Visual Question Answering Research Articles

Robust visual question answering via polarity enhancement and contrast

The Visual Question Answering (VQA) task is an important research direction in the field of artificial intelligence, which requires a model that can simultaneously understand visual images and natural language questions, and answer questions related to images. Recent studies have shown that many Visual Question Answering models rely on statistically regular correlations between questions and answers, which in turn weakens the correlation between visual content and textual information. In this work, we propose an unbiased Visual Question Answering method to solve language priors from the perspective of strengthening the contrast between the correct answer and the positive and negative predictions. We design a new model consisting of two modules with different roles. We input the image and the question corresponding to it into the Answer Visual Attention Modules to generate positive prediction output, and then use a Dual Channels Joint Module to generate negative prediction output with great linguistic prior knowledge. Finally, we input the positive and negative predictions together with the correct answer to our newly designed loss function for training. Our method achieves high performance (61.24%) on the VQA-CP v2 dataset. In addition, most existing debiasing methods improve performance on VQA-CP v2 dataset at the cost of reducing performance on VQA v2 dataset, while our method not only does not reduce the accuracy on VQA v2 dataset. Instead, it improves performance on both datasets mentioned above.

Multi-Modal Validation and Domain Interaction Learning for Knowledge-Based Visual Question Answering

Multi-Modal Validation and Domain Interaction Learning for Knowledge-Based Visual Question Answering

Robust Visual Question Answering utilizing Bias Instances and Label Imbalance

Visual Question Answering (VQA) models often suffer from bias issues which cause their predictions to rely on superficial correlations in datasets rather than the intrinsic properties of the task. However, most current debiasing methods primarily capture implicit biases but lack fine granularity, overlooking the need to address biases at a sample level. Additionally, these methods mostly fail to adequately utilize information about the imbalanced distribution of labels within the dataset. In this paper, we address these issues by utilizing Bias Instances and Label Imbalance for robust Visual Question Answering (BILI-VQA), which first identifies samples in the dataset that induce bias, defined as “Bias Instances”, and enhances their visual features during training to mitigate language shortcuts for achieving sample-level debiasing. The proposed method then introduces a new module that analyzes the label distribution, assigning higher weights to less frequent answer categories when computing the loss, thereby reducing the negative impact of label imbalance. Extensive experiments on the VQA-CP v2, VQA-CP v1, and VQA-CE datasets demonstrate the effectiveness of our BILI-VQA approach.

Image Captioning Based on Semantic Scenes.

With the development of artificial intelligence and deep learning technologies, image captioning has become an important research direction at the intersection of computer vision and natural language processing. The purpose of image captioning is to generate corresponding natural language descriptions by understanding the content of images. This technology has broad application prospects in fields such as image retrieval, autonomous driving, and visual question answering. Currently, many researchers have proposed region-based image captioning methods. These methods generate captions by extracting features from different regions of an image. However, they often rely on local features of the image and overlook the understanding of the overall scene, leading to captions that lack coherence and accuracy when dealing with complex scenes. Additionally, image captioning methods are unable to extract complete semantic information from visual data, which may lead to captions with biases and deficiencies. Due to these reasons, existing methods struggle to generate comprehensive and accurate captions. To fill this gap, we propose the Semantic Scenes Encoder (SSE) for image captioning. It first extracts a scene graph from the image and integrates it into the encoding of the image information. Then, it extracts a semantic graph from the captions and preserves semantic information through a learnable attention mechanism, which we refer to as the dictionary. During the generation of captions, it combines the encoded information of the image and the learned semantic information to generate complete and accurate captions. To verify the effectiveness of the SSE, we tested the model on the MSCOCO dataset. The experimental results show that the SSE improves the overall quality of the captions. The improvement in scores across multiple evaluation metrics further demonstrates that the SSE possesses significant advantages when processing identical images.

