8Vision-and-language reasoning requires an understanding of visual concepts, language semantics, and, most importantly, the alignment and relationships between these two modalities. 9 71. png","path":"misc/framework. 1% and 55. 4% on OK-VQA and 59. High-quality instruction tuning data (VQA-v2, A-OKVQA, Flickr30k) significantly improves LMM capabilities on benchmarks. A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge. 基于知识的数据集有R-VQA , FVQA , KVQA ,OKVQA,KBVQA. Visual Question Answering (VQA) 682 papers with code • 59 benchmarks • 106 datasets. Introduced by Schwenk et al. We show that the use of language guidance is a simple but powerful and effective strategy for visual question an-swering. Our starting point is a modular re-implementation of the bottom-up top-down (up-down) model. {"payload":{"allShortcutsEnabled":false,"fileTree":{"eval_mm":{"items":[{"name":"mmbench","path":"eval_mm/mmbench","contentType":"directory"},{"name":"mme","path. pip install open-flamingo [training] pip install open-flamingo [eval] pip install. {"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":"PythonEvaluationTools","path":"PythonEvaluationTools","contentType":"directory"},{"name. 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. a. Insights. 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. "Retrieval Augmented Visual Question Answering with. 4% on OK-VQA and 59. ; Dataset Download and Browsing: see Dataset Download for instructions and. We also conduct extensive ablation stud-ies on the contribution of each component, showing that PROMPTCAP gives a consistent performance gain (3. Sidney Black. What is LAVIS? LAVIS is a Python deep learning library for LAnguage-and-VISion research and applications. Hence, we call it Augmented OK-VQA (A-OKVQA). For example, we outperform Flamingo <cit. Given an image and a natural language question about the image, the task is to provide an accurate natural language answer. LAVIS是一个用于LAnguage-and-VISion智能研究和应用的Python深度学习库。. 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. 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. For now we use LLaVA-LLaMA-2-7B as the fixed model. It is trained on a large multimodal dataset (e. See examples for more inference examples, e. JourneyDB: A Benchmark for Generative Image Understanding{"payload":{"allShortcutsEnabled":false,"fileTree":{"minigpt4/configs/datasets/cc_sbu":{"items":[{"name":"align. sh --task ok --version okvqa_pretrain_1 --gpu 0. ternal corpus. 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. Visual. To start training, you need to apply for and download the LLaMA-2-7B-chat-hf checkpoints here and download the LLaVA pretrained. LLaVA, A-OKVQA, OKVQA. in A-OKVQA; (iv) An extensive analysis of the results leading to interesting findings (e. 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. Current state-of-the-art asymmetric dense retrieval model for this task uses an architecture with a multi-modal query encoder and a uni-modal document. Manually filtered to ensure all questions require outside knowledge (e. It features a unified design to access state-of-the-art foundation language-vision models (ALBEF, BLIP,. A-OKVQA A-OKVQA is a successor of OKVQA with more challenging and diverse questions. It is suggested to write a wrapper class using exiting dataset classes. 0 dataset: train2015. 1. In addition, some questions (18%) in A-OKVQA do require knowledge of detailed properties, but about basic-level categories. A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge. Our method integrates LLMs with three types of tools: (i) computer vision tools for extracting visual information from images, (ii) a web search tool. OKVQA (Schwenk et al. AI that explains properly. 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. 实验结果. 6% on A-OKVQA). 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. For example, the 2019 Outside Knowledge VQA dataset "OKVQA" extends VQA by adding more challenging questions that require complex, factual, and commonsense knowledge. OK-VQA: A Visual Question Answering Benchmark Requiring. comm [at [ gmail [dot] com and include (1) the OK-VQA test results output file, (2) a name for the method, (3) a github repo or paper link, (4) your institution. Then download the 2014_coco val anotation file in link, and put it in annotation_new folder. 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. OK-VQA and A-OKVQA, delivering 61. 6\% on VQAv2. See a full comparison of 11 papers with code. The VRQA regulates school education in Victoria, including senior secondary education and international education. Edit social preview. our idea on OK-VQA and A-OKVQA. Key tasks are translated into languages with an advanced translation system. This model runs on Nvidia T4 GPU hardware. CCS CONCEPTS •Computingmethodologies→Artificialintelligence;Knowl-edge representation and reasoning; Semantic networks. 