Some studies have further explored the use of LLMs for planning and invoking models or APIs to address more general multi-modal user queries. This approach requires the model to possess internal reasoning ability and incorporate external knowledge to enhance its generalization performance. The goal of VQA is to teach machines to understand the content of an image and answer questions about it in natural language. 5 51. 6% on A-OKVQA). Emu is trained with a unified autoregressive objective, i. We propose a multimodal framework that uses language guidance (LG) in the form of rationales, image captions, scene graphs, etc to answer questions more accurately. Fuyu-8B is a multi-modal text and image transformer trained by Adept AI. 1 Introduction Visual question answering (VQA) [5] is a prominent vision-language task that finds a broad range of real-world applications, such as assisting blind individuals in understanding their. vic. g. Arguments are as follows:Prophet significantly outperforms all existing state-of-the-art methods on two challenging knowledge-based VQA datasets, OK-VQA and A-OKVQA, delivering 61. To fill the information gap and better leverage the reasoning capability, we design a framework that enables LLMs to proactively ask relevant questions to unveil more details in the image, along with filters. Retrieval Augmented Visual Question Answering. ,2022) typically lead to. However, in our analysis, we found that 41. For example, we outperform Flamingo \cite{Deepmind:Flamingo2022} by 5. To account for this disparity while still benefiting from the additional data, we include a. 🚀 Train. We propose the task of free-form and open-ended Visual Question Answering (VQA). 0 vs 56. LAVIS是一个用于LAnguage-and-VISion智能研究和应用的Python深度学习库。. LAVIS aims to serve as a one-stop comprehensive library that brings recent advancements in the language-vision field accessible for researchers and practitioners, as well as fertilizing future research and development. • GCP Vision APIを⽤いてOCRも実施し，学習に利⽤. 6% on A-OKVQA). Finally we address VQA as a text generation task with an effective encoder-decoder paradigm, which achieves state-of-the-art results on OKVQA datasets. Case study shows VLM trained our models provide accurate answers for challenging. In this paper we create a dataset with questions exclusively about detailed properties{"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":"LICENSE","path":"LICENSE","contentType":"file"},{"name":"README. Codes for VPGTrans: Transfer Visual Prompt Generator across LLMs. 2 Kosmos-2 - 80. g. Extensive experiments demonstrate the effectiveness of the proposed approach on the knowledge-based VQA task. Large-scale models, such as T5, GPT-3, PaLM, Flamingo and PaLI, have demonstrated the ability to store substantial amounts of knowledge when scaled to tens of billions of parameters and trained on large text and image datasets. 1% and 55. model (FLAN-T5) of a question in A-OKVQA dataset. You switched accounts on another tab or window. 2019) and A-OKVQA (Schwenk et al. A-OKVQA, COCO Caption, and OCR VQA datasets is considered inferior compared to LLaVA and Mini-GPT4. 2% vs 44. "Frozen scratch" does not load a pre-trained LM and is trained from scratch. Finally, we investigate PROMPTCAP’sView Slide. We observe that many visual questions, which contain deictic referential phrases referring to entities in the image, can be rewritten as "non-grounded". Paper ID: Paper Title: Authors: 8: Learning Uncoupled-Modulation CVAE for 3D Action-Conditioned Human Motion Synthesis: Chongyang Zhong (Institute of Computing. To achieve. We demonstrate PromptCap's effectiveness on an existing pipeline in which GPT-3 is prompted with image captions to carry out VQA. Abstract. We propose embodied language models to directly incorporate real-world continuous sensor modalities into language models and thereby establish the link. GitHub is where people build software. It has been shown that PLM-enhanced approaches (Gui et al. Different from generic captions, PromptCap takes a natural-language prompt to control the visual entities to describe in the generated caption. multimodal-dense-retriever-for-okvqa 2 RELATED WORK Multi-Modal Dense Passage Retrieval. READ FULL TEXT. In this paper, we propose LaKo, a knowledge-driven VQA method via Late Knowledge-to-text Injection. "Retrieval Augmented Visual Question Answering with. LAVIS是一个用于LAnguage-and-VISion智能研究和应用的Python深度学习库。. g. A-OKVQA. okvqa. . py and then follow the instruction on the prompts to view in browser. 