7% accuracies on their testing sets, respectively. 3 An interpretable OKVQA system Continuinginthespiritof“smallstepsbeforegiantleap”,wepresent S3 (c. g. We use variants to distinguish between results evaluated on slightly different versions of the same dataset. md","path":"Datasets/OKVQA/Readme. json and candidates_okvqa. 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. Introduced by Schwenk et al. 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. - GitHub - VPGTrans/VPGTrans: Codes for VPGTrans: Transfer Visual Prompt Generator across LLMs. We demonstrate PromptCap's effectiveness on an existing pipeline in which GPT-3 is prompted with image captions to carry out VQA. Visual. 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. The hyperparameter settings match the NeuCRaB experiments. To install training or eval dependencies, run one of the first two commands. 9 82. txt. Prepare the data The cached files for converted OKVQA data, predicted text representations, and similarity features are in the coco_annotations, input_text, and coco_clip_new folders, respectively. Visual Question Answering (VQA) 682 papers with code • 59 benchmarks • 106 datasets. and. 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. To Launch a demo locally, you should: Download the pretrain weight and finetune weight of minigpt-4 and instructblip to local; Update MODEL_CKPT in line 9 of vigc_demo. It features a unified interface to easily access state-of-the-art image-language, video-language models and common datasets. Abstract. 2% vs 44. Figure 2: Dataset examples. Large-scale pretraining. 1. in OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge Outside Knowledge Visual Question Answering (OK-VQA) includes more than 14,000 questions that require external knowledge to answer. 41% point increase on A-OKVQA. “Easy to use AI that explains images” is published by MLBoy. Finetuning details are available in C. g. To effectively incorporate an external KG, we transfer triples into textual format and propose a late injection mechanism for knowledge fusion. We propose the task of free-form and open-ended Visual Question Answering (VQA). 预训练MCAN模型和在okvqa上微调是一起的吗？应该先预训练MCAN，再去微调。 但是，上面的脚本，task是ok，是不是MCAN已经预训练结束了，然后在okvqa上进行微调？还是，预训练和微调放在一起执行呢？ OKVQA S3. 它有一个统一的界面设计. Finally, we investigate PROMPTCAP’sVQAv2 OKVQA GQA SciQA-Img (0-shot) VizWiz (0-shot) Generalist Models Flamingo-9B - 61. 6% on VQAv2. 2) It flexibly interfaces with a wide range of LLMs to perform VQA. okvqa_train_corpus: the corpus is collected based on the training data. captioning, feature extraction, VQA, GradCam, zeros-shot classification. Before you begin, it is recommended that you setup SBERT in a new conda environment. This repo was made by Remi Cadene (LIP6) and Hedi Ben-Younes (LIP6-Heuritech), two PhD Students working on VQA at UPMC-LIP6 and their professors Matthieu Cord (LIP6) and Nicolas Thome (LIP6-CNAM). To address this, we propose. To address this, we propose a multitask learning approach towards a Unified Model for Answer. 8 3) It achieves comparable or better performance than methods relying on end-to-end training. 1% and 55. As shown in Figure[4] the Q-Former consists of two transformer submodules sharing the same self-attention layers. 3% on A-OKVQA, and 9. The vocabulary of the VQAv2 dataset is 3129, the vocabulary of the OKVQA dataset is 5117, and the vocabulary of the VizWiz dataset is 6285. Additionally, we find that using gold answers for oracle question candidate selection achieves a substantial gain in VQA accuracy by up to 14. Annotators were provided the audio tracks together with category hints (and with additional video hints. {"payload":{"allShortcutsEnabled":false,"fileTree":{"vigc/configs/datasets/a-okvqa/vic":{"items":[{"name":"train. Our new dataset includes more than 14,000 questions that require external knowledge to answer. 7% accuracies on their testing sets, respectively. The total model parameters are 17. The Victorian Registration and Qualifications Authority (VRQA) is the official regulator of education and training providers and qualifications in Victoria. github","path":". A-OKVQA is crowdsourced visual question answering dataset composed of a diverse set of about 25K questions requiring a broad base of commonsense and world knowledge to answer. 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. 0 vs 56. [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. py","path":"okvqa/function/__init__. md","path":"README. in AudioCaps: Generating Captions for Audios in The Wild. md. Visual Question Answering (VQA) v2. For example, we outperform Flamingo \cite{Deepmind:Flamingo2022} by 5. Given an image and a natural language question about the image, the task is to provide an accurate natural language answer. 