0 81. The path of the model trained previously (step2 OKVQA). We show that the use of language guidance is a simple but powerful and effective strategy for visual question an-swering. ScienceQA (test)Open-domain question answering relies on efficient passage retrieval to select candidate contexts, where traditional sparse vector space models, such as TF-IDF or BM25, are the de facto method. self. 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. {"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":"PythonEvaluationTools","path":"PythonEvaluationTools","contentType":"directory"},{"name. If possible, fine-tune it on that dataset to compare the results. 2) It renders end-to-end training unnecessary and significantly reduces the cost of deploying LLM for VQA tasks. 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. json and examples. To strike a balance between performance and efficiency, we choose to use K= 100 for all. Finally, we investigate PROMPTCAP’sView Slide. 6% needed to be removed. 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. which achieves state-of-the-art results on OKVQA datasets. The MC component of the dataset bypasses many difficulties inherent in (DA) evaluation and allows for a simple, clean accuracy score. Benefiting from large-scale vision-OKVQA S3. 8 145. Knowledge-based visual question answering is a very challenging and widely concerned task. 5亿训练数据的Qwen-VL和1. Emu is trained with a unified autoregressive objective, i. VLC-BERT is a vision-language-commonsense transformer model that incoporates contextualized commonsense for external knowledge visual questioning tasks, OK-VQA and A-OKVQA. 4 57. 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]. au Online enquiry form. Visual Question Answering (VQA) 682 papers with code • 59 benchmarks • 106 datasets. A-OKVQA: Choose the correct option for the following question: question: Prerequisites Models. {"payload":{"allShortcutsEnabled":false,"fileTree":{"Datasets/OKVQA":{"items":[{"name":"Readme. 6% on A-OKVQA). 13 Dustin Schwenk, et al. . 它有一个统一的界面设计. Related work 2. First, download the. R-VQA R-VQA: Learning Visual Relation Facts with Semantic Attention for Visual Question Answering（感觉有点奇怪，主要这个是涉及visual genome ，而且主要是提供了一个supportin fact 。其他文中描述较少。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. In this paper, we propose PROOFREAD -PROmpting vision language. To account for this disparity while still benefiting from the additional data, we include a. For example, we outperform Flamingo \cite{Deepmind:Flamingo2022} by 5. 8 145. To install training or eval dependencies, run one of the first two commands. sh for fine-tuning on image captioning. Finally we address VQA as a text generation task with an effective encoder-decoder paradigm. 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. 2 Table 2. 9 71. Sidney Black. More than 100 million people use GitHub to discover, fork, and contribute to over 420 million projects. 1 51. On the challenging A-OKVQA dataset, our method outperforms some few-shot methods by as much as 20\%. Given an image and a natural language question about the image, the task is to provide an accurate natural language answer. SelTDA. A big convergence of language, vision, and multimodal pretraining is emerging. 6 Unified-IO-XL 100. “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. A new vision-language instruction-tuning framework using BLIP-2 models, achieving state-of-the-art zero-shot generalization performance on a wide range of vision-language tasks. Factually Augmented RLHF effectively utilizes existing human annotations to improve. No milestone. Knowledge-Based Visual Question Answering (KBVQA) is a bi-modal task requiring external world knowledge in order to correctly answer a text question and associated image. 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. vic. 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). Explainability in Visual Question Answering The visual question answering (VQA) is firstly proposed by [33] that requires an intelligent agent to generate an an-A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge The Visual Question Answering (VQA) task aspires to provide a meaningful. 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. OKVQA OKVQA contains visual questions that require outside knowledge to answer. Paper ID: Paper Title: Authors: 8: Learning Uncoupled-Modulation CVAE for 3D Action-Conditioned Human Motion Synthesis: Chongyang Zhong (Institute of Computing. Large pre-trained vision and language models have demonstrated remarkable capacities for various tasks. "Frozen finetuned" has the language model finetuned, while "Frozen" keeps LM frozen. 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). 1. 