6 CIDEr score vs previous best 113. Introduction Recent advances in deep learning have enabled substan-tial progress in visual question answering (VQA) which re-quires a machine to answer free-form questions by reason-ing about given images. A-OKVQA is composed of about 25K questions paired with both multiple choice (MC) answer options and ten free-form answers to allow for direct answer (DA) evaluation. No need to download if you want to train your own model Sample commands Training, and evaluating on the validation set with the small validation collection A-OKVQA is composed of about 25K questions paired with both multiple choice (MC) answer options and ten free-form answers to allow for direct answer (DA) evaluation. Visual Question Answering (VQA) is a task in computer vision that involves answering questions about an image. 8 - - 49. 1. To effectively incorporate an external KG, the proposed LaKo method transfers triples into textual format and proposes a late injection mechanism for knowledge fusion, which achieves state-of-the-art results on OKVQA datasets. We introduce various ways to retrieve knowledge using text and images and two reader styles: classification. Emu is a multimodal generalist that can seamlessly generate images and texts in multimodal context. READ FULL TEXTThis work introduces A-OKVQA, a crowdsourced dataset composed of a diverse set of about 25K questions requiring a broad base of commonsense and world knowledge to answer, and demonstrates the potential of this new dataset through a detailed analysis of its contents and baseline performance measurements over a variety of state. For example, the 2019 Outside Knowledge VQA dataset "OKVQA" extends VQA by adding more challenging questions that require complex, factual, and commonsense knowledge. To address this, we propose. sh for fine-tuning on image captioning. 9 vs 56. It has two tasks for video-and-language research: (1) Multilingual Video Captioning, aimed at describing a video in various languages with a compact unified captioning model, and (2) Video-guided Machine Translation, to. Visual Question Answering (VQA) in its ideal form lets us study reasoning in the joint space of vision and language and serves as a proxy for the AI task of scene understanding. Prophet significantly outperforms all existing state-of-the-art methods on two challenging knowledge-based VQA datasets, OK-VQA and A-OKVQA, delivering 61. Fig. This is the official repository of the Retrieval Augmented Visual Question Answering (RAVQA) project. For OKVQA, earlier attempts that incorporate a fixed knowledge retriever report results that are below 45%. Legacy BIOS can only boot MBR drives. Train and test sets, contains 6765 question-image pairs. 小部分需要外部知识的数据集，依赖于结构化知识（例如基于知识库增强的. VQA is a new dataset containing open-ended questions about images. • 上記に加えて，物体検出⽤のデータセットやVQA⽤の. 0 - - - Kosmos-1 - 67. - GitHub - VPGTrans/VPGTrans: Codes for VPGTrans: Transfer Visual Prompt Generator across LLMs. Model type: LLaVA-RLHF represents a novel aligned end-to-end trained large multimodal model that combines a CLIP vision encoder and Vicuna for general-purpose visual and language understanding, achieving impressive visual reasoning and perception capabilities mimicking spirits of the multimodal GPT-4. 3), while in contrast requiring no end-to-end training!The task of Outside Knowledge Visual Question Answering (OKVQA) requires an automatic system to answer natural language questions about pictures and images using external knowledge. distributed. We show that Cola can be applied to various VLMs (including large multimodal models like InstructBLIP) and 7 datasets (VQA v2, OK-VQA, A-OKVQA, e-SNLI-VE, VSR, CLEVR, GQA), and it consistently improves the performance. This paper surveys vision-language pre-training (VLP) methods for multimodal intelligence that have been developed in the last few years. The task of Outside Knowledge Visual Question Answering (OKVQA) requires an automatic system to answer natural language questions about pictures and images using external knowledge. 5. S3VQA. * fix optimizer zero_grad under amp * zero-shot gqa evaluation * Fix #119. This work introduces A-OKVQA, a crowdsourced dataset composed of a diverse set of about 25K questions requiring a broad base of commonsense and world knowledge to answer, and demonstrates the potential of this new dataset through a detailed analysis of its contents and baseline performance measurements over a variety of state. For example, the 2019 Outside Knowledge VQA dataset "OKVQA" extends VQA by adding more challenging questions that require complex, factual, and commonsense knowledge. A-OKVQA A-OKVQA is a successor of OKVQA with more challenging and diverse questions. json. OK-VQA and A-OKVQA, delivering 61. A-OKVQA: A Benchmark for Visual Question