Dual Path Multi-modal High-Order Features for Textual Content based Visual Question Answering

Visual question answering: Datasets, algorithms, and future challenges

Image captioning and visual question answering are typical tasks that connect computer vision and natural language processing. Both of them need to effectively represent the visual content using computer vision methods and smoothly process the text sentence using natural language processing skills. The key problem of these two tasks is to infer the target result based on the interactive understanding of the word sequence and the image. Though they practically use similar algorithms, they are studied independently in the past few years. In this article, we attempt to exploit the mutual correlation between these two tasks. We propose the first VQA-improved image-captioning method that transfers the knowledge learned from the VQA corpora to the image-captioning task. A VQA model is first pretrained on image--question--answer instances. Then, the pretrained VQA model is used to extract VQA-grounded semantic representations according to selected free-form open-ended visual question--answer pairs. The VQA-grounded features are complementary to the visual features, because they interpret images from a different perspective. We incorporate the VQA model into the image-captioning model by adaptively fusing the VQA-grounded feature and the attended visual feature. We show that such simple VQA-improved image-captioning (VQA-IIC) models perform better than conventional image-captioning methods on large-scale public datasets.

Deep learning methods have revolutionized speech recognition, image recognition, and natural language processing since 2010. Each of these tasks involves a single modality in their input signals. However, many applications in the artificial intelligence field involve multiple modalities. Therefore, it is of broad interest to study the more difficult and complex problem of modeling and learning across multiple modalities. In this paper, we provide a technical review of available models and learning methods for multimodal intelligence. The main focus of this review is the combination of vision and natural language modalities, which has become an important topic in both the computer vision and natural language processing research communities. This review provides a comprehensive analysis of recent works on multimodal deep learning from three perspectives: learning multimodal representations, fusing multimodal signals at various levels, and multimodal applications. Regarding multimodal representation learning, we review the key concepts of embedding, which unify multimodal signals into a single vector space and thereby enable cross-modality signal processing. We also review the properties of many types of embeddings that are constructed and learned for general downstream tasks. Regarding multimodal fusion, this review focuses on special architectures for the integration of representations of unimodal signals for a particular task. Regarding applications, selected areas of a broad interest in the current literature are covered, including image-to-text caption generation, text-to-image generation, and visual question answering. We believe that this review will facilitate future studies in the emerging field of multimodal intelligence for related communities.

Visual Question Answering (VQA) is a multimodal research related to Computer Vision (CV) and Natural Language Processing (NLP). How to better obtain useful information from images and questions and give an accurate answer to the question is the core of the VQA task. This paper presents a VQA model based on multimodal encoders and decoders with gate attention (MEDGA). Each encoder and decoder block in the MEDGA applies not only self-attention and crossmodal attention but also gate attention, so that the new model can better focus on inter-modal and intra-modal interactions simultaneously within visual and language modality. Besides, MEDGA further filters out noise information irrelevant to the results via gate attention and finally outputs attention results that are closely related to visual features and language features, which makes the answer prediction result more accurate. Experimental evaluations on the VQA 2.0 dataset and the ablation experiments under different conditions prove the effectiveness of MEDGA. In addition, the MEDGA accuracy on the test-std dataset has reached 70.11%, which exceeds many existing methods.

Recent advancements in computer vision and natural language processing were applied to the Visual Question Answering task. Nonetheless, a significant proportion of models exhibiting high accuracy possess extensive architectural components. This has a significant impact on the process of bringing the technology to practical applications such as assistive devices for the blind and visually impaired, and other related fields. Our research focuses on compressing the Visual Question Answering model on the Vietnamese dataset by utilizing the knowledge distillation method. Furthermore, in order to enhance precision, we have also developed a Mixture of ViVQA Experts system that will adapt to each type of question for improving accuracy while increasing only a few parameters and not wasting time retraining the entire system from scratch. With a total of 204M parameters, this approach has reduced the size by 24.51% compared to the original model while only reducing accuracy by 6.59\% on the overall test set. More specifically, we have made accuracy improvements on each question type: "number" increased by 1.35% and "color" increased by 0.48\% compared to our distillation model. The code and pretrained models are available at: anonymous.