5 51. github","contentType":"directory"},{"name":"app","path":"app","contentType. 6 InstructBLIP(Vicuna-13B) 121. github","path":". Underspecification in VL tasks like VQA can manifest in several ways, leading to incorrect model predictions. 1 Introduction Large-scale language models (LLMs) have exhib-ited impressive capabilities in terms of their world${MINIGPTv2_EVALUATION_DATASET} ├── gqa │ └── test_balanced_questions. We introduce various ways to retrieve knowledge using text and images and two reader styles: classification. Reload to refresh your session. 5只需要120万公开数据，即可超越用了14. In this paper we create a dataset with questions exclusively about detailed propertiesoutperform Flamingo [3] by 5. PromptCap outperforms generic captions by a large margin and achieves state-of-the-art accuracy on knowledge-based VQA tasks (60. jsonl ├── iconvqa │ └── iconvqa_images │ ├── choose_text_val. Mia Qiao et al. 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. Summary. We propose an artificial intelligence challenge to design algorithms that answer visual questions asked by people who are blind. 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. json. 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. Introduction The field of Visual Question Answering (VQA) has made amazing strides in recent years,. The MC component of the dataset bypasses many difficulties inherent in direct answer evaluation and allows for a simple, clean accuracy score. 6% and BLIP-2 by 4. Insights. 4% of the dataset needed to be corrected and 10. A-OKVQA [46]). Underspecification in VL tasks like VQA can manifest in several ways, leading to incorrect model predictions. AI that explains properly. 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. The proposed method consists in several steps: 1. Through our evaluation on the knowledge-intensive OK-VQA and A-OKVQA datasets, we show that VLC-BERT is. Before you begin, it is recommended that you setup SBERT in a new conda environment. This week presented PaLI which is a language visual model that can perform tasks in 100 languages. We use variants to distinguish between results evaluated on slightly different versions of the same dataset. • GCP Vision APIを⽤いてOCRも実施し，学習に利⽤. Launching Demo. okvqa_train_clean_corpus: the corpus is based on okvqa_train_corpus but filtered with similar process as T5, detailed process referred to paper. py；. 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). Projects. 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. 6% on A-OKVQA). 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. A-OKVQA. 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. 70% (small model) and 70. Edit social preview. In addition, some questions (18%) in A-OKVQA do require knowledge of detailed properties, but about basic-level categories. 1% and 55. json" containing your results in the correct format and submit the ". However, the popular data set has serious limitations. Here is a way to logically break down this. Recent works have sought to use a large. 2 Kosmos-2 - 80. 2022) datasets, as utilized in InstructBLIP (Dai et al. When paired with GPT-3, and conditioned on user question, PromptCap get SOTA performance on knowledge-based VQA tasks (60. MSR-VTT (Microsoft Research Video to Text) is a large-scale dataset for the open domain video captioning, which consists of 10,000 video clips from 20 categories, and each video clip is annotated with 20 English sentences by Amazon Mechanical Turks. Before running the code, prepare two folders: datasets and assets. 1 65. 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. 1 - Flamingo 138. json' for reproducing results of okvqa results. 0 vs 56. We propose the task of free-form and open-ended Visual Question Answering (VQA). 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. json', 'okvqa_caption. A surprisingly large fraction of queries do not assess the ability to integrate cross-modal information. For example, we outperform Flamingo \cite{Deepmind:Flamingo2022} by 5. g. We demonstrate PromptCap's effectiveness on an existing pipeline in which GPT-3 is prompted with image captions to carry out VQA. ,2019) and its augmented versions S3VQA (Jain et al. VQA [35] and A-OKVQA [43] mostly require common-sense knowledge. This repository will hold the official code of SelTDA, the self-training framework introduced in our CVPR 2023 paper "Q: How to Specialize Large Vision-Language Models to Data-Scarce VQA Tasks?The availability of large-scale image captioning and visual question answering datasets has contributed significantly to recent successes in vision-and-language pre-training. Follow the below link to access the challenge : 3) It achieves comparable or better performance than methods relying on end-to-end training. ,2017) collects. Mirroring real-world scenarios, such as helping the visually impaired, both the questions and answers are open-ended. PromptCap outperforms generic captions by a large margin and achieves state-of-the-art accuracy on knowledge-based VQA tasks (60. Setup. f. 