Answering Using World Knowledge 🌻dataset VQA ; OOD-CV: A Benchmark for Robustness to Out-of-Distribution Shifts of Individual Nuisances in Natural Images ; The Anatomy of Video Editing: A Dataset and Benchmark Suite for AI-Assisted Video Editing 🌻dataset 视频编辑 Also, many of the models are trained using only English, but there are thousands of languages ( 7000 languages estimated) and it is important that other languages are represented and included. json' and 'okvqa_ans_to_cap_dict. Introduction The field of Visual Question Answering (VQA) has made amazing strides in recent years,. If possible, fine-tune it on that dataset to compare the results. "Frozen finetuned" has the language model finetuned, while "Frozen" keeps LM frozen. [CVPR 2023] Pytorch Code of MixPHM: Redundancy-Aware Parameter-Efficient Tuning for Low-Resource Visual Question Answering - GitHub - jingjing12110/MixPHM: [CVPR 2023] Pytorch Code of MixPHM: Redundancy-Aware Parameter-Efficient Tuning for Low-Resource Visual Question AnsweringA generic and efficient pre-training strategy that easily harvests development of pretrained vision models and large language models (LLMs) for vision-language pretraining. To prompt GPT-3 with answer heuristics and generate better answers, run the following command: okvqa. Recent. 3 ), establishing new state-of-the-art on zero-shot captioning (on NoCaps 121. This library aims to provide engineers and researchers with a one-stop. 1. OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge Kenneth Marino, Mohammad Rastegari, Ali Farhadi, Roozbeh Mottaghi. MAGMA outperforms Frozen on open-ended generative tasks, achieving state of the art results on the OKVQA benchmark and competitive results on a range of other popular VL benchmarks, while pretraining on 0. The datasets folder contains all the datasets and features used in this project, and the assets folder contains the pre-computed resources and other intermediate files (you can use them to skip some early experiment steps and save time). We demonstrate PromptCap's effectiveness on an existing pipeline in which GPT-3 is prompted with image captions to carry out VQA. , S3 (select, substitute and search), and build a new data set and challenge around it. However, current systems mostly rely on separate models to predict answers and generate explanations, leading to less grounded and frequently inconsistent results. and. 8Vision-and-language reasoning requires an understanding of visual concepts, language semantics, and, most importantly, the alignment and relationships between these two modalities. KBVQA:文中没有引用. We group these approaches into three categories: () VLP for image-text tasks, such as image captioning, image-text retrieval,. txt -. 6% on VQAv2. Our code is publicly available at this. PromptCap outperforms generic captions by a large margin and achieves state-of-the-art accuracy on knowledge-based VQA tasks (60. * update runner - configurable beta. These experimental results demonstrate that our proposed dataset poses a new challenge towards current black-box VQA models and can push the boundary of visual OKVQA [38] is a recent dataset where the visual content of an. A-OKVQA Knowledge-based visual question answering benchmark. Search. : LAVIS (short for LAnguage-VISion) is an open-source deep learning library for language-vision research and applications, offering comprehensive support for a wide range of tasks, datasets, and state-of. What is LAVIS? LAVIS is a Python deep learning library for LAnguage-and-VISion research and applications. In contrast to the existing knowledge-based VQA datasets, the questions generally cannot be answered by simply querying a knowledge base, and instead require some form of commonsense. [17] A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge [18] Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering [19] ViQuAE: a dataset for knowledge-based visual question answering about named entities [20] CLEVR: A diagnostic dataset for compositional language and. English | 简体中文 | 繁體中文 | 한국어 | Español | 日本語 | हिन्दी | Русский | Рortuguês | తెలుగు | . Knowledge-based visual question answering is a very challenging and widely concerned task. looking forward to the training and finetuning codeWe achieve state-of-the-art results on a wide range of vision-language tasks, such as image-text retrieval (+2. md","path":"README. , GPT-3) as an implicit. The Victorian Registration and Qualifications Authority (VRQA) is the official regulator of education and training providers and qualifications in Victoria. On the challenging A-OKVQA dataset, our method even outperforms few-shot methods by as much as 20%. 