Visual question answering (VQA) research has garnered increasing attention in recent years. It is considered a visual Turing test because it requires a computer to respond to textual questions based on an image. Expertise in computer vision, natural language processing, knowledge understanding, and reasoning is required to solve the problem of VQA. Most techniques employed for VQA consist of models that are developed to learn the combination of image and question features along with the expected answer. The techniques chosen for image and question feature extraction and combining the features change with each model. This method of teaching a model of the question–answer pattern is ineffective for queries that involve counting and reasoning. This approach also requires considerable resources and large datasets for the training. The general VQA datasets feature a restricted number of items as responses to counting questions ([Formula: see text]), and the distribution of the answers is not uniform. To investigate these issues in VQA, we created synthetic datasets that could be modified to adjust the number of objects in the image and the amount of occlusion. Specifically, a zero-shot learning VQA system was devised for counting-related questions that provide answers by analyzing the output of an object detector and the query keywords. Using synthetic datasets, our model generated 100% correct results. Testing on the benchmark datasets task directed image understanding challenge (TDIUC) and TallyQA-simple indicated that the proposed model matched the performance of the learning-based baseline models. This methodology can be used efficiently for counting VQA questions confined to certain domains when the number of items to be counted is significant.

The task of visual question answering (VQA) is receiving increasing interest from researchers in both the computer vision and natural language processing fields. Tremendous advances have been seen in the field of computer vision due to the success of deep learning, in particular on low- and midlevel tasks, such as image segmentation or object recognition. These advances have fueled researchers' confidence for tackling more complex tasks that combine vision with language and high-level reasoning. VQA is a prime example of this trend. This article presents the ongoing work in the field and the current approaches to VQA based on deep learning. VQA constitutes a test for deep visual understanding and a benchmark for general artificial intelligence (AI). While the field of VQA has seen recent successes, it remains a largely unsolved task.

Integrating information from vision and language modalities has sparked interesting applications in the fields of computer vision and natural language processing. Existing methods, though promising in tasks like image captioning and visual question answering, face challenges in understanding real-life issues and offering step-by-step solutions. In particular, they typically limit their scope to solutions with a sequential structure, thus ignoring complex inter-step dependencies. To bridge this gap, we propose a graph-based approach to vision-language problem solving. It leverages a novel integrated attention mechanism that jointly considers the importance of features within each step as well as across multiple steps. Together with a graph neural network method, this attention mechanism can be progressively learned to predict sequential and non-sequential solution graphs depending on the characterization of the problem-solving process. To tightly couple attention with the problem-solving procedure, we further design new learning objectives with attention metrics that quantify this integrated attention, which better aligns visual and language information within steps, and more accurately captures information flow between steps. Experimental results on VisualHow, a comprehensive dataset of varying solution structures, show significant improvements in predicting steps and dependencies, demonstrating the effectiveness of our approach in tackling various vision-language problems.

Visual Question Answering (VQA) is a multifaceted task that integrates computer vision and natural language processing to produce textual answers from images and questions. Existing VQA benchmarks predominantly adhere to a closed-set paradigm, limiting their ability to address arbitrary, unseen answers, and thus falling short in real-world scenarios. To address this limitation, we introduce the Open-Vocabulary Visual Question Answering (OVVQA) benchmark, specifically designed to evaluate models under open-world conditions by assessing their performance on both base classes (seen, common answers) and novel classes (unseen, rare answers). In conjunction with this benchmark, we propose a model-agnostic Causal Adapter to combat the inherent bias found in current VQA tasks. Our approach leverages front-door adjustment to enhance causal reasoning, significantly improving model performance on novel categories while maintaining accuracy on base classes. Additionally, we introduce an adaptive transfer loss to facilitate the transfer of more knowledge from the pretrained model to our OVVQA task. Extensive experiments across multiple datasets validate the superiority of our method over existing state-of-the-art approaches, demonstrating its robust generalization and adaptability in open-world VQA scenarios.

We propose a model for free-form visual question answering (VQA) from human brain activity. The task of VQA is leading to an answer given an image and a question about the image. Given brain activity data measured by functional magnetic resonance imaging (fMRI) and a natural language question in terms of the viewed image, the proposed method can provide an accurate natural language answer with the VQA algorithm. Visual questions selectively approach various areas of an image such as objects and backgrounds. As a result, a more detailed understanding of the image and complex reasoning are typically needed than general image captioning models. In this paper, we propose a method of answering a given question about a viewed image from fMRI data based on the VQA algorithm. We estimate the relation between fMRI data and visual features extracted from viewed images. Based on the relationship, we convert fMRI data into visual features. Finally, the proposed method can answer to a visual question from fMRI data measured while subjects are viewing images. Experimental results show that the proposed method enables accurate answering for questions about viewed images.