传统的VQA数据集作者分为两大类：是否需要外部知识进行支持（ knowledge-based ）. {"payload":{"allShortcutsEnabled":false,"fileTree":{"projects/krisp/configs/krisp/vqa2":{"items":[{"name":"krisp_pretrain. In this paper, we propose a novel knowledge memory embedding model with mutual modulation, named KM 4, to address the challenges of visual reasoning. Paper and Citing VIGC. Contributions. sh --task ok --version okvqa_pretrain_1 --gpu 0. json" containing your results in the correct format and submit the ". 7. To prompt GPT-3 with answer heuristics and generate better answers, run the following command: okvqa. 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. For example, we outperform Flamingo <cit. However, the popular data set has serious limitations. To submit your method to the leaderboard, contact okvqa. : 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. okvqa. Finally, 3% of the questions require knowledge about physics. 6% and BLIP-2 by 4. 6 Web-Image-Text (1. Introduction The field of Visual Question Answering (VQA) has made amazing strides in recent years,. g. 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. Dense Passage Retrieval. BLIP-2 beats Flamingo on zero-shot VQAv2 ( 65. 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. 14974-14983. The latest such methods simultaneously introduce LLM-based code generation to build programs and a number of. Recent. Multiple-choice VQA: A-OKVQA: Choose the correct option for the following question: question: For now, the visual instruction tuning data are formatted in the training format of LLaVA in data folder. Running. Run time and cost. state-of-the-art OKVQA systems, we are surprised to find existing OKVQA models yield close to 0 evaluation score on S3VQA. {"payload":{"allShortcutsEnabled":false,"fileTree":{"okvqa":{"items":[{"name":"data","path":"okvqa/data","contentType":"directory"},{"name":"function","path":"okvqa. 70% (small model) and 70. Jupyter Notebook Examples . yaml","path":"vigc. RLHF further enhances human alignment, reduces hallucination, and encourages truthfulness based on evaluations. 6 CC12M (12M) 53. The modifiers are added based on the original question, the original image, and data generated from the image and question like captions and rationales. data: train/val/test split and a small validation collection. PDF Abstractquestion-answering task of the A-OKVQA, Science-QA, VSR, and IconQA datasets using CLIP and BLIP models. Figure 3. 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. A-OKVQA, COCO Caption, and OCR VQA datasets is considered inferior compared to LLaVA and Mini-GPT4. A generic and efficient pre-training strategy that easily harvests development of pretrained vision models and large language models (LLMs) for vision-language pretraining. It is trained on a large multimodal dataset (e. We leverage semantic representations of both the scenes and questions to mitigate language. OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge Kenneth Marino, Mohammad Rastegari, Ali Farhadi, Roozbeh Mottaghi. The MC component of the dataset bypasses many dificulties inherent in direct answer evaluation and allows for a simple, clean accuracy score. Minor improvements. 1. 1, the winning entry from Facebook AI Research (FAIR)'s A-STAR team to the VQA Challenge 2018. In this paper, we. A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge. We thus propose the LXMERT (Learning Cross-Modality Encoder Representations from Transformers) framework to learn these vision-and-language. Large language models excel at a wide range of complex tasks. g. 🚀 Train. VQAv2 and OKVQA are natural image question-answering datasets, COCO is a captioning dataset, and AI2D is a multiple-choice dataset involving scientific diagrams. See examples for more inference examples, e. Knowledge-based visual question answering (VQA) requires external knowledge beyond the image to answer the question. 1 Introduction Large-scale language models (LLMs) have exhib-ited impressive capabilities in terms of their world${MINIGPTv2_EVALUATION_DATASET} ├── gqa │ └── test_balanced_questions. In. • 上記に加えて，物体検出⽤のデータセットやVQA⽤の. 8% in the challenging A-OKVQA dataset. By using the commonly used bottom-up-attention visual features, a single MCAN model delivers 70. We observe that many visual questions, which contain deictic referential phrases referring to entities in the image, can be rewritten as "non-grounded". OK-VQA and A-OKVQA, delivering 61. 