1 - - 82. zip" file. In this release, we use LLaVA at [email protected]) 55. and. Introduction The field of Visual Question Answering (VQA) has made amazing strides in recent years,. English | 简体中文 | 繁體中文 | 한국어 | Español | 日本語 | हिन्दी | Русский | Рortuguês | తెలుగు | . 6% on A-OKVQA) QuickStart Installation pip install promptcap Two pipelines are included. Our language guidance improves the performance of CLIP by 7. 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. Predictions typically complete within 27 seconds. 7% accuracies on their testing sets, respectively. (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),. 5 ground truth answers per question. 6% on A-OKVQA). 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. Visual Question Answering ALBEF, BLIP VQAv2, OKVQA, A-OKVQA Image Captioning BLIP COCO Caption, NoCaps Image Classification CLIP ImageNet Natural Language Visual Reasoning (NLVR 2) ALBEF, BLIP NLVR Visual Entailment ALBEF SNLI-VE Visual Dialogue BLIP VisDial Video-text Retrieval ALPRO, BLIP MSRVTT, DiDeMoThanks for your question. 3 An interpretable OKVQA system Continuinginthespiritof“smallstepsbeforegiantleap”,wepresent S3 (c. We developed this code in the frame of a research paper called MUTAN: Multimodal Tucker Fusion for VQA which is (as far as we know) the. sh provides the script for evaluation. Architecturally, Fuyu is a vanilla decoder-only transformer - there is no image encoder. VATEX is multilingual, large, linguistically complex, and diverse dataset in terms of both video and natural language descriptions. Emu is a multimodal generalist that can seamlessly generate images and texts in multimodal context. 10 ground truth answers per question. 5 51. DataEngine-InstData, high-quality and targeted VQA data generated by MLLM-DataEngine, also refered to as. The hyperparameter settings match the NeuCRaB experiments. g. Note: This repository has code for the VLC-BERT transformer model. e. model (FLAN-T5) of a question in A-OKVQA dataset. 7 - - 28. However, the popular data set has serious limitations. We propose. The MC component of the dataset bypasses many dificulties inherent in direct answer evaluation and allows for a simple, clean accuracy score. Annotators were provided the audio tracks together with category hints (and with additional video hints. General enquiries . VL-LLaMA, VL-Vicuna. 4 questions on average) per image. Hi, I'm trying to evaluate the provided pre-trained BEiT3 (beit3_large_indomain_patch16_480) on the A-OKVQA dataset to check its transferability to other VQA datasets. You need to enable JavaScript to run this app. OKVQA w/ pretrain Bibtex @inproceedings{Ding2022mukea, title={MuKEA: Multimodal Knowledge Extraction and Accumulation for Knowledge-based Visual Question Answering}, author={Yang Ding and Jing Yu and Bang Liu and Yue Hu and Mingxin Cui and Qi Wug}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 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. Improving and Diagnosing Knowledge-Based Visual Question Answering via Entity Enhanced Knowledge Injection install dependencies download data/models set paths for KVQA and OKVQA to train / test models on KVQA for evaluating finetuned models with explanations from integrated Bi-Modal attention explanation system Finetune/Test/Get Explainations. More than 100 million people use GitHub to discover, fork, and contribute to over 420 million projects. Introduced by Kim et al. 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 . For this purpose, we introduce the visual question answering (VQA) dataset. OK-VQA and A-OKVQA, delivering 61. ,2017) collects. {"payload":{"allShortcutsEnabled":false,"fileTree":{"misc":{"items":[{"name":"framework. yaml","path":"vigc/projects. No need to download if you want to train your own model; Sample. 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. 0 (Goyal et al. Codes for VPGTrans: Transfer Visual Prompt Generator across LLMs. On the challenging A-OKVQA dataset, our method even outperforms few-shot methods by as much as 20\%. 3 61. ,2021) is an augmented ver-sion of OKVQA, improving both the quantity and quality of some question types. S3 reaches the end result (i. launch --nproc_per_node 4 train_retriever. py --input_file=DATA_DIR/data/{}_pairs_cap_combine_sum. 13 Dustin Schwenk, et al. bash run_okvqa_full. Fig. The total model parameters are 17 billion (language. VL-LLaMA, VL-Vicuna. NExT-QA Video question answering (VideoQA) benchmark to advance video understanding from describing to explaining the temporal actions. Dense Passage Retrieval. , Section 5), a neural OKVQA system that targets this class of queries and reasoning structure. This library aims to provide engineers and researchers with a one-stop. {"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":"code","path":"code","contentType":"directory"},{"name":"competition files","path. 4 结果 结果显示，架构更加简单的LLaVA-1. Recent. 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. The question edition code is largely modified based on Edit-Unsup-TS, you need to have a CoreNLP Server running on port 9000 in code/src/. 