4 questions on average) per image. For OK-VQA we use dynamic qrels*/ /**IMPORTANT: The following parameters are only used for OKVQA**/ --ann_file /*Address to Annotation file in OK-VQA dataset for dynamic eval*/ --ques_file /*Address to Question file in OK-VQA dataset for dynamic eval*/ --passage_id_to_line_id_file /*Address to maping between passage id and line id in. The total model parameters are 17 billion (language. {"payload":{"allShortcutsEnabled":false,"fileTree":{"eval_mm":{"items":[{"name":"mmbench","path":"eval_mm/mmbench","contentType":"directory"},{"name":"mme","path. github","contentType":"directory"},{"name":"app","path":"app","contentType. Introduced by Schwenk et al. PDF Abstract CVPR 2023 PDF CVPR 2023 Abstract An Empirical Study of GPT-3 for Few-Shot Knowledge-Based VQA Zhengyuan Yang, Zhe Gan, Jianfeng Wang, Xiaowei Hu, Yumao Lu, Zicheng Liu, Lijuan Wang A-OKVQA: A Benchmark for Visual Question Answering Using World Knowledge 🌻dataset VQA ; OOD-CV: A Benchmark for Robustness to Out-of-Distribution Shifts of Individual Nuisances in Natural Images ; The Anatomy of Video Editing: A Dataset and Benchmark Suite for AI-Assisted Video Editing 🌻dataset 视频编辑 A-OKVQA [33] is an innovative benchmark for knowledge-aware visual question answering with 25K questions that demand a high-level comprehension of commonsense and world knowledge. Architecturally, Fuyu is a vanilla decoder-only transformer - there is no image encoder. We experimented with the older engine davinci instead of the current default text-davinci-001 that is boosted for instruction. In contrast to existing knowledge-based VQA datasets, the questions generally cannot be answered by simply querying a knowledge base, and instead require some form of commonsense. These models achieve state-of-the-art results on downstream tasks. The result on OKVQA by Flamingo (with “*”) is obtained in a 32-shot learning setup. Some example questions and their corresponding images and answers have been shown. However, enabling general inference in the real world, e. yaml","path":"lavis/projects/blip2/eval. json │ ├── testdev_balanced_questions. The path of the model trained previously (step2 OKVQA). 0 dataset: train2015. Resources and Tools ; Benchmarks: see Benchmark for instructions to evaluate and train supported models. Zero-shot results on WebQA show. Then you can run the shell in folder VL_captioning to reproduce results, e. 4 结果 结果显示，架构更加简单的LLaVA-1. On the challenging A-OKVQA dataset, our method outperforms few-shot methods by as much as 20%. 3) It achieves comparable or better performance than methods relying on end-to-end training. 6\% on VQAv2. 5 51. Our method continuously boosts the performance of baselines methods by an average gain of 2. In “ AVIS: Autonomous Visual Information Seeking with Large Language Models ”, we introduce a novel method that achieves state-of-the-art results on visual information seeking tasks. Furthermore, through a detailed analysis, we explain which questions benefit, and which don't, from contextualized commonsense knowledge from COMET. ,2021) is an augmented ver-sion of OKVQA, improving both the quantity and quality of some question types. Here, A-OKVQA was converted to a multiple-choice task and the following format was used for the prompt: Answer with the option’s letter from the given choices directly. First download all OK-VQA files. 2) It flexibly interfaces with a wide range of LLMs to perform VQA. In our experiments, UMAE models surpass the prior SOTA answer accuracy on A-OKVQA by 10~15%, show competitive results on OK-VQA, achieve new SOTA explanation scores on A-OKVQA and VCR, and. 70% (small model) and 70. For example, we outperform Flamingo \cite{Deepmind:Flamingo2022} by 5. python -u -m torch. Links: [Leaderboard] Abstract. 8 3) It achieves comparable or better performance than methods relying on end-to-end training. 2 % of the number of samples used to train SimVLM. 0 45. 7% accuracies on their testing sets, respectively. Only 18% of questions in A-OKVQA require answers from an external knowledge base. 9 67. We introduce A-OKVQA, a crowdsourced dataset composed of a diverse set of about 25K questions requiring a broad base of commonsense and world knowledge to. bin file generated: from_pretrained: same pre-trained Bert model (OK-VQA) as step2: task: task = 42 OKVQA is usedstate-of-the-art OKVQA systems, we are surprised to find existing OKVQA models yield close to 0 evaluation score on S3VQA. 2) It renders end-to-end training unnecessary and significantly reduces the cost of deploying LLM for VQA tasks. , 2022) is a multi-hop reasoning dataset that requires a system to aggregate multiple sources to answer a question, where the answers can be found ei-ther via image search or general web search. Recently a series of works utilize large language models (e. In our experiments, UMAE models surpass the prior state-of-the-art answer accuracy on A-OKVQA by 10 15%, show competitive results on OK-VQA, achieve new state-of-the-art explanation scores on A-OKVQA and VCR, and demonstrate promising out-of-domain performance on VQA-X. 