93% (large model) overall accuracy on the test-dev split of. A major step in developing OKVQA systems is to retrieve relevant documents for the given multimodal query. 4% on OK-VQA and 59. {"payload":{"allShortcutsEnabled":false,"fileTree":{"misc":{"items":[{"name":"framework. 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. 6 - - 31. A surprisingly large fraction of queries do not assess the ability to integrate cross-modal information. For example, the 2019 Outside Knowledge VQA dataset "OKVQA" extends VQA by adding more challenging questions that require complex, factual, and commonsense knowledge. The current state-of-the-art on A-OKVQA is Prophet. OK-VQA (Outside Knowledge Visual Question Answering) Introduced by Marino et al. python -u -m torch. Thanks. Projects. 1 54. We select the checkpoint at step 65'000 for IDEFICS-9B and at step 37'500 for IDEFICS. Performance of different versions of Frozen on (left) VQAv2 and (right) OKVQA, trained on Conceptual Captions. A-OKVQA: Choose the correct option for the following question: question: Prerequisites Models. 5. 4 57. The goal of VQA is to teach machines to understand the content of an image and answer questions about it in natural language. Please save the files to the appropriate locations. 3. from A-OKVQA (left) and VQAv2 (right) datasets along with REPARE outputs. 5亿训练数据的Qwen-VL和1. All code has been uploaded, but I'm still working on the documentation. {"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":"LICENSE","path":"LICENSE","contentType":"file"},{"name":"README. Img2Prompt-VQA surpasses Flamingo on zero-shot VQA on VQAv2 (61. 2 SimVLM. Different from generic captions, PromptCap takes a natural-language prompt to control the visual entities to describe in the generated caption. png","path":"misc/framework. Instead, some are. 1 - Flamingo 138. Modular vision-language models (Vision-LLMs) align pretrained image encoders with frozen large language models (LLMs), representing a computationally much more efficient alternative to end-to-end training of large vision-language models from scratch, which is prohibitively expensive for most. Knowledge-based visual question answering is a very challenging and widely concerned task. A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge Dustin Schwenk, Apoorv Khandelwal, Christopher Clark, Kenneth Marino, Roozbeh Mottaghi In EMNLP 2021 [project page] Webly Supervised Concept Expansion for General Purpose Vision Models. . 0 81. A-OKVQA, COCO Caption, and OCR VQA datasets is considered inferior compared to LLaV A and. Saved searches Use saved searches to filter your results more quickly We introduce the Multi-Modal, Multilingual Instruction Tuning (M3IT) dataset, comprises carefully curated datasets, including 2. Building SBERT annotations: . We group these approaches into three categories: () VLP for image-text tasks, such as image captioning, image-text retrieval,. ,2022;Lin et al. Specifically, we advance the big convergence from three aspects: backbone. Our language guidance improves the performance of CLIP by. PromptCap outperforms generic captions by a large margin and achieves state-of-the-art accuracy on knowledge-based VQA tasks (60. 8 145. Some example questions and their corresponding images and answers have been shown. With a semi-supervised learning. @inproceedings{wang-etal-2021-li, title = "利用图像描述与知识图谱增强表示的视觉问答(Exploiting Image Captions and External Knowledge as Representation Enhancement for Visual Question Answering)", author = "Wang, Gechao and Zhu, Muhua and Xu, Chen and Zhang, Yan and Wang, Huizhen and Zhu, Jingbo", editor = "Li, Sheng and Sun,. 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. You switched accounts on another tab or window. main. Prophet significantly outperforms all existing state-of-the-art methods on two challenging knowledge-based VQA datasets, OK-VQA and A-OKVQA, delivering 61. The benchmarks section lists all benchmarks using a given dataset or any of its variants. Image Captioning Visual Question Answering COCO NoCaps TextCaps VQAv2 TextVQA VizWiz-QA OKVQA GIT2 145. 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. 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. To account for this disparity while still benefiting from the additional data, we include a random sample of 5000 image-text pairs from the A-OKVQA dataset and 512 image-text pairs each from the COCO Caption and OCR VQA datasets in the training. 2RelatedWork Visual Question Answering. 2. Then download the 2014_coco val anotation file in link, and put it in annotation_new folder. 