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). 1 51. Model type: BLIVA is an open-source Vision-Languagde model trained by initializing from InstructBLIP and alignment with Vicuna on multimodal instruction-finetuning data. 1 - Flamingo 138. You need to enable JavaScript to run this app. @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 =. Code is available via the LAVIS [28] frameworkBeside the performance gain, Cola is also more robust to the VLMs' errors. py","contentType":"file"},{"name. gov. See our slides for details. {"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":". 4 57. 6% on VQAv2. Underspecification in VL tasks like VQA can manifest in several ways, leading to incorrect model predictions. 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. READ FULL TEXT. A-OKVQA is crowdsourced visual question. 1% and 55. {"payload":{"allShortcutsEnabled":false,"fileTree":{"vigc/configs/datasets/a-okvqa/vqg":{"items":[{"name":"train. 1. 2% on VQAv2) over a generic captioning model that shares the same architecture and training data. The proposed method consists in several steps: 1. 0 - 77. KiloGram is a resource for studying abstract visual reasoning in humans and machines. We leverage semantic representations of both the scenes and questions to mitigate language. txt. OK-VQA (Outside Knowledge Visual Question Answering) Introduced by Marino et al. In this paper, we present OtterHD-8B, an innovative multimodal model evolved from Fuyu-8B, specifically engineered to interpret high-resolution visual inputs with granular precision. 6 65. Visual question answering (VQA) often requires an understanding of visual concepts and language. For example, the 2019 Outside Knowledge VQA dataset "OKVQA" extends VQA by adding more challenging questions that require complex, factual, and commonsense knowledge. in Abstract Visual Reasoning with Tangram Shapes. > by 5. Large pre-trained vision and language models have demonstrated remarkable capacities for various tasks. 1. Obtain reader cross-attention scores. 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。. f. md","contentType":"file. Visual. pip install open-flamingo [training] pip install open-flamingo [eval] pip install open-flamingo. Knowledge-based visual question answering (VQA) requires external knowledge beyond the image to answer the question. initializing a BertForSequenceClassification model from a BertForPreTraining model). Reload to refresh your session. If possible, fine-tune it on that dataset to compare the results. g. 4% on OK-VQA and 59. 🚀 Train. Search. 0 124. Modular neural networks without additional training have recently been shown to surpass end-to-end neural networks on challenging vision-language tasks. 9 54. Thanks. 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 NocapsMoreover, we propose a Visual Retriever-Reader pipeline to approach knowledge-based VQA. 8 145. 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. To effectively incorporate an external KG, we transfer triples into text and propose a late injection mechanism. A surprisingly large fraction of queries do not assess the ability to integrate cross-modal information. 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. py inside the above 'meta data' folder. VQA is a new dataset containing open-ended questions about images. Zero-shot results on WebQA show that PromptCap. VQA [35] and A-OKVQA [43] mostly require common-sense knowledge. GitHub is where people build software. 3. 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. 26% on test-std and test-challenge splits, respectively. 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. 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. "Question: {question} Answer:"). github","contentType":"directory"},{"name":"app","path":"app","contentType. Benefiting from large-scale vision- $ bash scripts/pretrain. For example, we outperform Flamingo by 5. It contains about 2M samples from VQA, Detector, Detailed Description of Image, and others. 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. 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. We demonstrate PromptCap's effectiveness on an existing pipeline in which GPT-3 is prompted with image captions to carry out VQA. Finally we address VQA as a text generation task with an effective encoder-decoder paradigm, which achieves state-of-the-art results on OKVQA dataset. 5只需要120万公开数据，即可超越用了14. Then you can run the shell in folder VL_captioning to reproduce results, e. title = {VQA: Visual Question Answering}, booktitle = {International Conference on Computer Vision (ICCV)}, year = {2015}, } The following links contain the abstract scenes' composition files for Abstract Scenes v1. Reload to refresh your session. • 上記に加えて，物体検出⽤のデータセットやVQA⽤の. A-OKVQA. prdwb/okvqa-release official. and A-OKVQA (Schwenk et al. Summary. BLIP also demonstrates strong generalization ability when directly transferred to videolanguage tasks in a zero-shot manner. • 約10Bの画像・alt-textペアをフィルタリングし，約1Bのデータを学習に利⽤. We chose the OKVQA dataset because the task requires additional knowledge beyond its own training set, and it has been shown that proper pretraining brings significant benefits to performance [10, 30]. in OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge Outside Knowledge Visual Question. 