From Easy to Hard: Learning Language-Guided Curriculum for Visual Question Answering on Remote Sensing Data

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.

Visual Question Answering (VQA) in remote sensing (RS) is pivotal for interpreting Earth observation data. However, existing RS VQA datasets are constrained by limitations in annotation richness, question diversity, and the assessment of specific reasoning capabilities. This paper introduces Remote Sensing Vision Language Model Question Answering (RSVLM-QA) dataset, a new large-scale, content-rich VQA dataset for the RS domain. RSVLM-QA is constructed by integrating data from several prominent RS segmentation and detection datasets: WHU, LoveDA, INRIA, and iSAID. We employ an innovative dual-track annotation generation pipeline. Firstly, we leverage Large Language Models (LLMs), specifically GPT-4.1, with meticulously designed prompts to automatically generate a suite of detailed annotations including image captions, spatial relations, and semantic tags, alongside complex caption-based VQA pairs. Secondly, to address the challenging task of object counting in RS imagery, we have developed a specialized automated process that extracts object counts directly from the original segmentation data; GPT-4.1 then formulates natural language answers from these counts, which are paired with preset question templates to create counting QA pairs. RSVLM-QA comprises 13,820 images and 162,373 VQA pairs, featuring extensive annotations and diverse question types. We provide a detailed statistical analysis of the dataset and a comparison with existing RS VQA benchmarks, highlighting the superior depth and breadth of RSVLM-QA's annotations. Furthermore, we conduct benchmark experiments on Six mainstream Vision Language Models (VLMs), demonstrating that RSVLM-QA effectively evaluates and challenges the understanding and reasoning abilities of current VLMs in the RS domain. We believe RSVLM-QA will serve as a pivotal resource for the RS VQA and VLM research communities, poised to catalyze advancements in the field. The dataset, generation code, and benchmark models are publicly available at https://github.com/StarZi0213/RSVLM-QA.

Visual question answering: Datasets, algorithms, and future challenges

Answering questions with natural language by extracting information from image has great potential in various applications. Although visual question answering (VQA) for natural image has been broadly studied, VQA for remote sensing data is still in the early research stage. For the same remote sensing image, there exist questions with dramatically different difficulty-levels. Treating these questions equally may mislead the model and limit the VQA model performance. Considering this problem, in this work, we propose a self-paced curriculum learning (SPCL) based VQA model with hard and soft weighting strategies for remote sensing data. Like human learning process, the model is trained from easy to hard question samples gradually. Extensive experimental results on two datasets demonstrate that the proposed training method can achieve promising performance.

Modelling species distributions with remote sensing data: bridging disciplinary perspectives

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.

The remarkable achievements of ChatGPT and Generative Pre-trained Transformer 4 (GPT-4) have sparked a wave of interest and research in the field of large language models (LLMs) for artificial general intelligence (AGI). These models provide intelligent solutions that are closer to human thinking, enabling us to use general artificial intelligence (AI) to solve problems in various applications. However, in the field of remote sensing (RS), the scientific literature on the implementation of AGI remains relatively scant. Existing AI-related research in RS focuses primarily on visual-understanding tasks while neglecting the semantic understanding of the objects and their relationships. This is where vision-LMs (VLMs) excel as they enable reasoning about images and their associated textual descriptions, allowing for a deeper understanding of the underlying semantics. VLMs can go beyond visual recognition of RS images and can model semantic relationships as well as generate natural language descriptions of the image. This makes them better suited for tasks that require both visual and textual understanding, such as image captioning and visual question answering (VQA). This article provides a comprehensive review of the research on VLMs in RS, summarizing the latest progress, highlighting current challenges, and identifying potential research opportunities. Specifically, we review the application of VLMs in mainstream RS tasks, including image captioning, text-based image generation, text-based image retrieval (TBIR), VQA, scene classification, semantic segmentation, and object detection. For each task, we analyze representative works and discuss research progress. Finally, we summarize the limitations of existing works and provide possible directions for future development. This review aims to provide a comprehensive overview of the current research progress of VLMs in RS (see Figure 1), and to inspire further research in this exciting and promising field.

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.

Understanding the diagnosis and management of external eye disease: Gurwood AS: Br J Optom Disp 1995;3:155–163

A survey of methods, datasets and evaluation metrics for visual question answering

Multimodal feature fusion by relational reasoning and attention for visual question answering

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.

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.

LRB-Net: Improving VQA via division of labor strategy and multimodal classifiers

Attention has been an important mechanism for both humans and computer vision systems. While state-of-the-art models to predict attention focus on estimating a static probabilistic saliency map with free-viewing behavior, real-life scenarios are filled with tasks of varying types and complexities, and visual exploration is a temporal process that contributes to task performance. To bridge the gap, we conduct a first study to understand and predict the temporal sequences of eye fixations (a.k.a. scanpaths) during performing general tasks, and examine how scanpaths affect task performance. We present a new deep reinforcement learning method to predict scanpaths leading to different performances in visual question answering. Conditioned on a task guidance map, the proposed model learns question-specific attention patterns to generate scanpaths. It addresses the exposure bias in scanpath prediction with self-critical sequence training and designs a Consistency-Divergence loss to generate distinguishable scanpaths between correct and incorrect answers. The proposed model not only accurately predicts the spatio-temporal patterns of human behavior in visual question answering, such as fixation position, duration, and order, but also generalizes to free-viewing and visual search tasks, achieving human-level performance in all tasks and significantly outperforming the state of the art.