8% in CIDEr), and VQA (+1. We observe that many visual questions, which contain deictic referential phrases referring to entities in the image, can be rewritten as "non-grounded" questions. This implementation is based on python3. Modular neural networks without additional training have recently been shown to surpass end-to-end neural networks on challenging vision-language tasks. The models are evaluated with in-context few-shot learning, where the priming instances are selected. 3) It achieves comparable or better performance than methods relying on end-to-end training. e. READ FULL TEXT. {"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":"PythonEvaluationTools","path":"PythonEvaluationTools","contentType":"directory"},{"name. You will need to create a JSON file with the name "output. VQA Questions about images that require an understanding of vision, language and. Our method continuously boosts the performance of baselines methods by an average gain of 2. The visual retriever aims to retrieve relevant knowledge, and the visual reader seeks to predict answers based on given knowledge. Official repository for A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge. 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. Focusing on two visual question answering tasks, we show that RepARe can result in a 3. Paper ID Paper Title Authors : 8 : Learning Uncoupled-Modulation CVAE for 3D Action-Conditioned Human Motion Synthesis : Chongyang Zhong. 4 结果 结果显示，架构更加简单的LLaVA-1. In the evaluation with. yaml","path":"lavis/projects/blip2/eval. For example, we outperform Flamingo \cite{Deepmind:Flamingo2022} by 5. g. 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. 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). 2) It renders end-to-end training unnecessary and significantly reduces the cost of deploying LLM for VQA tasks. It features a unified interface to easily access state-of-the-art image-language, video-language models and common datasets. Recent works have sought to use a large. py","contentType":"file"},{"name. 观察分析可知，MUTAN和BAN这类专门用于学习图像和问题之间的高级关联的VQA模型也在OK-VQA数据集上得到了远低于VQA数据集上的结果，表明OK-VQA不能简单地由一个聪明的模型来解决，而实际上需要结合图像之外信息的方法。. 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. On the challenging A-OKVQA dataset, our method even outperforms few-shot methods by as much as 20%. Edit social preview. 1% and 55. Statistics of our instructions: Statistics of our dataset grouped by task: Model Evaluation. initializing a BertForSequenceClassification model from a BertForPreTraining model). We demonstrate PROMPTCAP's effectiveness on an existing pipeline in which GPT-3 is prompted with image captions to carry out VQA. ∙various PLMs. Themulti-modalitycanbeinthequeries, with a corpus of uni-modal documents, which enables the under-In contrast to data_source. 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. e. Only 18% of questions in A-OKVQA require answers from an external knowledge base. 6 65. BIOS mode,. json' and 'okvqa_ans_to_cap_dict. 3 61. This document describes Pythia v0. The visual retriever aims to retrieve relevant knowledge, and the visual reader seeks to predict answers based on given knowledge. . You signed out in another tab or window. 0 - 77. A Good Prompt Is Worth Millions of Parameters: Low-resource Prompt-based Learning for Vision-Language Models Installation Datasets Pre-trained checkpoints Pre-training Zero/few-shot Learning VQA OKVQA GQA Flickr30k Nocaps Moreover, we propose a Visual Retriever-Reader pipeline to approach knowledge-based VQA. Image Captioning Passage Retrieval Question Answering Retrieval Visual Question Answering Visual Question Answering (VQA) Datasets. 1 51. 6\% on VQAv2. NExT-QA Video question answering (VideoQA) benchmark to advance video understanding from describing to explaining the temporal actions. . Multimodal IR, spanning text corpus, knowledge graph and images, called outside knowledge visual question answering (OKVQA), is of much recent interest. ,2017) collects. No milestone. 表1における「4 +OKVQA/OCR」に示している通り、InstructBLIPが使用するデータセットのサブセットのみでLLaVAは3つのタスク全てにおいてInstructBLIPを上回っており、LLaVAの設計が効果的なものであることを示唆しています。We demonstrate PromptCap's effectiveness on an existing pipeline in which GPT-3 is prompted with image captions to carry out VQA. task dataset model metric name metric value global rank removeTo sanity-check the architectural changes underlying Fuyu-8B, we chose four of the most commonly-used image-understanding datasets: VQAv2, OKVQA, COCO Captions, and AI2D. 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. We benchmark our method on the multi-choice question-answering task of the A-OKVQA, Science-QA, VSR, and IconQA datasets using CLIP and BLIP models. 