The Analysis of Smarter Future

With the continuous development of technology, smart home control systems, as one of the applications of artificial intelligence in daily life, are receiving increasing attention. However, smart home control systems face challenges in practical applications, such as complex scenarios and multimodal information. In this context, this paper introduces Visual Question Answering (VQA) technology to enhance the intelligence and user experience of smart home control systems. Despite the potential advantages of VQA technology in smart home control systems, current models still have certain shortcomings in handling local image information and integrating visual-language multimodal features. To address this, this paper proposes an innovative Transformer-based Multimodal Fusion Network (TMFNet) model. TMFNet aims to overcome the limitations of existing models in dealing with complex smart home scenarios by introducing a global-local feature attention mechanism, deep encoding-decoding modules, and multimodal representation modules.

A robust visual question answering approach to reduce multimodal bias

Currently, many visual question answering models have bias problems. Specifically, when the question-answer relationship in the training data shows a more obvious mapping relationship, the model shows poor generalization ability. For such biased predictions, existing research work mainly considers language bias, while ignoring the bias information introduced by images. In order to enhance the robustness of visual question answering models, a bias reduction method is proposed, and on this basis, the influence of language and visual information on bias is explored. Furthermore, two bias learning branches are constructed to capture language bias and the bias caused by language and images respectively, and the bias reduction method is used to obtain more robust prediction results. Finally, according to the difference in prediction probability between the standard visual question answering and bias branches, the samples are dynamically weighted, so that the model can dynamically adjust the learning degree for samples with different bias levels. Experiments on datasets such as VQA-CP v2.0 prove the effectiveness of the proposed method and alleviate the influence of bias on the model.

Multi-stage reasoning on introspecting and revising bias for visual question answering

Visual Question Answering (VQA) is a task that involves predicting an answer to a question depending on the content of an image. However, recent VQA methods have relied more on language priors between the question and answer rather than the image content. To address this issue, many debiasing methods have been proposed to reduce language bias in model reasoning. However, the bias can be divided into two categories: good bias and bad bias. Good bias can benefit to the answer prediction, while the bad bias may associate the models with the unrelated information. Therefore, instead of excluding good and bad bias indiscriminately in existing debiasing methods, we proposed a bias discrimination module to distinguish them. Additionally, bad bias may reduce the model’s reliance on image content during answer reasoning and thus attend little on image features updating. To tackle this, we leverage Markov theory to construct a Markov field with image regions and question words as nodes. This helps with feature updating for both image regions and question words, thereby facilitating more accurate and comprehensive reasoning about both the image content and question. To verify the effectiveness of our network, we evaluate our network on VQA v2 and VQA cp v2 datasets and conduct extensive quantity and quality studies to verify the effectiveness of our proposed network. Experimental resu- lts show that our network achieves significant performance against the previous state-of-the-art methods.

Modular Co-attention Networks in Nepali Visual Question Answering Systems

Visual question answering (VQA) has been regarded as a challenging task requiring a perfect blend of computer vision and natural language processing. As no dataset was available to train such a model for the Nepali language, a new dataset was developed during the research by translating the VQAv2 dataset. Then the dataset consisting of 202,577 images and 886,560 questions was used to train an attention-based VQA model. The dataset consists of yes/no, counting, and other questions with primarily one-word answers. Modular Co-attention Network (MCAN) was applied to the visual features extracted using the Faster RCNN framework and question embeddings extracted using the Nepali GloVe model. After co-attending the visual and language features for a few cascaded MCAN layers, the features are fused to train the whole network. During evaluation, an overall accuracy of 69.87% was obtained with 81.09% accuracy in yes/no type questions. The results surpassed the performance of models developed for Hindi and Bengali languages. Overall, novel research has been done in the Nepali Language VQA domain paving the way for further advancements.

