered as the model's neglect of information from a particular modality during prediction. We employ a collaborative training approach to facilitate mutual modeling between different modalities, achieving efficient feature fusion and enabling the model to fully leverage multimodal knowledge for prediction. Our experiments on various datasets, including VQA-CP v2, VQA v2, and VQA-VS, using different validation strategies, demonstrate the effectiveness of our approach. Notably, employing a basic baseline model resulted in an accuracy of 60.14% on VQA-CP v2.

Coordinating explicit and implicit knowledge for knowledge-based VQA

Pre-trained models often generate plausible looking statements that are factually incorrect because of the inaccurate implicit knowledge contained in the model’s parameters. Related methods retrieve explicit knowledge from the external knowledge source to help improve the prediction performance and reliability. However, these methods often use weak training signals for the retriever, and require the model to make each prediction based on the retrieved knowledge, even when the retrieved knowledge is not reliable or the model can produce better prediction only using its implicit knowledge. Therefore, it is necessary to enable the pre-trained model to actively select more beneficial knowledge for producing better prediction. This work proposes a novel method to help the model to Coordinate Explicit and Implicit Knowledge (CEIK) for the knowledge-based visual question answering (VQA) task, which is an important direction of pre-trained models. Furthermore, a better training signal is proposed for the retriever according to whether the retrieved knowledge can correct the prediction. Experimental results demonstrate the effectiveness of our method.

A focus fusion attention mechanism integrated with image captions for knowledge graph-based visual question answering

A focus fusion attention mechanism integrated with image captions for knowledge graph-based visual question answering

A Dual-Attention Learning Network With Word and Sentence Embedding for Medical Visual Question Answering.

Research in medical visual question answering (MVQA) can contribute to the development of computer-aided diagnosis. MVQA is a task that aims to predict accurate and convincing answers based on given medical images and associated natural language questions. This task requires extracting medical knowledge-rich feature content and making fine-grained understandings of them. Therefore, constructing an effective feature extraction and understanding scheme are keys to modeling. Existing MVQA question extraction schemes mainly focus on word information, ignoring medical information in the text, such as medical concepts and domain-specific terms. Meanwhile, some visual and textual feature understanding schemes cannot effectively capture the correlation between regions and keywords for reasonable visual reasoning. In this study, a dual-attention learning network with word and sentence embedding (DALNet-WSE) is proposed. We design a module, transformer with sentence embedding (TSE), to extract a double embedding representation of questions containing keywords and medical information. A dual-attention learning (DAL) module consisting of self-attention and guided attention is proposed to model intensive intramodal and intermodal interactions. With multiple DAL modules (DALs), learning visual and textual co-attention can increase the granularity of understanding and improve visual reasoning. Experimental results on the ImageCLEF 2019 VQA-MED (VQA-MED 2019) and VQA-RAD datasets demonstrate that our proposed method outperforms previous state-of-the-art methods. According to the ablation studies and Grad-CAM maps, DALNet-WSE can extract rich textual information and has strong visual reasoning ability.

So Many Heads, So Many Wits: Multimodal Graph Reasoning for Text-Based Visual Question Answering

So Many Heads, So Many Wits: Multimodal Graph Reasoning for Text-Based Visual Question Answering

See and Learn More: Dense Caption-Aware Representation for Visual Question Answering

See and Learn More: Dense Caption-Aware Representation for Visual Question Answering

VL-Few: Vision Language Alignment for Multimodal Few-Shot Meta Learning

Complex tasks in the real world involve different modal models, such as visual question answering (VQA). However, traditional multimodal learning requires a large amount of aligned data, such as image text pairs, and constructing a large amount of training data is a challenge for multimodal learning. Therefore, we propose VL-Few, which is a simple and effective method to solve the multimodal few-shot problem. VL-Few (1) proposes the modal alignment, which aligns visual features into language space through a lightweight model network and improves the multimodal understanding ability of the model; (2) adopts few-shot meta learning in the multimodal problem, which constructs a few-shot meta task pool to improve the generalization ability of the model; (3) proposes semantic alignment to enhance the semantic understanding ability of the model for the task, context, and demonstration; (4) proposes task alignment that constructs training data into the target task form and improves the task understanding ability of the model; (5) proposes generation alignment, which adopts the token-level training and multitask fusion loss to improve the generation ability of the model. Our experimental results show the effectiveness of VL-Few for multimodal few-shot problems.

From image to language: A critical analysis of Visual Question Answering (VQA) approaches, challenges, and opportunities

The multimodal task of Visual Question Answering (VQA) encompassing elements of Computer Vision (CV) and Natural Language Processing (NLP), aims to generate answers to questions on any visual input. Over time, the scope of VQA has expanded from datasets focusing on an extensive collection of natural images to datasets featuring synthetic images, video, 3D environments, and various other visual inputs. The emergence of large pre-trained networks has shifted the early VQA approaches relying on feature extraction and fusion schemes to vision language pre-training (VLP) techniques. However, there is a lack of comprehensive surveys that encompass both traditional VQA architectures and contemporary VLP-based methods. Furthermore, the VLP challenges in the lens of VQA haven’t been thoroughly explored, leaving room for potential open problems to emerge. Our work presents a survey in the domain of VQA that delves into the intricacies of VQA datasets and methods over the field’s history, introduces a detailed taxonomy to categorize the facets of VQA, and highlights the recent trends, challenges, and scopes for improvement. We further generalize VQA to multimodal question answering, explore tasks related to VQA, and present a set of open problems for future investigation. The work aims to navigate both beginners and experts by shedding light on the potential avenues of research and expanding the boundaries of the field.