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. g. Model details. OK-VQA (Outside Knowledge Visual Question Answering) Introduced by Marino et al. {"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":". 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. Retrieval-augmented visual-language pre-training. initializing a BertForSequenceClassification model from a BertForPreTraining model). py. Sidney Black. yaml","path":"minigpt4/configs/datasets/cc_sbu/align. Multimodal IR, spanning text corpus, knowledge graph and images, called outside knowledge visual question answering (OKVQA), is of much recent interest. Zhenwei Shao, Zhou Yu, Meng Wang, Jun Yu; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023, pp. Our system. 0 45. or to create a conda environment for running OpenFlamingo, run. 可以看到，尽管AN效. yml. {"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":"coco_annotations","path":"coco_annotations","contentType":"directory"},{"name":"coco_clip. 7% accuracies on their testing sets, respectively. Follow the below link to access the challenge :For example, we outperform Flamingo by 5. Image Captioning Visual Question Answering COCO NoCaps TextCaps VQAv2 TextVQA VizWiz-QA OKVQA GIT2 145. 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. ,2022) typically lead to. txt -. In addition, some questions (18%) in A-OKVQA do require knowledge of detailed properties, but about basic-level categories. {"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":"PythonEvaluationTools","path":"PythonEvaluationTools","contentType":"directory"},{"name. 1 WIT w/o L contra 47. BLIP-2 framework with the two stage pre-training strategy. GPT-4 evalaution using FairEval on 300 instances from OK-VQA, A-OKVQA and ViQuAE, where our model outperforms MiniGPT4 and InstructBLIP in most cases. in A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge. This version of Multimodal Instruction Data includes diverse and high-quality dowanstream data. 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. OK-VQA and A-OKVQA, delivering 61. You will need to create a JSON file with the name "output. yml. yaml","path":"vigc/projects. There is not any. 2022) datasets, as utilized in InstructBLIP (Dai et al. If our work (including the software provided) helped your research, please kindly cite our paper at EMNLP 2022: Lin, Weizhe, and Bill Byrne. As shown in Figure[4] the Q-Former consists of two transformer submodules sharing the same self-attention layers. 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. Reload to refresh your session. 1, the winning entry from Facebook AI Research (FAIR)'s A-STAR team to the VQA Challenge 2018. The. 3 ), establishing new state-of-the-art on zero-shot captioning (on NoCaps 121. Note: Code release is in progress. Manually filtered to ensure all questions require outside knowledge (e. 7% in average recall@1), image captioning (+2. However, current systems mostly rely on separate models to predict answers and generate explanations, leading to less grounded and frequently inconsistent results. 6 - - 31. 0 81. We perform checkpoint selection based on validation sets of VQAv2, TextVQA, OKVQA, VizWiz, Visual Dialogue, Coco, Flickr30k, and HatefulMemes. in AudioCaps: Generating Captions for Audios in The Wild. It has been shown that PLM-enhanced approaches (Gui et al. pip install open-flamingo [training] pip install open-flamingo [eval] pip install. On the challenging A-OKVQA dataset, our method even outperforms few-shot methods by as much as 20%. 7 - - 28. ,2022). Links: [Leaderboard] Abstract. Finally, the two types of answer heuristics are encoded into the prompts to enable GPT-3 to better comprehend the task thus enhancing its capacity. OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge Kenneth Marino, Mohammad Rastegari, Ali Farhadi, Roozbeh Mottaghi. Annotators were provided the audio tracks together with category hints (and with additional video hints. 2% of the number of samples used to train SimVLM. Through our evaluation on the knowledge-intensive OK-VQA and A-OKVQA datasets, we show that VLC-BERT is. 1 - Flamingo 138. 1% and 55. , natural language answer) for the VQA type query by first reformulating the input question (using Select and Substitute) and then retrieving external knowledge (using Search). Recent works have sought to use a large. Submitting to the leaderboard. We introduce various ways to retrieve knowledge using text and images and two reader styles: classification and extraction. 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). We thus propose the LXMERT (Learning Cross-Modality Encoder Representations from Transformers) framework to learn these vision-and-language. Figure 2: Dataset examples. It has 17K/1K/6K questions for train/val/test. General enquiries . 