. Try for $5/month. Zero-shot results on WebQA show. To submit your method to the leaderboard, contact okvqa. JourneyDB: A Benchmark for Generative Image Understanding{"payload":{"allShortcutsEnabled":false,"fileTree":{"minigpt4/configs/datasets/cc_sbu":{"items":[{"name":"align. Zero-shot results on WebQA show. We leverage semantic representations of both the scenes and questions to mitigate language. Knowledge-based visual question answering (VQA) requires external knowledge beyond the image to answer the question. 7% in average recall@1), image captioning (+2. To account for this disparity while still benefiting from the additional data, we include a. Instead, some are. , predict-the-next-element, including both visual embeddings and textual tokens. See our slides for details. PDF Abstract . prdwb/okvqa-release official. 🚀 Train. The MC component of the dataset bypasses many difficulties inherent in direct answer evaluation and allows for a simple, 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. 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. 1 65. A-OKVQA. 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. 4. You need to enable JavaScript to run this app. md","path":"Datasets/OKVQA/Readme. OK-VQA: A Visual Question Answering Benchmark Requiring. md. Our system. Img2Prompt-VQA surpasses Flamingo on zero-shot VQA on VQAv2 (61. 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. 2) It renders end-to-end training unnecessary and significantly reduces the cost of deploying LLM for VQA tasks. Co-authors. We propose the task of free-form and open-ended Visual Question Answering (VQA). Contributions. 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,. PromptCap outperforms generic captions by a large margin and achieves state-of-the-art accuracy on knowledge-based VQA tasks (60. Some studies have further explored the use of LLMs for planning and invoking models or APIs to address more general multi-modal user queries. 8 44. 6% on A-OKVQA). However, in our analysis, we found that 41. {"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":"LICENSE","path":"LICENSE","contentType":"file"},{"name":"README. 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. 4 57. We propose MM-REACT, a system paradigm that integrates ChatGPT with a pool of vision experts to achieve multimodal reasoning and action. 41% point increase on A-OKVQA. our idea on OK-VQA and A-OKVQA. KEYWORDS Visual Question Answering; Knowledge Graph; Knowledge-to-Text; Late Knowledge Injection ACM Reference Format:In response, we identify a key structural idiom in OKVQA ,viz. 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. github","contentType":"directory"},{"name":"app","path":"app","contentType. Our model consists of three components: mutual modulation, knowledge-based key–value memory network and knowledge-based representation learning. KiloGram is a resource for studying abstract visual reasoning in humans and machines. These questions require an understanding of vision, language and commonsense knowledge to answer. GitHub is where people build software. However, these datasets are often collected with overrestrictive requirements inherited from their original target tasks (e. 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. 2 Table 2. 1. This library aims to provide engineers and researchers with a one-stop. A-OKVQA, COCO Caption, and OCR VQA datasets is considered inferior compared to LLaVA and Mini-GPT4. 3) It eliminates the need to specialize LLMs using end-to-end finetuning and serve highly specialized LLMs to end users, thereby reducing cost. There is not any. zip" file. Model details. 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. This version of Multimodal Instruction Data includes diverse and high-quality dowanstream data. 14974-14983. Setup. Emu is a multimodal generalist that can seamlessly generate images and texts in multimodal context. Large-scale pretraining. This model runs on Nvidia T4 GPU hardware. 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. 2. “Easy to use AI that explains images” is published by MLBoy. To install everything, run the third command. 1% and 55. The total model parameters are 17. 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。. With a semi-supervised learning. @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 =. {"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":". 2% on VQAv2) over a generic captioning model that shares the same architecture and training data. model (FLAN-T5) of a question in A-OKVQA dataset. from A-OKVQA (left) and VQAv2 (right) datasets along with REPARE outputs. Search. Please save the files to the appropriate locations. json' for reproducing results of okvqa results. Recent works have sought to use a large. In this release, we use LLaVA at [email protected]) 55. Early studies retrieve required knowledge from explicit knowledge. 