大部分的VQA任务不需要外部知识，仅仅局限于：简单计数，视觉属性判断（如颜色），物体检测任务。. General enquiries . g. To effectively incorporate an external KG, we transfer triples into textual format and propose a late injection mechanism for knowledge fusion. main. Against the formidable image-understanding datasets like VQAv2, OKVQA, COCO Captions, and AI2D, Fuyu-8B didn’t just survive; it thrived, challenging even the behemoths with more parameters!This work identifies a key structural idiom in OKVQA ,viz. Performance of different versions of Frozen on (left) VQAv2 and (right) OKVQA, trained on Conceptual Captions. Co-authors. Follow the below link to access the challenge : 3) It achieves comparable or better performance than methods relying on end-to-end training. Our language guidance improves the performance of CLIP by 7. Then download the 2014_coco val anotation file in link, and put it in annotation_new folder. Knowledge-based visual question answering (VQA) requires external knowledge beyond the image to answer the question. It features a unified interface to easily access state-of-the-art image-language, video-language models and common datasets. , natural language answer) for the VQA type query by first reformulating the input question (using Select and Substitute) and then retrieving external knowledge (using Search). Mirroring real-world scenarios, such as helping the visually impaired, both the questions and answers are open-ended. 5只需要120万公开数据，即可超越用了14. Image Captioning Visual Question Answering COCO NoCaps TextCaps VQAv2 TextVQA VizWiz-QA OKVQA GIT2 145. By defining new functions in ModuleParser, e. The current state-of-the-art on A-OKVQA is Prophet. in A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge. 它有一个统一的界面设计. In this paper, we address the task of knowledge-based visual question answering and provide a benchmark, called OK-VQA, where the image content is not sufficient to answer the questions, encouraging methods that rely on external knowledge resources. LAVIS is a Python deep learning library for LAnguage-and-VISion intelligence research and applications. Model type: BLIVA is an open-source Vision-Languagde model trained by initializing from InstructBLIP and alignment with Vicuna on multimodal instruction-finetuning data. json: map passages ids to line ids in all_blocks. 6\% on VQAv2. It has 17K/1K/6K questions for train/val/test. In this release, we use LLaVA at [email protected]) 55. initializing a BertForSequenceClassification model from a BertForPreTraining model). sh --task ok --version okvqa_pretrain_1 --gpu 0. py inside the above 'meta data' folder. 6% on A-OKVQA). {"payload":{"allShortcutsEnabled":false,"fileTree":{"vigc/projects/instruct_blip_vicuna7b/generate_qa/a-okvqa":{"items":[{"name":"generate_answer. M3IT-80 is the translated version of M3IT, an open-source, large-scale Multi-modal, Multilingual Instruction Tuning dataset, designed to enable the development of general-purpose multi-modal agents. , Section 5), a neural OKVQA system that targets this class of queries and reasoning structure. 3 An interpretable OKVQA system Continuinginthespiritof“smallstepsbeforegiantleap”,wepresent S3 (c. 2 Table 2. MLLM-DataEngine, a novel closed-loop system that bridges data generation, model training, and evaluation. 4. self. We propose a method to generate, select, and encode external commonsense knowledge alongside visual and textual cues in a new pre-trained Vision-Language-Commonsense transformer model, VLC-BERT. github","contentType":"directory"},{"name":"app","path":"app","contentType. json" containing your results in the correct format and submit the ". 3 61. WebQA (Chang et al. ---, 视频播放量 250047、弹幕量 1596、点赞数 4915、投硬币枚数 104、收藏人数 1385、转发人数 563, 视频作者 PinkGentleman, 作者简介 空你几哇～我是杂食向日娱UP主、在日华人。在2010年前后入的日饭圈，于2012年夏旅居日本。请大家多多支持～非常感谢！