Automated construction safety reporting system integrating deep learning-based real-time advanced detection and visual question answering

The construction sector is globally acknowledged as one of the most hazardous industries, owing to the vulnerability of its workers to accidents, injuries, and even loss of life. Effective precautionary measures are necessary and ensuring the use of personal protective equipment (PPE) by workers is crucial for protecting them from accidents. Existing deep learning-based PPE detection systems mainly use simple vision-based target detection methods for tasks such as the identification of helmets or vests, and they tend to be task-specific. However, the identification of specific PPE based on respective job types and maintaining detailed safety records, requires further innovative approaches. In this paper, we propose an innovative intelligent system that not only accurately recognizes specific PPE according to the needs of different work types but also automatically generates safety inspection reports and establishes complete safety records, thus providing critical data to support accident investigations. The proposed system integrates a target detection model, visual question answering model, and text-based analysis of the relevant regulations to realize real-time detection of PPE and automatic generation of safety inspection reports. The experimental results show that the proposed YOLOv8n-DCA network strikes a good balance between performance and computational cost—, with a mAP value of 86%. Compared to the original YOLOv8n network, the mAP value is improved by 5.1%, while the model parameters and size are significantly reduced. Further, the visual question answering model exhibited a precision is 95.9. Finally, the automatic generation of safety inspection reports was successfully realized, verifying the feasibility of the developed system. This innovative system promises a comprehensive and efficient PPE management solution for the construction industry to ensure worker safety and provide strong data support.

VG-CALF: A vision-guided cross-attention and late-fusion network for radiology images in Medical Visual Question Answering

Image and question matching is essential in Medical Visual Question Answering (MVQA) in order to accurately assess the visual-semantic correspondence between an image and a question. However, the recent state-of-the-art methods focus solely on the contrastive learning between an entire image and a question. Though contrastive learning successfully model the global relationship between an image and a question, it is less effective to capture the fine-grained alignments conveyed between image regions and question words. In contrast, large-scale pre-training poses significant drawbacks, including extended training times, handling substantial data volumes, and necessitating high computational power. To address these challenges, we propose the Vision-Guided Cross-Attention based Late Fusion (VG-CALF) network, which integrates image and question features into a unified deep model without relying on pre-training for MVQA tasks. In our proposed approach, we use self-attention to effectively leverage intra-modal relationships within each modality and implement vision-guided cross-attention to emphasize the inter-modal relationships between image regions and question words. By simultaneously considering intra-modal and inter-modal relationships, our proposed method significantly improves the overall performance of MVQA without the need for pre-training on extensive image-question pairs. Experimental results on benchmark datasets, such as, SLAKE and VQA-RAD demonstrate that our proposed approach performs competitively with existing state-of-the-art methods.

OphGLM: An ophthalmology large language-and-vision assistant

Vision computer-aided diagnostic methods have been used in early ophthalmic disease screening and diagnosis. However, the limited output formats of these methods lead to poor human–computer interaction and low clinical applicability value. Thus, ophthalmic visual question answering is worth studying. Unfortunately, no practical solutions exist before Large Language Models(LLMs). In this paper, we investigate the ophthalmic visual diagnostic interaction problem. We construct an ophthalmology large language-and-vision assistant, OphGLM, consisting of an image encoder, a text encoder, a fusion module, and an LLM module. We establish a new Chinese ophthalmic fine-tuning dataset, FundusTuning-CN, including the fundus instruction and conversation sets. Based on FundusTuning-CN, we establish a novel LLM-tuning strategy to introduce visual model understanding and ophthalmic knowledge into LLMs at a low cost and high efficiency. Leveraging the pre-training of the image encoder, OphGLM demonstrates strong visual understanding and surpasses open-source visual language models in common fundus disease classification tasks. The FundusTuning-CN enables OphGLM to surpass open-source medical LLMs in both ophthalmic knowledge and interactive capabilities. Our proposed OphGLM has the potential to revolutionize clinical applications in ophthalmology. The dataset, code, and models will be publicly available at https://github.com/ML-AILab/OphGLM.