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-of-the-art vision-language models. Retrieval-augmented visual-language pre-training. 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. It has 17K/1K/6K questions for train/val/test. It features a unified interface to easily access state-of-the-art image-language, video-language models and common datasets. , how well models perform when answers are in the tail of the dis-tribution, and the complementarity of the studied models). OK-VQA and A-OKVQA, delivering 61. py inside the above 'meta data' folder. 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. See examples for more inference examples, e. The Victorian Registration and Qualifications Authority (VRQA) is the official regulator of education and training providers and qualifications in Victoria. g. GPT drive partitioning would be on the order of milliseconds. The model of VIGC are finetuned on these datasets. @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,. 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. from A-OKVQA (left) and VQAv2 (right) datasets along with REPARE outputs. (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),. 93% (large model) overall accuracy on the test-dev split of. Try for $5/month. mkdir -p data/nocaps && cd data/nocaps # download images from # original annotations can be downloaded from. 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. 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. 1 - - 82. Visual Question Answering (VQA) 682 papers with code • 59 benchmarks • 106 datasets. txt -. For OKVQA, earlier attempts that incorporate a fixed knowledge retriever report results that are below 45%. READ FULL TEXT. 3 An interpretable OKVQA system Continuinginthespiritof“smallstepsbeforegiantleap”,wepresent S3 (c. Official repository for A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge. okvqa. 2 % of the number of samples used to train SimVLM. We propose the task of free-form and open-ended Visual Question Answering (VQA). 1. 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. . in the order defined in input_modules, and then the postprocessing unit PostProcessInputTokenization is used to tokenize the input into input_ids and input_attention_masks. S3VQA. Img2Prompt-VQA surpasses Flamingo on zero-shot VQA on VQAv2 (61. yaml","path":"lavis/projects/blip2/eval. Please save the files to the appropriate locations. Model details. [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. It has been split into 9K/5K for train and test. To install everything, run the third command. 14,055 open-ended. Key tasks are translated into languages with an advanced translation system. The latest such methods simultaneously introduce LLM-based code generation to build programs and a number of. 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. The field of visual question answering (VQA) has recently seen a surge in research focused on providing explanations for predicted answers. 4% on OK-VQA and 59. gov. For example, we outperform Flamingo <cit. g. Hi, eval_okvqa_zeroshot_flant5xl. 1 WIT w/o L contra 47. Recent. json and candidates_okvqa. 1% and 55. These datasets, necessitating. NExT-QA Video question answering (VideoQA) benchmark to advance video understanding from describing to explaining the temporal actions. f. English | 简体中文 | 繁體中文 | 한국어 | Español | 日本語 | हिन्दी | Русский | Рortuguês | తెలుగు | . Analyzing Modular Approaches for Visual Question Decomposition. zip, we provide a processing script and some source data for both vqa2 and okvqa datasets. 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. Corresponding of the last pytorch_model_**. in A-OKVQA; (iv) An extensive analysis of the results leading to interesting findings (e. 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. Visual Question Answering (VQA) is a task in computer vision that involves answering questions about an image. A major step in developing OKVQA systems is to retrieve relevant documents for the given multimodal query. 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. Finetuning details are available in C. Our method continuously boosts the performance of baselines methods by an average gain of 2. bash run_okvqa_train. sh provides the script for evaluation. 1. 8% on OK-VQA, 5. 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. 4% on OK-VQA and 59. initializing a BertForSequenceClassification model from a BertForPreTraining model). txt. , predict-the-next-element, including both visual embeddings and textual tokens. 