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. Prophet significantly outperforms all existing state-of-the-art methods on two challenging knowledge-based VQA datasets, OK-VQA and A-OKVQA, delivering 61. 3 An interpretable OKVQA system Continuinginthespiritof“smallstepsbeforegiantleap”,wepresent S3 (c. You switched accounts on another tab or window. VQA Questions about images that require an understanding of vision, language and. When booting in UEFI, I would bet the speed differences between MBR v. A-OKVQA, COCO Caption, and OCR VQA datasets is considered inferior compared to LLaVA and Mini-GPT4. 5. 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. OK-VQA and A-OKVQA, delivering 61. > by 5. 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. captioning, feature extraction, VQA, GradCam, zeros-shot classification. , Section 5), a neural OKVQA system that targets this class of queries and reasoning structure. We show one example question for each knowledge category. Finetuning details are available in C. We show that the use of language guidance is a simple but powerful and effective strategy for visual question an-swering. Submitting to the leaderboard. Data Preparation . 6 - - 31. Ablation on Pre-Training Corpus: We pre-train REVEAL-Base on WIT and CC12M dataset, and report the fine-tuned OKVQA performance. [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. 8 3) It achieves comparable or better performance than methods relying on end-to-end training. ,2021) and A-OKVQA (Schwenk et al. 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. Visual Question Answering (VQA) v2. 9 54. 3 61. However, the popular data set has serious limitations. 大部分的VQA任务不需要外部知识，仅仅局限于：简单计数，视觉属性判断（如颜色），物体检测任务。. Our new dataset includes more than 14,000 questions that require external knowledge to answer. 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. PROMPTCAP outperforms generic captions by a large margin and achieves state-of-the-art accuracy on knowledge-based VQA tasks (60. It has 17K/1K/6K questions for train/val/test. Follow the below link to access the challenge :For example, we outperform Flamingo by 5. zip" file. READ FULL TEXTThis work introduces A-OKVQA, a crowdsourced dataset composed of a diverse set of about 25K questions requiring a broad base of commonsense and world knowledge to answer, and demonstrates the potential of this new dataset through a detailed analysis of its contents and baseline performance measurements over a variety of state. Note: This repository has code for the VLC-BERT transformer model. Zero-shot results on WebQA show that PromptCap. OK-VQA is a new dataset for visual question answering that requires methods which can draw upon outside knowledge to answer questions. Then download the collecton file (all_blocks. As shown in Figure[4] the Q-Former consists of two transformer submodules sharing the same self-attention layers. 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. Mia Qiao et al. 8% in CIDEr), and VQA (+1. txt -. * 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(). 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. VQAv2, OKVQA, OCRVQA, GQA, TextVQA, VGQA, DocVQA, DVQA: question Answer the question directly with a short sentence or phrase. MLLM-DataEngine, a novel closed-loop system that bridges data generation, model training, and evaluation. Corresponding of the last pytorch_model_**. Specifically, we used OKVQA (Marino et al. To install training or eval dependencies, run one of the first two commands. 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. 6% on A-OKVQA). Put the download. However, current systems mostly rely on separate models to predict answers and generate explanations, leading to less grounded and frequently inconsistent results. 6 CIDEr score vs previous best 113. Our language guidance improves the performance of CLIP by 7. 8% in the challenging A-OKVQA dataset. Knowledge-based visual question answering is a very challenging and widely concerned task. You signed in with another tab or window. 6% on VQAv2. 