Deep Fuzzy Multiteacher Distillation Network for Medical Visual Question Answering

Deep Fuzzy Multiteacher Distillation Network for Medical Visual Question Answering

Histopathology in focus: a review on explainable multi-modal approaches for breast cancer diagnosis.

Precision and timeliness in breast cancer detection are paramount for improving patient outcomes. Traditional diagnostic methods have predominantly relied on unimodal approaches, but recent advancements in medical data analytics have enabled the integration of diverse data sources beyond conventional imaging techniques. This review critically examines the transformative potential of integrating histopathology images with genomic data, clinical records, and patient histories to enhance diagnostic accuracy and comprehensiveness in multi-modal diagnostic techniques. It explores early, intermediate, and late fusion methods, as well as advanced deep multimodal fusion techniques, including encoder-decoder architectures, attention-based mechanisms, and graph neural networks. An overview of recent advancements in multimodal tasks such as Visual Question Answering (VQA), report generation, semantic segmentation, and cross-modal retrieval is provided, highlighting the utilization of generative AI and visual language models. Additionally, the review delves into the role of Explainable Artificial Intelligence (XAI) in elucidating the decision-making processes of sophisticated diagnostic algorithms, emphasizing the critical need for transparency and interpretability. By showcasing the importance of explainability, we demonstrate how XAI methods, including Grad-CAM, SHAP, LIME, trainable attention, and image captioning, enhance diagnostic precision, strengthen clinician confidence, and foster patient engagement. The review also discusses the latest XAI developments, such as X-VARs, LeGrad, LangXAI, LVLM-Interpret, and ex-ILP, to demonstrate their potential utility in multimodal breast cancer detection, while identifying key research gaps and proposing future directions for advancing the field.

Integrating IoT and visual question answering in smart cities: Enhancing educational outcomes

Emerging as a paradigmatic shift in urban development, smart cities harness the potential of advanced information and communication technologies to seamlessly integrate urban functions, optimize resource allocation, and improve the effectiveness of city management. Within the domain of smart education, the imperative application of Visual Question Answering (VQA) technology encounters significant limitations at the prevailing stage, particularly the absence of a robust Internet of Things (IoT) framework and the inadequate incorporation of large pre-trained language models (LLMs) within contemporary smart education paradigms, especially in addressing zero-shot VQA scenarios, which pose considerable challenges. In response to these constraints, this paper introduces an IoT-based smart city framework that is designed to refine the functionality and efficacy of educational systems. This framework is delineated into four cardinal layers: the data collection layer, data transmission layer, data management layer, and application layer. Furthermore, we introduce the innovative TeachVQA methodology at the application layer, synergizing VQA technology with extensive pre-trained language models, thereby considerably enhancing the dissemination and assimilation of educational content. Evaluative metrics in the VQAv2 and OKVQA datasets substantiate that the TeachVQA methodology not only outperforms existing VQA approaches, but also underscores its profound potential and practical relevance in the educational sector.

Adversarial Sample Synthesis for Visual Question Answering

Language prior is a major block to improving the generalization of visual question answering (VQA) models. Recent work has revealed that synthesizing extra training samples to balance training sets is a promising way to alleviate language priors. However, most existing methods synthesize extra samples in a manner independent of training processes, which neglect the fact that the language priors memorized by VQA models are changing during training, resulting in insufficient synthesized samples. In this paper, we propose an adversarial sample synthesis method, which synthesizes different adversarial samples by adversarial masking at different training epochs to cope with the changing memorized language priors. The basic idea behind our method is to use adversarial masking to synthesize adversarial samples that will cause the model to make wrong answers. To this end, we design a generative module to carry out adversarial masking by attacking the VQA model, and introduce a bias-oriented objective to supervise the training of the generative module. We couple the sample synthesis with the training process of the VQA model, which ensures that the synthesized samples at different training epochs are beneficial to the VQA model. We incorporated the proposed method into three VQA models including UpDn, LMH and LXMERT, and conducted experiments on three datasets including VQA-CP v1, VQA-CP v2 and VQA v2. Experimental results demonstrate that a large improvement of our method, such as 16.22% gains on LXMERT in the overall accuracy of VQA-CP v2.