3 70. 可以看到，尽管AN效. 1 Introduction Large-scale language models (LLMs) have exhib-ited impressive capabilities in terms of their world${MINIGPTv2_EVALUATION_DATASET} ├── gqa │ └── test_balanced_questions. @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,. Large-scale pretraining. 🤗 Transformers provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio. On the challenging A-OKVQA dataset, our method even outperforms few-shot methods by as much as 20%. 1 54. 2% vs 44. OCR-VQA: Visual Question Answering by Reading Text in Images Anand Mishra, Shashank Shekhar, Ajeet Kumar Singh, Anirban Chakraborty ICDAR 2019Recent research on Large Language Models (LLMs) has led to remarkable advancements in general NLP AI assistants. github","path":". It is based on the following paper: Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, Wen-tau Yih. . 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. Performance of different versions of Frozen on (left) VQAv2 and (right) OKVQA, trained on Conceptual Captions. formance on VQA-X [13] and A-OKVQA [49] benchmark datasets. 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. 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. sh for fine-tuning on image captioning. OK-VQA (Outside Knowledge Visual Question Answering) Introduced by Marino et al. To address this, we propose. For example, OpenFlamingo can be used to generate a caption for an image, or to generate a question given an image and a. Project Explorer. json" containing your results in the correct format and submit the ". github","contentType":"directory"},{"name":"app","path":"app","contentType. Knowledge-based visual question answering (VQA) requires external knowledge beyond the image to answer the question. 4% on OK-VQA and 59. 2 SimVLM. Retrieval-augmented visual-language pre-training. Conclusion. 6% on VQAv2. S3VQA. {"payload":{"allShortcutsEnabled":false,"fileTree":{"vigc/projects/instruct_blip_vicuna7b/vigc/a-okvqa":{"items":[{"name":"lora_vig. 70% (small model) and 70. Run python vigc_demo. On the challenging A-OKVQA dataset, our method even outperforms few-shot methods by as much as 20%. 6% needed to be removed. You signed in with another tab or window. Answer vocabularies for the OK-VQA and A-OKVQA . It has been shown that PLM-enhanced approaches (Gui et al. There are about 29,000 unique words in all captions. ,2022). 6% on A-OKVQA). WebQA (Chang et al. , GPT-3) as an implicit. au Online enquiry form. PDF Abstract Vision-and-language reasoning requires an understanding of visual concepts, language semantics, and, most importantly, the alignment and relationships between these two modalities. However, the popular data set has serious limitations. A-OKVQA [46]). It contains a richly annotated dataset with >1k. 2% of the number of samples used to train SimVLM. TextBasedVisionInput, a new behavior can be easily introduced to transform. Student exchange. "Frozen finetuned" has the language model finetuned, while "Frozen" keeps LM frozen. However, in our analysis, we found that 41. 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. 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. 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. {"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":". Minor improvements. When paired with GPT-3, and conditioned on user question, PromptCap get SOTA performance on knowledge-based VQA tasks (60. 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). 1. Qwen-VL: A Frontier Large Vision-Language Model with Versatile Abilities JinzeBai ∗ShuaiBai ShushengYang ShijieWang SinanTan PengWang JunyangLin ChangZhou† JingrenZhou AlibabaGroup Abstract WeintroducetheQwen-VLseries,asetoflarge-scalevision-languagemodelsdesignedtoKiloGram. from A-OKVQA (left) and VQAv2 (right) datasets along with REPARE outputs. 3 70. 85% (absolute) increase in zero-shot performance on VQAv2 and a 6. md","path":"Datasets/OKVQA/Readme. 1% and 55. corpus size. g. 8% in the challenging A-OKVQA dataset. okvqa. LAVIS简介. PDF Abstract . Follow the below link to access the challenge : 3) It achieves comparable or better performance than methods relying on end-to-end training. * 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(). Assuming that we have already retrieved relevant passages for each question, the first step consists in generating cross-attention scores. 8 Flamingo-80B - 67. 9 vs 56. Previous methods adopts the implicit knowledge in large language models (LLM) to achieve excellent results, but we argue that existing methods may suffer from biasing understanding of the image and insufficient knowledge to solve the problem. 