14,055 open-ended. Case study shows VLM trained our models provide accurate answers for challenging. au Online enquiry form. , Section 5), a neural OKVQA system that targets this class of queries and reasoning structure. OK-VQA and A-OKVQA, delivering 61. pytorch multimodal-learning visual-question-answering gpt-3 prompt-engineering okvqa a-okvqa. Model type: BLIVA is an open-source Vision-Languagde model trained by initializing from InstructBLIP and alignment with Vicuna on multimodal instruction-finetuning data. Image Captioning Visual Question Answering COCO NoCaps TextCaps VQAv2 TextVQA VizWiz-QA OKVQA GIT2 145. We ultized well-trained model on Wikilarge to conduct inference on the VQA datasets, the trained word2vec model can be found here, should be put in code/src. 6 Web-Image-Text (1. distributed. g. 4% of the dataset needed to be corrected and 10. 2) It flexibly interfaces with a wide range of LLMs to perform VQA. Vision-and-language reasoning requires an understanding of visual concepts, language semantics, and, most importantly, the alignment and relationships between these two modalities. 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. 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. 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. Knowledge graphs are commonly. 7%, which would no longer be SOTA as it is a bit less than your own group's work on PNP-VQA). We train a VLM model on our. GQA Compositional questions over real-world images. SelTDA. . Different from generic captions, PromptCap takes a natural-language prompt to control the visual entities to describe in the generated caption. It achieves SOTA performance on COCO captioning (150 CIDEr). py","contentType":"file"},{"name. * fix optimizer zero_grad under amp * zero-shot gqa evaluation * Fix #119. ECCV 2022 论文开源项目合集，同时欢迎各位大佬提交issue，分享ECCV 2020开源项目 - GitHub - amusi/ECCV2022-Papers-with-Code: ECCV 2022 论文开源项目合集，同时欢迎. This category is called outside-knowledge visual question answering (OK-VQA). json: map passages ids to line ids in all_blocks. 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. 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. 0 vs 56. 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. There are also other advantages to booting in UEFI mode v. 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 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. {"payload":{"allShortcutsEnabled":false,"fileTree":{"Datasets/OKVQA":{"items":[{"name":"Readme. Factually Augmented RLHF effectively utilizes existing human annotations to improve. Knowledge-based visual question answering (VQA) requires external knowledge beyond the image to answer the question. PDF. 1% and 55. md","contentType":"file. Phone: +61 3 9637 2806 (from 9:00 am–5:00 pm, Monday–Friday) Email: vrqa@education. On the challenging A-OKVQA dataset, our method outperforms few-shot methods by as much as 20%. Dongxu Li. (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),. Official repository for A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge. 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. 6% on VQAv2. We demonstrate PROMPTCAP's effectiveness on an existing pipeline in which GPT-3 is prompted with image captions to carry out VQA. 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. 9 71. It is trained on a large multimodal dataset (e. 这些数据集包括需要广泛知识的 vqa（如 okvqa 和 a-okvqa）、需要 ocr 的 vqa（如 ocrvqa 和 textcaps）等。 2. In this paper we create a dataset with questions exclusively about detailed properties{"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":"LICENSE","path":"LICENSE","contentType":"file"},{"name":"README. This week presented PaLI which is a language visual model that can perform tasks in 100 languages. S3VQA provides a new approach that involves Select, Substitute, and Search (SSS) for open-domain visual question answering. The MC component of the dataset bypasses many difficulties inherent in direct answer evaluation and allows for a simple, clean accuracy score. By defining new functions in ModuleParser, e. 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. yaml","path":"projects/krisp/configs/krisp. 2% of the number of samples used to train SimVLM. a. 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. yaml","path":"vigc/configs/datasets/a-okvqa/vqg/train. S3VQA. In the evaluation with. from A-OKVQA (left) and VQAv2 (right) datasets along with REPARE outputs. 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. yaml","path":"vigc. py and then follow the instruction on the prompts to view in browser. On the challenging A-OKVQA dataset, our method even outperforms few-shot methods by as much as 20%. Knowledge-based visual question answering (VQA) requires external knowledge beyond the image to answer the question. • GCP Vision APIを⽤いてOCRも実施し，学習に利⽤. 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. 