Test-Time Model Adaptation for Visual Question Answering With Debiased Self-Supervisions

The ability of Visual Question Answering (VQA) models to provide insightful responses to images based on their content holds the potential to completely transform machine interpretation and interaction. VQA systems face challenges due to noisy image data, making accurate question answering difficult. Several deep learning algorithms are used for predicting answers from images, but they don’t yield enough outcomes. To get over these problems, this work is suggested. In this manuscript, a visual question–answer model-based Transformer-Based Multi-Head Cross Attention Network with optimized Gold Rush Octave Convolution Network (T-MHCAN-OGRCNN) is proposed. To enhance VQA accuracy, three datasets are taken: VQA v2.0, OK-VQA, and FVQA, which include images with embedded questions and answers. These images often contain noise, necessitating pre-processing for improved results. Then, Task-Oriented Homogenized Automatic (TOHA) method for noise reduction is used. For word embedding, Multiscale Parallel MobileNetV3 (WE-MP-MobileNetV3) is employed. Following that, visual and textual features are extracted using Transformer-Based Multi-Head Cross Attention Network (T-MHCAN). The core VQA task is handled by a Fully Octave Convolution Network (OCNN), whose weight parameters are optimized using Gold Rush Optimizer (GRO). Therefore, this method incorporates cutting-edge strategies to improve precision and resilience of VQA systems. The suggested T-MHCAN-OGRCNN technique is implemented on Python platform. Three datasets were used to evaluate the effectiveness of the suggested T-MHCAN-OGRCNN, which achieves superior results than current techniques with 99.9% accuracy and 99.8% recall. This demonstrates approach’s exceptional effectiveness and room for growth in industry.

Most Visual Question Answering (VQA) models suffer from the language prior problem, which is caused by inherent data biases. Specifically, VQA models tend to answer questions (e.g., what color is the banana?) based on the high-frequency answers (e.g., yellow) ignoring image contents. Existing approaches tackle this problem by creating delicate models or introducing additional visual annotations to reduce question dependency and strengthen image dependency. However, they are still subject to the language prior problem since the data biases have not been fundamentally addressed. In this paper, we introduce a self-supervised learning framework to solve this problem. Concretely, we first automatically generate labeled data to balance the biased data, and then propose a self-supervised auxiliary task to utilize the balanced data to assist the VQA model to overcome language priors. Our method can compensate for the data biases by generating balanced data without introducing external annotations. Experimental results show that our method achieves state-of-the-art performance, improving the overall accuracy from 49.50% to 57.59% on the most commonly used benchmark VQA-CP v2. In other words, we can increase the performance of annotation-based methods by 16% without using external annotations. Our code is available on GitHub.

Methodologies for training visual question answering (VQA) models assume the availability of datasets with human-annotated \textit{Image-Question-Answer} (I-Q-A) triplets. This has led to heavy reliance on datasets and a lack of generalization to new types of questions and scenes. Linguistic priors along with biases and errors due to annotator subjectivity have been shown to percolate into VQA models trained on such samples. We study whether models can be trained without any human-annotated Q-A pairs, but only with images and their associated textual descriptions or captions. We present a method to train models with synthetic Q-A pairs generated procedurally from captions. Additionally, we demonstrate the efficacy of spatial-pyramid image patches as a simple but effective alternative to dense and costly object bounding box annotations used in existing VQA models. Our experiments on three VQA benchmarks demonstrate the efficacy of this weakly-supervised approach, especially on the VQA-CP challenge, which tests performance under changing linguistic priors.

Most Visual Question Answering (VQA) models are faced with language bias when learning to answer a given question, thereby failing to understand multimodal knowledge simultaneously. Based on the fact that VQA samples with different levels of language bias contribute differently for answer prediction, in this paper, we overcome the language prior problem by proposing a novel Language Bias driven Curriculum Learning (LBCL) approach, which employs an easy-to-hard learning strategy with a novel difficulty metric Visual Sensitive Coefficient (VSC). Specifically, in the initial training stage, the VQA model mainly learns the superficial textual correlations between questions and answers (easy concept) from more-biased examples, and then progressively focuses on learning the multimodal reasoning (hard concept) from less-biased examples in the following stages. The curriculum selection of examples on different stages is according to our proposed difficulty metric VSC, which is to evaluate the difficulty driven by the language bias of each VQA sample. Furthermore, to avoid the catastrophic forgetting of the learned concept during the multi-stage learning procedure, we propose to integrate knowledge distillation into the curriculum learning framework. Extensive experiments show that our LBCL can be generally applied to common VQA baseline models, and achieves remarkably better performance on the VQA-CP v1 and v2 datasets, with an overall 20% accuracy boost over baseline models.