Visual Question Answering (VQA) has attracted much attention recently in both natural language processing and computer vision communities, as it offers insight into the relationships between two relevant sources of information. Tremendous advances are seen in the field of VQA due to the success of deep learning. Based upon advances and improvements, the Affective Visual Question Answering Network (AVQAN) enriches the understanding and analysis of VQA models by making use of the emotional information contained in the images to produce sensitive answers, while maintaining the same level of accuracy as ordinary VQA baseline models. It is a reasonably new task to integrate the emotional information contained in the images into VQA. However, it is challenging to separate questionguided-attention from mood-guided-attention due to the concatenation of the question words and the mood labels in AVQAN. Also, it is believed that this type of concatenation is harmful to the performance of the model. To mitigate such an effect, we propose the Double-Layer Affective Visual Question Answering Network (DAVQAN) that divides the task of generating emotional answers in VQA into two simpler subtasks: the generation of non-emotional responses and the production of mood labels, and two independent layers are utilized to tackle these subtasks. Comparative experimentation conducted on a preprocessed dataset to performance comparison shows that the overall performance of DAVQAN is 7.6% higher than AVQAN, demonstrating the effectiveness of the proposed model.We also introduce more advanced word embedding method and more fine-grained image feature extractor into AVQAN and DAVQAN to further improve their performance and obtain better results than their original models, which proves that VQA integrated with affective computing can improve the performance of the whole model by improving these two modules just like the general VQA.

Lying in the cross-section of computer vision and natural language processing, vision language models are capable of processing images and text at once. These models are helpful in various tasks: text generation from image and vice versa, image-text retrieval, or visual navigation. Besides building a model trained on a dataset for a task, people also study general-purpose models to utilize many datasets for multitasks. Their two primary applications are image captioning and visual question answering. For English, large datasets and foundation models are already abundant. However, for Vietnamese, they are still limited. To expand the language range, this work proposes a pretrained general-purpose image-text model named VisualRoBERTa. A dataset of 600k images with captions (translated MS COCO 2017 from English to Vietnamese) is introduced to pretrain VisualRoBERTa. The model’s architecture is built using Convolutional Neural Network and Transformer blocks. Fine-tuning VisualRoBERTa shows promising results on the ViVQA dataset with 34.49% accuracy, 0.4173 BLEU 4, and 0.4390 RougeL (in visual question answering task), and best outcomes on the sViIC dataset with 0.6685 BLEU 4, 0.6320 RougeL (in image captioning task).

Visual Question Answering (VQA) is an interdisciplinary artificial intelligence (AI) activity that integrates computer vision and natural language processing. Its purpose is to empower machines to respond to questions by util... | Find, read and cite all the research you need on Tech Science Press

Research in the Vision and Language area encompasses challenging topics that seek to connect visual and textual information. When the visual information is related to videos, this takes us into Video-Text Research, which includes several challenging tasks such as video question answering, video summarization with natural language, and video-to-text and text-to-video conversion. This paper reviews the video-to-text problem, in which the goal is to associate an input video with its textual description. This association can be mainly made by retrieving the most relevant descriptions from a corpus or generating a new one given a context video. These two ways represent essential tasks for Computer Vision and Natural Language Processing communities, called text retrieval from video task and video captioning/description task. These two tasks are substantially more complex than predicting or retrieving a single sentence from an image. The spatiotemporal information present in videos introduces diversity and complexity regarding the visual content and the structure of associated language descriptions. This review categorizes and describes the state-of-the-art techniques for the video-to-text problem. It covers the main video-to-text methods and the ways to evaluate their performance. We analyze twenty-six benchmark datasets, showing their drawbacks and strengths for the problem requirements. We also show the progress that researchers have made on each dataset, we cover the challenges in the field, and we discuss future research directions.

Visual Question Answering (VQA) is a Multimodal Interaction task between two domains, Computer vision and Natural Language Processing. The task is to develop a system that could answer a question related to an image. The task is easy for a human being, but it is difficult for a system as it involves multiple reasoning over the Image and question to get the most accurate answer. the basic step involved is to extract the image features using a Convolutional Neural Network and the question features extracted via a Recurrent Neural Network. features so extracted undergo multiple reasoning to get the most accurate answer. VQA can act as a visual assistant for the blind to know about the beautiful world around them. This work is basically to develop a VQA assistant for the blind using a real-world dataset known as VizWiz dataset originating from the blind.KeywordsVisual Question AnsweringVizWiz