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. Reload to refresh your session. Saved searches Use saved searches to filter your results more quicklyStatistics. No need to download if you want to train your own model; Sample. It covers a range of. Assuming that we have already retrieved relevant passages for each question, the first step consists in generating cross-attention scores. • 上記に加えて，物体検出⽤のデータセットやVQA⽤の. RLHF further enhances human alignment, reduces hallucination, and encourages truthfulness based on evaluations. 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. Recent. VQAv2 and OKVQA are natural image question-answering datasets, COCO is a captioning dataset, and AI2D is a multiple-choice dataset involving scientific diagrams. 1 - - 82. f. 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). VQA 2. We design a new dataset, GQA, to address these shortcomings, featuring compositional questions over real-world images. A-OKVQA, COCO Caption, and OCR VQA datasets is considered inferior compared to LLaVA and Mini-GPT4. Sidney Black. OK-VQA and A-OKVQA, delivering 61. 4% on OK-VQA and 59. yaml","path":"vigc/projects. ∙various PLMs. looking forward to the training and finetuning codeWe achieve state-of-the-art results on a wide range of vision-language tasks, such as image-text retrieval (+2. On the challenging A-OKVQA dataset, our method outperforms some few-shot methods by as much as 20\%. A-OKVQA. github","path":". Mini-GPT4. 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. 6% needed to be removed. We observe that many visual questions, which contain deictic referential phrases referring to entities in the image, can be rewritten as "non-grounded" questions. json files for OK-VQA are answer_aware_examples_okvqa. datasets: pre-extracted image features with this script (Optional) checkpoint: our model checkpoint. UEFI can boot both MBR and GPT drives. 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. Here is a way to logically break down this. Introduction. vic. Hi, eval_okvqa_zeroshot_flant5xl. 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). bash run_okvqa_train. Fig. 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. OK-VQA (Outside Knowledge Visual Question Answering) Introduced by Marino et al. md","path":"README. g. The. It has been shown that PLM-enhanced approaches (Gui et al. 7% accuracies on their testing sets, respectively. Finally, 3% of the questions require knowledge about physics. However, the popular data set has serious limitations. 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. The goal of VQA is to teach machines to understand the content of an image and answer questions about it in natural language. 0 dataset: train2015. OK-VQA [36]. 8% on OK-VQA, 5. Recently a series of works utilize large language models (e. State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow. Finally we address VQA as a text generation task with an effective encoder-decoder paradigm, which achieves state-of-the-art results on OKVQA datasets. 7% accuracies on their testing sets, respectively. corpus size 112,724. 0 81. Reload to refresh your session. 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. 4 questions on average) per image. 1 WIT w/o L contra 47. However, in these existing zero-shot or few-shot methods, the captioning model is unaware of both task goal and information need for the integratedThis 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. We observe that many visual questions, which contain deictic referential phrases referring to entities in the image, can be rewritten as "non-grounded". 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. 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. treat OKVQA as a task of fusing structured data from the image with the unstructured text rather than a visual recog-nition problem. , how well models perform when answers are in the tail of the dis-tribution, and the complementarity of the studied models). 基于知识的数据集有R-VQA , FVQA , KVQA ,OKVQA,KBVQA. md","path":"README. 8 145. Note: Code release is in progress. 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. image is not su cient to answer the question. We provided Baidu Cloud (password:r42d) and Google Link. LAVIS是一个用于LAnguage-and-VISion智能研究和应用的Python深度学习库。. You signed out in another tab or window. main. 6% on A-OKVQA). json and candidates_okvqa. However, the popular data set has serious limitations. 