6 CIDEr score vs previous best 113. For example, you can download 'okvqa_question. We demonstrate PromptCap's effectiveness on an existing pipeline in which GPT-3 is prompted with image captions to carry out VQA. Mia Qiao et al. 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. We thus propose the LXMERT (Learning Cross-Modality Encoder Representations from Transformers) framework to learn these vision-and. This is the official repository of the Retrieval Augmented Visual Question Answering (RAVQA) project. 1. 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. In the evaluation with. 4. 0 81. zip, we provide a processing script and some source data for both vqa2 and okvqa datasets. 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. Hi, eval_okvqa_zeroshot_flant5xl. S3VQA provides a new approach that involves Select, Substitute, and Search (SSS) for open-domain visual question answering. state-of-the-art OKVQA systems, we are surprised to find existing OKVQA models yield close to 0 evaluation score on S3VQA. json' for reproducing results of okvqa results. 0 124. . We train a VLM model on our. Zhenwei Shao, Zhou Yu, Meng Wang, Jun Yu; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023, pp. Knowledge graphs are commonly. A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge The Visual Question Answering (VQA) task aspires to provide a meaningful. 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. The total model parameters are 17. In addition, some questions (18%) in A-OKVQA do require knowledge of detailed properties, but about basic-level categories. 6 InstructBLIP(Vicuna-13B) 121. 6% on A-OKVQA). To prompt GPT-3 with answer heuristics and generate better answers, run the following command: okvqa. Train and test sets, contains 6765 question-image pairs. This version of Multimodal Instruction Data includes diverse and high-quality dowanstream data. In this paper, we propose an end-to-end Retrieval-Augmented Visual Language Model (REVEAL) that learns to encode world knowledge into a large-scale memory, and to retrieve from it to answer knowledge-intensive queries. json' and 'okvqa_ans_to_cap_dict. 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. A-OKVQA, COCO Caption, and OCR VQA datasets is considered inferior compared to LLaVA and Mini-GPT4. 41%. 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. Visual Question Answering (VQA) 682 papers with code • 59 benchmarks • 106 datasets. 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. 41% point increase on A-OKVQA. 2. Multimodal IR, spanning text corpus, knowledge graph and images, called outside knowledge visual question answering (OKVQA), is of much recent interest. Extensive experiments demonstrate the effectiveness of the proposed approach on the knowledge-based VQA task. We demonstrate PROMPTCAP's effectiveness on an existing pipeline in which GPT-3 is prompted with image captions to carry out VQA. The MC component of the dataset bypasses many difficulties inherent in (DA) evaluation and allows for a simple, clean accuracy score. On the challenging A-OKVQA dataset, our method even outperforms few-shot methods by as much as 20\%. 0 81. Qwen-VL: A Frontier Large Vision-Language Model with Versatile Abilities JinzeBai ∗ShuaiBai ShushengYang ShijieWang SinanTan PengWang JunyangLin ChangZhou† JingrenZhou AlibabaGroup Abstract WeintroducetheQwen-VLseries,asetoflarge-scalevision-languagemodelsdesignedtoHi @dxli94, I saw that some of this work (VQAv2 and OKVQA) has landed now -- thanks for that! I'm particularly interested in GQA, and still unable to reproduce that result (42. 2) It flexibly interfaces with a wide range of LLMs to perform VQA. 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. OK-VQA is a new dataset for visual question answering that requires methods which can draw upon outside knowledge to answer questions. “视觉问答作为多模态任务,需要深度理解图像和文本问题从而推理出答案。然而在许多情况下,仅在图像和问题上进行简单推理难以得到正确的答案,事实上还有其它有效的信息可以被利用,例如图像描述、外部知识等。We convert VQA-v2 (83k) and A-OKVQA (16k) into a multi-round QA task, and Flickr30k (23k) into a Spotting Captioning task, and train the LLaVA-SFT+ models based on the new mixture of data including LLaVA-Instruct-90k (randomly sampled from LLaVA-Instruct-150K) Factually-Augmented RLHF. These questions require an understanding of vision, language and commonsense knowledge to answer. LAVIS is a Python deep learning library for LAnguage-and-VISion intelligence research and applications. . The Victorian Registration and Qualifications Authority (VRQA) is the official regulator of education and training providers and qualifications in Victoria. e. Given an image and a natural language question about the image, the task is to provide an accurate natural language answer. 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. 3% on A-OKVQA, and 9. datasets: pre-extracted image features with this script (Optional) checkpoint: our model checkpoint.