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. Knowledge-based visual question answering (VQA) requires external knowledge beyond the image to answer the question. We observe that many visual questions, which contain deictic referential phrases referring to entities in the image, can be rewritten as "non-grounded" questions. Constantin Eichenberg 3 publications . 7% accuracies on their testing sets, respectively. The modifiers are added based on the original question, the original image, and data generated from the image and question like captions and rationales. Figure 3. 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. 4% on OK-VQA and 59. main. 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. Setup. json │ ├── testdev_balanced_questions. Large language models excel at a wide range of complex tasks. It features a unified interface to easily access state-of-the-art image-language, video-language models and common datasets. json' for reproducing results of okvqa results. 小部分需要外部知识的数据集，依赖于结构化知识（例如基于知识库增强的. py --input_file=DATA_DIR/data/{}_pairs_cap_combine_sum. Focusing on two visual question answering tasks, we show that RepARe can result in a 3. 2) It renders end-to-end training unnecessary and significantly reduces the cost of deploying LLM for VQA tasks. Meanwhile, automatic measures and human eval-uations all show the effectiveness of our method. Knowledge-based visual question answering (VQA) requires external knowledge beyond the image to answer the question. Mia Qiao et al. Dense Passage Retrieval. Then you can run the shell in folder VL_captioning to reproduce results, e. g. We demonstrate PromptCap's effectiveness on an existing pipeline in which GPT-3 is prompted with image captions to carry out VQA. 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。. Introduced by Kim et al. 8% on OK-VQA, 5. 1% and 55. Note: This repository has code for the VLC-BERT transformer model. 它有一个统一的界面设计. 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/. In this work, we introduce a general-purpose multimodal foundation model BEiT-3, which achieves state-of-the-art transfer performance on both vision and vision-language tasks. To account for this disparity while still benefiting from the additional data, we include a. 7. Flickr Caption [30] 32k COCO Caption [29] 164k VQA v2 [31] 204k A-OKVQA [32] 24k LAION-400M [33] 400M DiffusionDB [7] 14M. "Retrieval Augmented Visual Question Answering with. Finally, we investigate PROMPTCAP’sView Slide. 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. Summary. 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 result on OKVQA by Flamingo (with “*”) is obtained in a 32-shot learning setup. We propose embodied language models to directly incorporate real-world continuous sensor modalities into language models and thereby establish the link. 7%, which would no longer be SOTA as it is a bit less than your own group's work on PNP-VQA). 4. A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge. Modular neural networks without additional training have recently been shown to surpass end-to-end neural networks on challenging vision-language tasks. 4% on OK-VQA and 59. Our method integrates LLMs with three types of tools: (i) computer vision tools for extracting visual information from images, (ii) a web search tool for. The Victorian Registration and Qualifications Authority (VRQA) is the official regulator of education and training providers and qualifications in Victoria. Given an image and a natural language question about the image, the task is to provide an accurate natural language answer. 0 (Goyal et al. 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% and 55. Finally we address VQA as a text generation task with an effective encoder-decoder paradigm. g. 3亿数据. Unlike conventional models that are constrained by fixed-size vision encoders, OtterHD-8B boasts the ability to handle flexible input dimensions, ensuring its. 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. 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. 93% (large model) overall accuracy on the test-dev split of. Visual Question Answering (VQA) is a task in computer vision that involves answering questions about an image. Model type: BLIVA is an open-source Vision-Languagde model trained by initializing from InstructBLIP and alignment with Vicuna on multimodal instruction-finetuning data. The MC component of the dataset bypasses many difficulties inherent in direct answer evaluation and allows for a simple, clean accuracy score. We propose an artificial intelligence challenge to design algorithms that answer visual questions asked by people who are blind. , for robotics problems, raises the challenge of grounding. “Easy to use AI that explains images” is published by MLBoy. 