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. Annotators were provided the audio tracks together with category hints (and with additional video hints. A-OKVQA: A Benchmark for Visual Question Answering Using World Knowledge 🌻dataset VQA ; OOD-CV: A Benchmark for Robustness to Out-of-Distribution Shifts of Individual Nuisances in Natural Images ; The Anatomy of Video Editing: A Dataset and Benchmark Suite for AI-Assisted Video Editing 🌻dataset 视频编辑 Also, many of the models are trained using only English, but there are thousands of languages ( 7000 languages estimated) and it is important that other languages are represented and included. We observe that many visual questions, which contain deictic referential phrases referring to entities in the image, can be rewritten as "non-grounded". 23% and 75. 0 124. in AudioCaps: Generating Captions for Audios in The Wild. It features a unified design to access state-of-the-art foundation language-vision models (ALBEF, BLIP,. To achieve. yaml","path":"vigc/configs/datasets/a-okvqa/vig/train. 1. NExT-QA Video question answering (VideoQA) benchmark to advance video understanding from describing to explaining the temporal actions. These questions require an understanding of vision, language and commonsense knowledge to answer. Despite this progress, complex visual-based tasks still remain challenging due. 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. Then you can run the shell in folder VL_captioning to reproduce results, e. In addition, some questions (18%) in A-OKVQA do require knowledge of detailed properties, but about basic-level categories. CCS CONCEPTS •Computingmethodologies→Artificialintelligence;Knowl-edge representation and reasoning; Semantic networks. f. On the challenging A-OKVQA dataset, our method even outperforms few-shot methods by as much as 20\%. You will need to create a JSON file with the name "output. png","path":"misc/framework. {"payload":{"allShortcutsEnabled":false,"fileTree":{"vigc/configs/datasets/a-okvqa/vig":{"items":[{"name":"train. GPT-3) as implicit knowledge sources, which achieve much better performance with the. Constantin Eichenberg 3 publications . 2RelatedWork Visual Question Answering. ,2017) collects. We also conduct extensive ablation stud-ies on the contribution of each component, showing that PROMPTCAP gives a consistent performance gain (3. Some example questions and their corresponding images and answers have been shown. 8 - - 49. OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge Kenneth Marino, Mohammad Rastegari, Ali Farhadi, Roozbeh Mottaghi. Recent. Reload to refresh your session. 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. A generic and efficient pre-training strategy that easily harvests development of pretrained vision models and large language models (LLMs) for vision-language pretraining. TextBasedVisionInput, a new behavior can be easily introduced to transform. ,2022) typically lead to. Modular neural networks without additional training have recently been shown to surpass end-to-end neural networks on challenging vision-language tasks. A surprisingly large fraction of queries do not assess the ability to integrate cross-modal information. 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. 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. 2% on VQAv2) over a generic captioning model that shares the same architecture and training data. py inside the above 'meta data' folder. zip, we provide a processing script and some source data for both vqa2 and okvqa datasets. MBR, they are entirely 2 different comparisons. With an ensemble of 27 models, we achieved an overall accuracy 75. LLaVA, A-OKVQA, OKVQA. 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. 12 Tasks Edit Add Remove. g. okvqa_full_corpus: the corpus is collected based on the training data and testing data 168,306. {"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":"coco_annotations","path":"coco_annotations","contentType":"directory"},{"name":"coco_clip. Flickr Caption [30] 32k COCO Caption [29] 164k VQA v2 [31] 204k A-OKVQA [32] 24k LAION-400M [33] 400M DiffusionDB [7] 14M. in A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge. 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. 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. 4% on OK-VQA and 59. We show that the use of language guidance is a simple but powerful and effective strategy for visual question answering. There are about 29,000 unique words in all captions. g. g. {"payload":{"allShortcutsEnabled":false,"fileTree":{"okvqa":{"items":[{"name":"data","path":"okvqa/data","contentType":"directory"},{"name":"function","path":"okvqa. py. 1. BLIP also demonstrates strong generalization ability when directly transferred to videolanguage tasks in a zero-shot manner. 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. Finally, 3% of the questions require knowledge about physics. This document describes Pythia v0. These questions. For example, we outperform Flamingo \cite{Deepmind:Flamingo2022} by 5.