2 Kosmos-2 - 80. For now we use LLaVA-LLaMA-2-7B as the fixed model. Specifically, we used OKVQA (Marino et al. What you were trying to do is to call a class object within the module object that happens to have the same name as the module that contains it. json. We design a new dataset, GQA, to address these shortcomings, featuring compositional questions over real-world images. {"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":". These experimental results demonstrate that our proposed dataset poses a new challenge towards current black-box VQA models and can push the boundary of visualpip install open-flamingo. formance on VQA-X [13] and A-OKVQA [49] benchmark datasets. We show that the use of language guidance is a simple but powerful and effective strategy for visual question an-swering. pytorch multimodal-learning visual-question-answering gpt-3 prompt-engineering okvqa a-okvqa. LAVIS is a Python deep learning library for LAnguage-and-VISion intelligence research and applications. Note: Code release is in progress. 我们在三个基于外部知识的数据集上做了相关实验：FVQA,Visual7w+KB,OKVQA。FVQA前面已经介绍过了，包括2190张图像，5286个问题，193449条知识。Visual7w+KB是通过模板在Visual7w的基础上自动生成的需要使用conceptnet知识的数据集，包含8425张图像，16850个问题。To address this challenge, we propose PromptCap (Prompt-guided image Captioning), a captioning model designed to serve as a better connector between images and black-box LMs. Hi, eval_okvqa_zeroshot_flant5xl. 6% needed to be removed. Co-authors. {"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":"PythonEvaluationTools","path":"PythonEvaluationTools","contentType":"directory"},{"name. See our slides for details. DoubleSsh commented on Mar 21. , S3 (select, substitute and search), and build a new data set and challenge around it. 6 InstructBLIP(Vicuna-13B) 121. 2022) datasets, as utilized in InstructBLIP (Dai et al. 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. To start training, you need to apply for and download the LLaMA-2-7B-chat-hf checkpoints here and download the LLaVA pretrained. pip install open-flamingo. These datasets, necessitating. 1 WIT w/o L contra 47. 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. 0 81. Before running the code, prepare two folders: datasets and assets. 3 70. 3 ), establishing new state-of-the-art on zero-shot captioning (on NoCaps 121. OK-VQA [36]. ,2019) and its augmented versions S3VQA (Jain et al. 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. 3% on A-OKVQA, and 9. ing A-OKVQA dataset, our method outperforms few-shot methods by as much as 20%. There are about 29,000 unique words in all captions. Then download the collecton file (all_blocks. Our data is based on the OK-VQA dataset. However, the popular data set has serious limitations. github","contentType":"directory"},{"name":"app","path":"app","contentType. This library aims to provide engineers and researchers with a one-stop solution to rapidly develop models for their specific multimodal scenarios, and benchmark them across standard and customized datasets. 7% accuracies on their testing sets, respectively. Multimodal C4) and can be used to generate text conditioned on interleaved images/text. VQA 2. Factually Augmented RLHF effectively utilizes existing human annotations to improve. Train and test sets, contains 2640 question-image pairs. Knowledge-based visual question answering (VQA) requires external knowledge beyond the image to answer the question. Visual Question Answering ALBEF, BLIP, BLIP2, InstructBLIP VQAv2, OKVQA, A-OKVQA, GQA Image Captioning BLIP, BLIP2, InstructBLIP COCO Caption, NoCaps Image Classication CLIP ImageNet Natural Language Visual Reasoning (NLVR 2) ALBEF, BLIP NLVR Visual Entailment ALBEF SNLI-VE Visual Dialogue BLIP, InstructBLIP VisDialKnowledge based visual question-answering is an emerging technique that combines computer vision and natural language processing to address image-based questions. S3VQA provides a new approach that involves Select, Substitute, and Search (SSS) for open-domain visual question answering. 6% on A-OKVQA). 8Vision-and-language reasoning requires an understanding of visual concepts, language semantics, and, most importantly, the alignment and relationships between these two modalities. {"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":"coco_annotations","path":"coco_annotations","contentType":"directory"},{"name":"coco_clip. Specifically, the questioner identifies an entity in the image and asks a question involving that entity which can be answered only by consulting a knowledge graph or corpus passage mentioning the. This model runs on Nvidia T4 GPU hardware. tasks, exemplified by the task of knowledge-based visual question answering (VQA) that aims to an-swer open-ended questions given an image based on outside knowledge (Schwenk et al. We propose. our idea on OK-VQA and A-OKVQA. 