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. We propose the task of free-form and open-ended Visual Question Answering (VQA). datasets: pre-extracted image features with this script (Optional) checkpoint: our model checkpoint. To achieve. You will need to create a JSON file with the name "output. txt. However, the popular data set has serious limitations. To install training or eval dependencies, run one of the first two commands. Knowledge-based visual question answering (VQA) requires external knowledge beyond the image to answer the question. Shanghai Artificial Intellegence Laboratory. 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. However, current systems mostly rely on separate models to predict answers and generate explanations, leading to less grounded and frequently inconsistent results. 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. ---, 视频播放量 250047、弹幕量 1596、点赞数 4915、投硬币枚数 104、收藏人数 1385、转发人数 563, 视频作者 PinkGentleman, 作者简介 空你几哇～我是杂食向日娱UP主、在日华人。在2010年前后入的日饭圈，于2012年夏旅居日本。请大家多多支持～非常感谢！，相关视频：2023年日本女性声优人气排行榜top 10，2020. PromptCap outperforms generic captions by a large margin and achieves state-of-the-art accuracy on knowledge-based VQA tasks (60. 3 50. 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). BIOS mode,. DoubleSsh commented on Mar 21. py. 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. Our model consists of three components: mutual modulation, knowledge-based key–value memory network and knowledge-based representation learning. Benefiting from large-scale vision-OKVQA S3. UEFI can boot both MBR and GPT drives. The MC component of the dataset bypasses many difficulties inherent in (DA) evaluation and allows for a simple, clean accuracy score. DataEngine-InstData, high-quality and targeted VQA data generated by MLLM-DataEngine, also refered to as. Different from generic captions, PromptCap takes a natural-language prompt to control the visual entities to describe in the generated caption. OK-VQA and A-OKVQA, delivering 61. 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). What you were trying to do is to call a class object within the module object that happens to have the same name as the module that contains it. VL-LLaMA, VL-Vicuna. ,2017) collects. 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. exact ground truth common-sense fact triple for question support. These questions require an understanding of vision, language and commonsense knowledge to answer. The hyperparameter settings match the NeuCRaB experiments. A-OKVQA. 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. A generic and efficient pre-training strategy that easily harvests development of pretrained vision models and large language models (LLMs) for vision-language pretraining. py","contentType":"file"},{"name. 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. yaml","path":"projects/krisp/configs/krisp. json', 'okvqa_caption. 6% in VQA score). Benefiting from large-scale vision- Especially, the candidates. Vision-and-language reasoning requires an understanding of visual concepts, language semantics, and, most importantly, the alignment and relationships between these two modalities. 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. For example, the 2019 Outside Knowledge VQA dataset "OKVQA" extends VQA by adding more challenging questions that require complex, factual, and commonsense knowledge. 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. : LAVIS (short for LAnguage-VISion) is an open-source deep learning library for language-vision research and applications, offering comprehensive support for a wide range of tasks, datasets, and state-of. 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. Vision-Language Pre-training: Basics, Recent Advances, and Future Trends. md","path":"README. jsonl ├── iconvqa │ └── iconvqa_images │ ├── choose_text_val. 0 19. 6% on VQAv2. 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. Running. gov. • 上記に加えて，物体検出⽤のデータセットやVQA⽤の. The benchmarks section lists all benchmarks using a given dataset or any of its variants. 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. 8Vision-and-language reasoning requires an understanding of visual concepts, language semantics, and, most importantly, the alignment and relationships between these two modalities. KiloGram is a resource for studying abstract visual reasoning in humans and machines. In the evaluation with. 9 67. You signed out in another tab or window. VLC-BERT is a vision-language-commonsense transformer model that incoporates contextualized commonsense for external knowledge visual questioning tasks, OK-VQA and A-OKVQA. The "text_input" returns the instruction (e. 6% on A-OKVQA). md. The models are evaluated with in-context few-shot learning, where the priming instances are selected. If possible, fine-tune it on that dataset to compare the results. However, these datasets are often collected with overrestrictive requirements inherited from their original target tasks (e. 1. 