9 67. or to create a conda environment for running OpenFlamingo, run. 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. zip" file. Multimodal IR, spanning text corpus, knowledge graph and images, called outside knowledge visual question answering (OKVQA), is of much recent interest. By using the commonly used bottom-up-attention visual features, a single MCAN model delivers 70. Code is available via the LAVIS [28] frameworkBeside the performance gain, Cola is also more robust to the VLMs' errors. Our model consists of three components: mutual modulation, knowledge-based key–value memory network and knowledge-based representation learning. github","contentType":"directory"},{"name":"app","path":"app","contentType. 2% of the number of samples used to train SimVLM. a 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. Our data is based on the OK-VQA dataset. VL-LLaMA, VL-Vicuna. Our method integrates LLMs with three types of tools: (i) computer vision tools for extracting visual information from images, (ii) a web search tool. ,2022), models are free to use any existing knowledge bases to re-trieve relevant knowledge. We show that the use of language guidance is a simple but powerful and effective strategy for visual question answering. The MC component of the dataset bypasses many dificulties inherent in direct answer evaluation and allows for a simple, clean accuracy score. As shown in Figure[4] the Q-Former consists of two transformer submodules sharing the same self-attention layers. Student exchange. "Frozen scratch" does not load a pre-trained LM and is trained from scratch. 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. datasets: pre-extracted image features. 9 82. The total model parameters are 17. 7% accuracies on their testing sets, respectively. Knowledge-based visual question answering (VQA) requires external knowledge beyond the image to answer the question. You switched accounts on another tab or window. However, in our analysis, we found that 41. To start training, you need to apply for and download the LLaMA-2-7B-chat-hf checkpoints here and download the LLaVA pretrained. This IS NOT expected if you are initializing LxmertModel from the checkpoint of a model. 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. 5. {"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":". bash run_okvqa_full. . conda env create -f environment. multimodal-dense-retriever-for-okvqa 2 RELATED WORK Multi-Modal Dense Passage Retrieval. Recently a series of works utilize large language models (e. 85% (absolute) increase in zero-shot performance on VQAv2 and a 6. Introduced by Schwenk et al. Paper ID Paper Title Authors : 8 : Learning Uncoupled-Modulation CVAE for 3D Action-Conditioned Human Motion Synthesis : Chongyang Zhong. main. 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. Our code is publicly available at this. VQA is a new dataset containing open-ended questions about images. yaml","path":"projects/krisp/configs/krisp. 1% and 55. A-OKVQA A-OKVQA is a successor of OKVQA with more challenging and diverse questions. 6% on VQAv2. 26% on test-std and test-challenge splits, respectively. It is trained on a large multimodal dataset (e. Visual. py. 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. When booting in UEFI, I would bet the speed differences between MBR v. 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,. For example, OpenFlamingo can be used to generate a caption for an image, or to generate a question given an image and a. KiloGram is a resource for studying abstract visual reasoning in humans and machines. We demonstrate PROMPTCAP's effectiveness on an existing pipeline in which GPT-3 is prompted with image captions to carry out VQA. Project Explorer. 7% accuracies on their testing sets, 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. g. 23% and 75. 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. 3) It achieves comparable or better performance than methods relying on end-to-end training. model (FLAN-T5) of a question in A-OKVQA dataset. Statistics of our instructions: Statistics of our dataset grouped by task: Model Evaluation. Dense Passage Retrieval (DPR) - is a set of tools and models for state-of-the-art open-domain Q&A research. 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. Knowledge-based visual question answering (VQA) requires external knowledge beyond the image to answer the question. Thanks. It contains about 2M samples from VQA, Detector, Detailed Description of Image, and others. The VRQA regulates school education in Victoria, including senior secondary education and international education. Fuyu-8B is a multi-modal text and image transformer trained by Adept AI. All code has been uploaded, but I'm still working on the documentation. 