This paper demonstrates a medical visual question answering (VQA) system to address three challenges: 1) medical VQA often lacks large-scale labeled training data which requires huge efforts to build; 2) it is costly to implement and thoroughly compare medical VQA models on self-created datasets; 3) applying general VQA models to the medical domain by transfer learning is challenging due to various visual concepts between general images and medical images. Our system has three main components: data generation, model library, and model practice. To address the first challenge, we first allow users to upload self-collected clinical data such as electronic medical records (EMRs) to the data generation component and provides an annotating tool for labeling the data. Then, the system semi-automatically generates medical VQAs for users. Second, we develop a model library by implementing VQA models for users to evaluate their datasets. Users can do simple configurations by selecting self-interested models. The system then automatically trains the models, conducts extensive experimental evaluation, and reports comprehensive findings. The reports provide new insights into the strengths and weaknesses of selected models. Third, we provide an online chat module for users to communicate with an AI robots for further evaluating the models. The source codes are shared on https://github.com/shyanneshan/VQA-Demo.

Albeit progress has been made in Composed Image Retrieval (CIR), we empirically find that a certain percentage of failure retrieval results are not consistent with their relative captions. To address this issue, this work provides a Visual Question Answering (VQA) perspective to boost the performance of CIR. The resulting VQA4CIR is a post-processing approach and can be directly plugged into existing CIR methods. Given the top-C retrieved images by a CIR method, VQA4CIR aims to decrease the adverse effect of the failure retrieval results being inconsistent with the relative caption. To find the retrieved images inconsistent with the relative caption, we resort to the "QA generation → VQA" self-verification pipeline. For QA generation, we suggest fine-tuning LLM (e.g., LLaMA) to generate several pairs of questions and answers from each relative caption. We then fine-tune LVLM (e.g., LLaVA) to obtain the VQA model. By feeding the retrieved image and question to the VQA model, one can find the images inconsistent with relative caption when the answer by VQA is inconsistent with the answer in the QA pair. Consequently, the CIR performance can be boosted by modifying the ranks of inconsistently retrieved images. Experimental results show that our proposed method outperforms state-of-the-art CIR methods on the CIRR and Fashion-IQ datasets.

Recent advances in vision-language models have resulted in improved accuracy in visual question answering (VQA) tasks. However, their robustness remains limited when faced with out-of-distribution data containing unanswerable questions. In this study, we first construct a simple randomised VQA dataset, incorporating unanswerable questions from the VQA v2 dataset, to evaluate the robustness of a state-of-the-art VQA model. Our findings reveal that the model struggles to predict the “unknown” answer or provides inaccurate responses with high confidence scores for irrelevant questions. To address this issue without retraining the large backbone models, we propose Cross Modal Augmentation (CMA), a model-agnostic, test-time-only, multi-modal semantic augmentation technique. CMA generates multiple semantically-consistent but heterogeneous instances from the visual and textual inputs, which are then fed to the model, and the predictions are combined to achieve a more robust output. We demonstrate that implementing CMA enables the VQA model to provide more reliable answers in scenarios involving unanswerable questions, and show that the approach is generalisable across different categories of pre-trained vision language models.

Counting-based visual question answering with serial cascaded attention deep learning

In this paper, we make a simple observation that questions about images often contain premises – objects and relationships implied by the question – and that reasoning about premises can help Visual Question Answering (VQA) models respond more intelligently to irrelevant or previously unseen questions. When presented with a question that is irrelevant to an image, state-of-the-art VQA models will still answer purely based on learned language biases, resulting in non-sensical or even misleading answers. We note that a visual question is irrelevant to an image if at least one of its premises is false (i.e. not depicted in the image). We leverage this observation to construct a dataset for Question Relevance Prediction and Explanation (QRPE) by searching for false premises. We train novel question relevance detection models and show that models that reason about premises consistently outperform models that do not. We also find that forcing standard VQA models to reason about premises during training can lead to improvements on tasks requiring compositional reasoning.