6% on A-OKVQA). Through our evaluation on the knowledge-intensive OK-VQA and A-OKVQA datasets, we show that VLC-BERT is capable of outperforming existing models that utilize static knowledge bases. Knowledge graphs are commonly. {"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":"coco_annotations","path":"coco_annotations","contentType":"directory"},{"name":"coco_clip. • GCP Vision APIを⽤いてOCRも実施し，学習に利⽤. 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. See to download and browse the dataset. No need to download if you want to train your own model; Sample. 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. {"payload":{"allShortcutsEnabled":false,"fileTree":{"misc":{"items":[{"name":"framework. We propose the task of free-form and open-ended Visual Question Answering (VQA). 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. from A-OKVQA (left) and VQAv2 (right) datasets along with REPARE outputs. 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. e. A-OKVQA, COCO Caption, and OCR VQA datasets is considered inferior compared to LLaVA and Mini-GPT4. 2019) and A-OKVQA (Schwenk et al. S3VQA. A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge. This can be done using the option --write_crossattention_scores in test. 2RelatedWork Visual Question Answering. 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. txt) Finally, download other files here . 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. 8 145. json ├── vizwiz . PDF Abstract . 1. 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. Instead, some are. github","contentType":"directory"},{"name":"app","path":"app","contentType. First, download the. 0 vs 56. 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. pip install open-flamingo. 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. yaml","path":"lavis/projects/blip2/eval. When booting in UEFI, I would bet the speed differences between MBR v. github","contentType":"directory"},{"name":"app","path":"app","contentType. This category is called outside-knowledge visual question answering (OK-VQA). In. 23% and 75. datasets: pre-extracted image features with this script (Optional) checkpoint: our model checkpoint. 8 3) It achieves comparable or better performance than methods relying on end-to-end training. You need to enable JavaScript to run this app. Our data is based on the OK-VQA dataset. We design a new dataset, GQA, to address these shortcomings, featuring compositional questions over real-world images. or to create a conda environment for running OpenFlamingo, run. What is LAVIS? LAVIS is a Python deep learning library for LAnguage-and-VISion research and applications. We show that the use of language guidance is a simple but powerful and effective strategy for visual question an-swering. Mirroring real-world scenarios, such as helping the visually impaired, both the questions and answers are open-ended. 1 - Flamingo 138. We simply treat the transformer decoder like an image transformer. A-OKVQA: Choose the correct option for the following question: question: Prerequisites Models. A-OKVQA A-OKVQA is a successor of OKVQA with more challenging and diverse questions. Finally, we investigate PROMPTCAP’sVQAv2 OKVQA GQA SciQA-Img (0-shot) VizWiz (0-shot) Generalist Models Flamingo-9B - 61. Then download the 2014_coco val anotation file in link, and put it in annotation_new folder. 4% on OK-VQA and 59. Mirroring real-world scenarios, such as helping the visually impaired, both the questions and answers are open-ended. ,2022). zip" file. In particular, S3VQA (Jain et al. {"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":". 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. This week presented PaLI which is a language visual model that can perform tasks in 100 languages. To prompt GPT-3 with answer heuristics and generate better answers, run the following command: 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. For example, you can download 'okvqa_question. {"payload":{"allShortcutsEnabled":false,"fileTree":{"projects/krisp/configs/krisp/vqa2":{"items":[{"name":"krisp_pretrain. ,2022), models are free to use any existing knowledge bases to re-trieve relevant knowledge. Visual Question Answering (VQA) is a task in computer vision that involves answering questions about an image. Underspecification in VL tasks like VQA can manifest in several ways, leading to incorrect model predictions. S3VQA provides a new approach that involves Select, Substitute, and Search (SSS) for open-domain visual question answering. md","contentType":"file. It features a unified interface to easily access state-of-the-art image-language, video-language models and common datasets. {"payload":{"allShortcutsEnabled":false,"fileTree":{"vigc/configs/datasets/a-okvqa/vig":{"items":[{"name":"train. Train and test sets, contains 6765 question-image pairs. You switched accounts on another tab or window. 