，相关视频：2023年日本女性声优人气排行榜top 10，2020. . On the challenging A-OKVQA dataset, our method even outperforms few-shot methods by as much as 20%. We propose Unified-IO, a model that performs a large variety of AI tasks spanning classical computer vision tasks, including pose estimation, object detection, depth estimation and image generation, vision-and-language tasks such as region captioning and referring expression, to natural language processing tasks such as question answering. which achieves state-of-the-art results on OKVQA datasets. In the evaluation with. 0 - - - 29. Factually Augmented RLHF effectively utilizes existing human annotations to improve. MBR, they are entirely 2 different comparisons. py. 6% and BLIP-2 by 4. PROMPTCAP outperforms generic captions by a large margin and achieves state-of-the-art accuracy on knowledge-based VQA tasks (60. Finetuning details are available in C. A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge. 0 is a dataset containing open-ended questions about images. yaml","path":"minigpt4/configs/datasets/cc_sbu/align. GPT-3) as implicit knowledge sources, which achieve much better performance with the. This IS expected if you are initializing LxmertModel from the checkpoint of a model trained on another task or with another architecture (e. A surprisingly large fraction of queries do not assess the ability to integrate cross-modal information. The visual retriever aims to retrieve relevant knowledge, and the visual reader seeks to predict answers based on given knowledge. Code is available via the LAVIS [28] frameworkBeside the performance gain, Cola is also more robust to the VLMs' errors. Specifically, on the challenging A-OKVQA dataset, LAMOC outperforms several competitive zero-shot methods and even achieves comparable results to a fine-tuned VLP model. Knowledge-based visual question answering (VQA) requires external knowledge beyond the image to answer the question. There are also other advantages to booting in UEFI mode v. The standard splits uses 6,513 clips for training, 497 clips for validation, and 2,990 clips. pip install open-flamingo [training] pip install open-flamingo [eval] pip install. OKVQA [11] X VCR [12] X X Our KRVQR X X X X knowledge triplets prediction, the current state-of-the-art VQA models still achieve low answering accuracy on our proposed KRVQR dataset. LAVIS简介. You need to enable JavaScript to run this app. First, download the. 5亿训练数据的Qwen-VL和1. If you're using VIGC in your research or applications, please cite using this BibTeX: Prophet significantly outperforms all existing state-of-the-art methods on two challenging knowledge-based VQA datasets, OK-VQA and A-OKVQA, delivering 61. 4% of the dataset needed to be corrected and 10. CCS CONCEPTS •Computingmethodologies→Artificialintelligence;Knowl-edge representation and reasoning; Semantic networks. KEYWORDS Visual Question Answering; Knowledge Graph; Knowledge-to-Text; Late Knowledge Injection ACM Reference Format:In response, we identify a key structural idiom in OKVQA ,viz. VATEX is multilingual, large, linguistically complex, and diverse dataset in terms of both video and natural language descriptions. Train and test sets, contains 6765 question-image pairs. e. PromptCap outperforms generic captions by a large margin and achieves state-of-the-art accuracy on knowledge-based VQA tasks (60. Jan 2023, LAVIS is now available on PyPI for installation! A plug-and-play module that enables off-the-shelf use of Large Language Models (LLMs) for visual question answering (VQA). 3% on A-OKVQA, and 9. {"payload":{"allShortcutsEnabled":false,"fileTree":{"vigc/configs/datasets/a-okvqa/vig":{"items":[{"name":"train. 3 Datasets This paper used three publicly available datasets in the training and evaluation experiments, including VQAv2, OKVQA, and VizWiz datasets,whose basic information can be found in Table 2 . 4% on OK-VQA and 59. Run time and cost. Annotators were provided the audio tracks together with category hints (and with additional video hints. Fangas initialization of word embeddings. in AudioCaps: Generating Captions for Audios in The Wild. 1% and 55. A-OKVQA has shifted its core task to reasoning questions . 1 testing sets, respectively. bash run_okvqa_full. Official repository for A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge. 它有一个统一的界面设计. Knowledge-based visual question answering (VQA) requires external knowledge beyond the image to answer the question. For example, you can download 'okvqa_question. There are about 29,000 unique words in all captions. The task of Outside Knowledge Visual Question Answering (OKVQA) requires an automatic system to answer natural language questions about pictures and images using external knowledge. 3 50. Early studies retrieve required knowledge from explicit knowledge bases (KBs), which often introduces irrelevant information to the question, hence restricting the performance of their models. conda env create -f environment. 2. 