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. 6% in VQA score). 2019) and A-OKVQA (Schwenk et al. 8 3) It achieves comparable or better performance than methods relying on end-to-end training. OK-VQA (Outside Knowledge Visual Question Answering) Introduced by Marino et al. 3亿数据. BLIP also demonstrates strong generalization ability when directly transferred to videolanguage tasks in a zero-shot manner. In this paper, we. Mini-GPT4. 7. 1% and 55. The text-only version of the original. The benchmarks section lists all benchmarks using a given dataset or any of its variants. 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. A-OKVQA. Early studies retrieve required knowledge from explicit knowledge. 0 - - - 29. We thus propose the LXMERT (Learning Cross-Modality Encoder Representations from Transformers) framework to learn these vision-and-language connections. The goal of VQA is to teach machines to understand the content of an image and answer questions about it in natural language. UEFI can boot both MBR and GPT drives. 6% on VQAv2. VL-LLaMA, VL-Vicuna. We propose embodied language models to directly incorporate real-world continuous sensor modalities into language models and thereby establish the link. Zhenwei Shao, Zhou Yu, Meng Wang, Jun Yu; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023, pp. 4% on OK-VQA and 59. By defining new functions in ModuleParser, e. VQAv2 NAME@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 = "Proceedings of the 61st. However, the popular data set has serious limitations. Sidney Black 1; Samuel Weinbach 1; Letitia Parcalabescu 1;It says module object is not callable, because your code is calling a module object. okvqa. Introduction. 41% point increase on A-OKVQA. Reload to refresh your session. Despite this progress, complex visual-based tasks still remain challenging due. exact ground truth common-sense fact triple for question support. The field of visual question answering (VQA) has recently seen a surge in research focused on providing explanations for predicted answers. 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. Python. The visual retriever aims to retrieve relevant knowledge, and the visual reader seeks to predict answers based on given knowledge. ,2022) typically lead to. 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. Specifically, we used OKVQA (Marino et al. 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. 3% on A-OKVQA, and 9. 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. 6% on A-OKVQA). Building SBERT annotations: . For example, we outperform Flamingo by 5. In the evaluation with. In. A-OKVQA, COCO Caption, and OCR VQA datasets is considered inferior compared to LLaV A and. ,2021) is an augmented ver-sion of OKVQA, improving both the quantity and quality of some question types. 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. ,2022). OpenFlamingo is a multimodal language model that can be used for a variety of tasks. This category is called outside-knowledge visual question answering (OK-VQA). 3 50. Knowledge-based visual question answering (VQA) requires external knowledge beyond the image to answer the question. e. * update runner - configurable beta. and. On the challenging A-OKVQA dataset, our method even outperforms few-shot methods by as much as 20%. 1% and 55. Architecturally, Fuyu is a vanilla decoder-only transformer - there is no image encoder. png","path":"misc/framework. 265,016 images (COCO and abstract scenes) At least 3 questions (5. 6% on A-OKVQA). g. MLLM-DataEngine: An Iterative Refinement Approach for MLLM . 2 SimVLM. 3. 2% vs 44. To effectively incorporate an external KG, we transfer triples into textual format and propose a late injection mechanism for knowledge fusion. Dongxu Li. 1 - - - - BLIP-2(Vicuna-13B) 103. 41%. 6% needed to be removed. treat OKVQA as a task of fusing structured data from the image with the unstructured text rather than a visual recog-nition problem. In this work, we show that retrieval can be practically implemented using dense representations alone, where embeddings are learned from a. 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. 3) It achieves comparable or better performance than methods relying on end-to-end training. Meanwhile, automatic measures and human eval-uations all show the effectiveness of our method. Only 18% of questions in A-OKVQA require answers from an external knowledge base. Furthermore, through a detailed analysis, we explain which questions benefit, and which don't, from contextualized commonsense knowledge from COMET. yaml","path":"vigc/configs/datasets/a-okvqa/vic/train.