Visual question answering is a complex multimodal task involving images and text, with broad application prospects in human–computer interaction and medical assistance. Therefore, how to deal with the feature interaction and multimodal feature fusion between the critical regions in the image and the keywords in the question is an important issue. To this end, we propose a neural network based on the encoder–decoder structure of the transformer architecture. Specifically, in the encoder, we use multi-head self-attention to mine word–word connections within question features and stack multiple layers of attention to obtain multi-level question features. We propose a mutual attention module to perform information exchange between modalities for better question features and image features representation on the decoder side. Besides, we connect the encoder and decoder in a meshed manner, perform mutual attention operations with multi-level question features, and aggregate information in an adaptive way. We propose a multi-scale fusion module in the fusion stage, which utilizes feature information at different scales to complete modal fusion. We test and validate the model effectiveness on VQA v1 and VQA v2 datasets. Our model achieves better results than state-of-the-art methods.

Safety compliance checking of construction behaviors using visual question answering

We propose EASE, a simple diagnostic tool for Visual Question Answering (VQA) which quantifies the difficulty of an image, question sample. EASE is based on the pattern of answers provided by multiple annotators to a given question. In particular, it considers two aspects of the answers: (i) their Entropy; (ii) their Semantic content. First, we prove the validity of our diagnostic to identify samples that are easy/hard for state-of-art VQA models. Second, we show that EASE can be successfully used to select the most-informative samples for training/fine-tuning. Crucially, only information that is readily available in any VQA dataset is used to compute its scores.

Visual Question Answering (VQA) is a method of answering questions in natural language based on the content of images and has been widely concerned by researchers. The existing research on the visual question answering model mainly focuses on the point of view of attention mechanism and multi-modal fusion. It only pays attention to the visual semantic features of the image in the process of image modeling, ignoring the importance of modeling the spatial relationship of visual objects. We are aiming at the existing problems of the existing VQA model research. An effective spatial relationship reasoning network model is proposed, which can combine visual object semantic reasoning and spatial relationship reasoning at the same time to realize fine-grained multi-modal reasoning and fusion. A sparse attention encoder is designed to capture contextual information effectively in the semantic reasoning module. In the spatial relationship reasoning module, the graph neural network attention mechanism is used to model the spatial relationship of visual objects, which can correctly answer complex spatial relationship reasoning questions. Finally, a practical compact self-attention (CSA) mechanism is designed to reduce the redundancy of self-attention in linear transformation and the number of model parameters and effectively improve the model's overall performance. Quantitative and qualitative experiments are conducted on the benchmark datasets of VQA 2.0 and GQA. The experimental results demonstrate that the proposed method performs favorably against the state-of-the-art approaches. Our best single model has an overall accuracy of 71.18% on the VQA 2.0 dataset and 57.59% on the GQA dataset.

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.

We propose a novel approach to identify the difficulty of visual questions for Visual Question Answering (VQA) without direct supervision or annotations to the difficulty. Prior works have considered the diversity of ground-truth answers of human annotators. In contrast, we analyze the difficulty of visual questions based on the behavior of multiple different VQA models. We propose to cluster the entropy values of the predicted answer distributions obtained by three different models: a baseline method that takes as input images and questions, and two variants that take as input images only and questions only. We use a simple k-means to cluster the visual questions of the VQA v2 validation set. Then we use state-of-the-art methods to determine the accuracy and the entropy of the answer distributions for each cluster. A benefit of the proposed method is that no annotation of the difficulty is required, because the accuracy of each cluster reflects the difficulty of visual questions that belong to it. Our approach can identify clusters of difficult visual questions that are not answered correctly by state-of-the-art methods. Detailed analysis on the VQA v2 dataset reveals that 1) all methods show poor performances on the most difficult cluster (about 10% accuracy), 2) as the cluster difficulty increases, the answers predicted by the different methods begin to differ, and 3) the values of cluster entropy are highly correlated with the cluster accuracy. We show that our approach has the advantage of being able to assess the difficulty of visual questions without ground-truth (i.e., the test set of VQA v2) by assigning them to one of the clusters. We expect that this can stimulate the development of novel directions of research and new algorithms.