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\%. Emu is trained with a unified autoregressive objective, i. [CVPR 2023] Pytorch Code of MixPHM: Redundancy-Aware Parameter-Efficient Tuning for Low-Resource Visual Question Answering - GitHub - jingjing12110/MixPHM: [CVPR 2023] Pytorch Code of MixPHM: Redundancy-Aware Parameter-Efficient Tuning for Low-Resource Visual Question AnsweringA generic and efficient pre-training strategy that easily harvests development of pretrained vision models and large language models (LLMs) for vision-language pretraining. 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. txt. 1. python -u -m torch. NExT-QA Video question answering (VideoQA) benchmark to advance video understanding from describing to explaining the temporal actions. Conclusion. See examples for more inference examples, e. Projects. You signed in with another tab or window. Dense Passage Retrieval (DPR) - is a set of tools and models for state-of-the-art open-domain Q&A research. A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge The Visual Question Answering (VQA) task aspires to provide a meaningful. OK-VQA [36]. 2 Table 2. In this paper, we. 0 - - - 29. 4% of the dataset needed to be corrected and 10. passage_id_to_line_id. 2. Assuming that we have already retrieved relevant passages for each question, the first step consists in generating cross-attention scores. 8 44. 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. formance on VQA-X [13] and A-OKVQA [49] benchmark datasets. MLLM-DataEngine, a novel closed-loop system that bridges data generation, model training, and evaluation. . datasets: pre-extracted image features. 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. Knowledge-based visual question answering is a very challenging and widely concerned task. PromptCap outperforms generic captions by a large margin and achieves state-of-the-art accuracy on knowledge-based VQA tasks (60. 9 82. Visual Question Answering (VQA) 682 papers with code • 59 benchmarks • 106 datasets. md","path":"README. (with “ † ”) is the winning model of TextVQA Challenge 2021, based on fine-tuning T5-XL Raffel et al. 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. in OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge Outside Knowledge Visual Question. A-OKVQA. md","path":"Datasets/OKVQA/Readme. 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. OK-VQA is a new dataset for visual question answering that requires methods which can draw upon outside knowledge to answer questions. OpenFlamingo is a multimodal language model that can be used for a variety of tasks. The visual retriever aims to retrieve relevant knowledge, and the visual reader seeks to predict answers based on given knowledge. Code is available via the LAVIS [28] frameworkBeside the performance gain, Cola is also more robust to the VLMs' errors. On the challenging A-OKVQA dataset, our method even outperforms few-shot methods by as much as 20%. okvqa_train_clean_corpus: the corpus is based on okvqa_train_corpus but filtered with similar process as T5, detailed process referred to paper. A-OKVQA [46]). json and candidates_okvqa. png","path":"misc/framework. For example, we outperform Flamingo <cit. VATEX is multilingual, large, linguistically complex, and diverse dataset in terms of both video and natural language descriptions. Numbers shown in gray are from models using closed-vocabulary classification. The modifiers are added based on the original question, the original image, and data generated from the image and question like captions and rationales. Official repository for A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge. 🚀 Train. 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. zip" file. 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. To effectively incorporate an external KG, we transfer triples into text and propose a late injection mechanism. 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. 6 CIDEr score vs previous best 113. VQA [37] and A-OKVQA [46] mostly require common-sense knowledge. 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. However, solving the knowledge-based visual reasoning tasks remains challenging, which requires a model to comprehensively understand image content, connect the external world knowledge, and perform step-by. 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 . {"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":"LICENSE","path":"LICENSE","contentType":"file"},{"name":"README. Phone: +61 3 9637 2806 (from 9:00 am–5:00 pm, Monday–Friday) Email: vrqa@education. Visual Question Answering (VQA) 682 papers with code • 59 benchmarks • 106 datasets. 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. f. 2 % of the number of samples used to train SimVLM. 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 questions on average) per image. in Abstract Visual Reasoning with Tangram Shapes. 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.