2 56. 这个库的目的是为工程师和研究人员提供一个一站式的解决方案，为他们特定的多模态场景快速开发模型，并在标准和定制的数据集中对其进行基准测试。. OpenFlamingo is a multimodal language model that can be used for a variety of tasks. {"payload":{"allShortcutsEnabled":false,"fileTree":{"eval_mm":{"items":[{"name":"mmbench","path":"eval_mm/mmbench","contentType":"directory"},{"name":"mme","path. First download all OK-VQA files. It has 17K/1K/6K questions for train/val/test. Multimodal IR, spanning text corpus, knowledge graph and images, called outside knowledge visual question answering (OKVQA), is of much recent interest. OK-VQA is a new dataset for visual question answering that requires methods which can draw upon outside knowledge to answer questions. 3) It eliminates the need to specialize LLMs using end-to-end finetuning and serve highly specialized LLMs to end users, thereby reducing cost. e. and A-OKVQA (Schwenk et al. MBR, they are entirely 2 different comparisons. We thus propose the LXMERT (Learning Cross-Modality Encoder Representations from Transformers) framework to learn these vision-and-language connections. More than 100 million people use GitHub to discover, fork, and contribute to over 420 million projects. Follow the below link to access the challenge : 3) It achieves comparable or better performance than methods relying on end-to-end training. We demonstrate PromptCap's effectiveness on an existing pipeline in which GPT-3 is prompted with image captions to carry out VQA. * add scripts for blip2 zero-shot vqa&okvqa evaluation * delete draft task and add back caption evaluation * fix amp scaler, fix freeze ViT, add blip-2 finetune script * remove OKVQA task, apply lemmatization after predict_answers(). Related Material @InProceedings{Guo_2023_CVPR, author = {Guo, Jiaxian and Li, Junnan and Li, Dongxu and Tiong, Anthony Meng Huat and Li, Boyang and Tao, Dacheng and Hoi,. 4% on OK-VQA and 59. 9 54. AudioCaps is a dataset of sounds with event descriptions that was introduced for the task of audio captioning, with sounds sourced from the AudioSet dataset. 8 44. LAVIS简介. Put the download. 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. 41%. Reload to refresh your session. "Retrieval Augmented Visual Question Answering with. json' for reproducing results of okvqa results. Benefiting from large-scale vision- Especially, the candidates. md","path":"README. Zero-shot results on WebQA show that PromptCap. , 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). 6% on VQAv2. 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. exact ground truth common-sense fact triple for question support. 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. {"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":". Codes for VPGTrans: Transfer Visual Prompt Generator across LLMs. 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. For example, the 2019 Outside Knowledge VQA dataset "OKVQA" extends VQA by adding more challenging questions that require complex, factual, and commonsense knowledge. (Optimized for stable-diffusion (clip ViT-L/14))We use a dataset of 1M+ images spanning 10k+ visual concepts to demonstrate webly-supervised concept expansion for two existing GPVs (GPV-1 and VL-T5) on 3 benchmarks: 5 COCO-based datasets (80 primary concepts), a newly curated series of 5 datasets based on the OpenImages and VisualGenome repositories (~500 concepts),. 6% on VQAv2. 14,055 open-ended. Paper ID: Paper Title: Authors: 8: Learning Uncoupled-Modulation CVAE for 3D Action-Conditioned Human Motion Synthesis: Chongyang Zhong (Institute of Computing. 4% on OK-VQA and 59. 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. from Wikipeida) OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge Kenneth Marino, Mohammad Rastegari, Ali Farhadi, Roozbeh Mottaghi 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. 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. The MC component of the dataset bypasses many difficulties inherent in direct answer evaluation and allows for a simple, clean accuracy score. github","path":". VQA [35] and A-OKVQA [43] mostly require common-sense knowledge. LAVIS是一个用于LAnguage-and-VISion智能研究和应用的Python深度学习库。. VATEX is multilingual, large, linguistically complex, and diverse dataset in terms of both video and natural language descriptions. {"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":". OKVQA (Schwenk et al. Run python vigc_demo. 10 ground truth answers per question. We introduce various ways to retrieve knowledge using text and images and two reader styles: classification.