Learning to enhance areal video captioning with visual question answering

ABSTRACT The utilization of Unmanned Aerial Vehicles (UAV) in remote sensing (RS) has witnessed a significant surge, offering valuable insights into Earth dynamics and human activities. However, this has led to a substantial increase in the volume of video data, rendering manual screening and analysis impractical. Consequently, there is a pressing need for the development of automated interpretation models for these aerial videos. In this paper, we propose a novel approach that leverages visual dialogue to enhance aerial video captioning. Our model adopts an encoder-decoder architecture, integrating a Visual Question Answering (VQA) task before the captioning task. The VQA task aims to enrich the captioning process by soliciting additional information about the image content. Specifically, our video encoder utilizes ViT-L/16, while the decoder employs Generative Pre-trained Transformer-2 (Distill-GPT-2). To validate our model, we introduce a novel benchmark dataset named CapERA-VQA, comprising videos accompanied by sets of questions, answers, and captions. Through experimental validation, we demonstrate the effectiveness of our proposed approach in enhancing the automated captioning of aerial videos.

Robust-EQA: Robust Learning for Embodied Question Answering With Noisy Labels.

Embodied question answering (EQA) is a recently emerged research field in which an agent is asked to answer the user's questions by exploring the environment and collecting visual information. Plenty of researchers turn their attention to the EQA field due to its broad potential application areas, such as in-home robots, self-driven mobile, and personal assistants. High-level visual tasks, such as EQA, are susceptible to noisy inputs, because they have complex reasoning processes. Before the profits of the EQA field can be applied to practical applications, good robustness against label noise needs to be equipped. To tackle this problem, we propose a novel label noise-robust learning algorithm for the EQA task. First, a joint training co-regularization noise-robust learning method is proposed for noisy filtering of the visual question answering (VQA) module, which trains two parallel network branches by one loss function. Then, a two-stage hierarchical robust learning algorithm is proposed to filter out noisy navigation labels in both trajectory level and action level. Finally, by taking purified labels as inputs, a joint robust learning mechanism is given to coordinate the work of the whole EQA system. Empirical results demonstrate that, under extremely noisy environments (45% of noisy labels) and low-level noisy environments (20% of noisy labels), the robustness of deep learning models trained by our algorithm is superior to the existing EQA models in noisy environments.

Prompting Large Language Models with Knowledge-Injection for Knowledge-Based Visual Question Answering

Prompting Large Language Models with Knowledge-Injection for Knowledge-Based Visual Question Answering

Fair Attention Network for Robust Visual Question Answering

Fair Attention Network for Robust Visual Question Answering

VADS: Visuo-Adaptive DualStrike attack on visual question answer

Visual Question Answering (VQA) is a fundamental task in computer vision and natural language process fields. The adversarial vulnerability of VQA models is crucial for their reliability in real-world applications. However, current VQA attacks are mainly focused on the white-box and transfer-based settings, which require the attacker to have full or partial prior knowledge of victim VQA models. Besides that, query-based VQA attacks require a massive amount of query times, which the victim model may detect. In this paper, we propose the Visuo-Adaptive DualStrike (VADS) attack, a novel adversarial attack method combining transfer-based and query-based strategies to exploit vulnerabilities in VQA systems. Unlike current VQA attacks focusing on either approach, VADS leverages a momentum-like ensemble method to search potential attack targets and compress the perturbation. After that, our method employs a query-based strategy to dynamically adjust the weight of perturbation per surrogate model. We evaluate the effectiveness of VADS across 8 VQA models and two datasets. The results demonstrate that VADS outperforms existing adversarial techniques in both efficiency and success rate. Our code is available at: https://github.com/stevenzhang9577/VADS.