3) It achieves comparable or better performance than methods relying on end-to-end training. Underspecification in VL tasks like VQA can manifest in several ways, leading to incorrect model predictions. Visual. Zero-shot results on WebQA show. Manually filtered to ensure all questions require outside knowledge (e. To strike a balance between performance and efficiency, we choose to use K= 100 for all. The Visual Question Answering (VQA) task aspires to provide a meaningful testbed for the development of AI models that can jointly reason over visual and natural language inputs. g. MAGMA - a simple method for augmenting generative language models with additional modalities using adapter-based finetuning and outperforms Frozen on open-ended generative tasks, achieving state of the art results on the OKVQA benchmark and competitive results on a range of other popular VL benchmarks. It contains about 2M samples from VQA, Detector, Detailed Description of Image, and others. Additionally, we find that using gold answers for oracle question candidate selection achieves a substantial gain in VQA accuracy by up to 14. Edit social preview. Mia Qiao et al. 4 57. Our method integrates LLMs with three types of tools: (i) computer vision tools for extracting visual information from images, (ii) a web search tool for. BLIP-2 framework with the two stage pre-training strategy. Large pre-trained vision and language models have demonstrated remarkable capacities for various tasks. OK-VQA and A-OKVQA, delivering 61. We introduce A-OKVQA, a crowdsourced dataset composed of a diverse set of about 25K questions requiring a broad base of commonsense and world knowledge to answer. 1. OK-VQA and A-OKVQA, delivering 61. treat OKVQA as a task of fusing structured data from the image with the unstructured text rather than a visual recog-nition problem. PDF Abstractquestion-answering task of the A-OKVQA, Science-QA, VSR, and IconQA datasets using CLIP and BLIP models. 9 32. VQA [37] and A-OKVQA [46] mostly require common-sense knowledge. The idea is to transform the multi-modal input (image + text) to a text-only input so that the text-based QA model can directly interpret and answer (Figure 1 shows a sample). It composes of an EVA-CLIP vision encoder, a Q-Former, a projection layer and an auto-regressive language model, based on the decoder only transformer architecture. 8 145. We use variants to distinguish between results evaluated on slightly different versions of the same dataset. Finally, the two types of answer heuristics are encoded into the prompts to enable GPT-3 to better comprehend the task thus enhancing its capacity. Our model consists of three components: mutual modulation, knowledge-based key–value memory network and knowledge-based representation learning. 10 ground truth answers per question. 8 Flamingo-80B - 67. yaml","path":"vigc/configs/datasets/a-okvqa/vqg/train. 85% (absolute) increase in zero-shot performance on VQAv2 and a 6. PromptCap outperforms generic captions by a large margin and achieves state-of-the-art accuracy on knowledge-based VQA tasks (60. github","contentType":"directory"},{"name":"app","path":"app","contentType. As shown in Figure[4] the Q-Former consists of two transformer submodules sharing the same self-attention layers. 2 56. okvqa. However, in our analysis, we found that 41. I'd like to implement my own dataset, I tried to do that using the tutorial of adding dataset in the documentation but I always end up with something unclear. We observe that many visual questions, which contain deictic referential phrases referring to entities in the image, can be rewritten as "non-grounded" questions. @inproceedings{subramanian-etal-2023-modular, title = "Modular Visual Question Answering via Code Generation", author = "Subramanian, Sanjay and Narasimhan, Medhini and Khangaonkar, Kushal and Yang, Kevin and Nagrani, Arsha and Schmid, Cordelia and Zeng, Andy and Darrell, Trevor and Klein, Dan", booktitle =. In this paper, we propose a new Semi-Supervised VQA-NLE via Self-Critical Learning (S3C), which evaluates the candidate explanations by answering rewards to improve the logical consistency between answers and rationales. 1 54. Through our evaluation on the knowledge-intensive OK-VQA and A-OKVQA datasets, we show that VLC-BERT is capable of outperforming existing models that utilize static knowledge bases. {"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":". 8 44. See our slides for details. Experimental results on the OKVQA dataset show that the proposed approach achieves an improvement of 1:71% over the baseline system and 1:88% over the best-reported previous system. 265,016 images (COCO and abstract scenes) At least 3 questions (5. In addition, some questions (18%) in A-OKVQA do require knowledge of detailed properties, but about basic-level categories. Summary.