General enquiries . 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. In particular, S3VQA (Jain et al. 2 SimVLM. A-OKVQA, COCO Caption, and OCR VQA datasets is considered inferior compared to LLaV A and. Search. 3 An interpretable OKVQA system Continuinginthespiritof“smallstepsbeforegiantleap”,wepresent S3 (c. VL-LLaMA, VL-Vicuna. Abstract. Follow the below link to access the challenge : 3) It achieves comparable or better performance than methods relying on end-to-end training. This IS NOT expected if you are initializing LxmertModel from the checkpoint of a model. 4 结果 结果显示，架构更加简单的LLaVA-1. The Visual Question Answering (VQA) task aspires to provide a meaningful. To effectively incorporate an external KG, we transfer triples into textual format and propose a late injection mechanism for knowledge fusion. 5. Note: This repository has code for the VLC-BERT transformer model. Sidney Black. ,2021) and A-OKVQA (Schwenk et al. 1. Running. sh --task ok --version okvqa_pretrain_1 --gpu 0. Contributions. VQAv2 and OKVQA are natural image question-answering datasets, COCO is a captioning dataset, and AI2D is a multiple-choice dataset involving scientific diagrams. RLHF further enhances human alignment, reduces hallucination, and encourages truthfulness based on evaluations. On the challenging A-OKVQA dataset, our method even outperforms few-shot methods by as much as 20\%. 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. 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. f. Flickr Caption [30] 32k COCO Caption [29] 164k VQA v2 [31] 204k A-OKVQA [32] 24k LAION-400M [33] 400M DiffusionDB [7] 14M. Related work 2. Mia Qiao et al. 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. 6 - - 31. Run python vigc_demo. 0 81. “Easy to use AI that explains images” is published by MLBoy. Download the meta data, which also can be found in the main page (Resources-Data) of SBU Captions Dataset. 2RelatedWork Visual Question Answering. png","path":"misc/framework. txt. png","contentType":"file"},{"name":"tree. In this paper, we propose LaKo, a knowledge-driven VQA method via Late Knowledge-to-text Injection. It features a unified interface to easily access state-of-the-art image-language, video-language models and common datasets. In OKVQA (Marino et al. 6 65. We observe that many visual questions, which contain deictic referential phrases referring to entities in the image, can be rewritten as "non-grounded". Thanks. A-OKVQA. 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. 9 vs 56. Hi, I'm trying to evaluate the provided pre-trained BEiT3 (beit3_large_indomain_patch16_480) on the A-OKVQA dataset to check its transferability to other VQA datasets. 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. gov. 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. 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. # Evaluation ## Dependencies ```bash pip install pycocoevalcap tqdm ``` ## Image Caption ### [Flickr30K](Data Preparation. These questions. txt) Finally, download other files here . from Wikipeida) OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge Kenneth Marino, Mohammad Rastegari, Ali Farhadi, Roozbeh Mottaghi Visual Question Answering (VQA) in its ideal form lets us study reasoning in the joint space of vision and language and serves as a proxy for the AI task of scene understanding. 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. 1% and 55. main. “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. ,2022) typically lead to. You can find more details in our paper. We introduce various ways to retrieve knowledge using text and images and two reader styles: classification. Projects. Implemented in one code library. 2 ). See a full comparison of 11 papers with code. g. g. in A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge. in OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge Outside Knowledge Visual Question. This is the official repository of the Retrieval Augmented Visual Question Answering (RAVQA) project. You switched accounts on another tab or window. 3 ), establishing new state-of-the-art on zero-shot captioning (on NoCaps 121. Finally, we investigate PROMPTCAP’sView Slide. Dense Passage Retrieval. This document describes Pythia v0. ,2022;Lin et al. Figure 3. 3% on A-OKVQA, and 9. S3VQA. Furthermore, through a detailed analysis, we explain which questions benefit, and which don't, from contextualized commonsense knowledge from COMET. py. 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. 0 - - - Kosmos-1 - 67. READ FULL TEXT. g. 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. 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. Architecturally, Fuyu is a vanilla decoder-only transformer - there is no image encoder. 9 71. Then you can run the shell in folder VL_captioning to reproduce results, e. It has two tasks for video-and-language research: (1) Multilingual Video Captioning, aimed at describing a video in various languages with a compact unified captioning model, and (2) Video-guided Machine Translation, to. ∙various PLMs. OK-VQA and A-OKVQA, delivering 61. 0 124. 2% on VQAv2) over a generic captioning model that shares the same architecture and training data. 1% and 55. , image caption generation), which limit the. The total model parameters are 17 billion (language. 3 61. yml. [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. We demonstrate PromptCap's effectiveness on an existing pipeline in which GPT-3 is prompted with image captions to carry out VQA. However, the popular data set has serious limitations. image is not su cient to answer the question. json. Get an approximate text prompt, with style, matching an image. ; Dataset Download and Browsing: see Dataset Download for instructions and. 1 54. Keywords: Visual Question Answering , Multimodal Fusion , Knowledge Graph , Image Captioning á Í. You will need to create a JSON file with the name "output. A-OKVQA: Choose the correct option for the following question: question: Prerequisites Models. We perform checkpoint selection based on validation sets of VQAv2, TextVQA, OKVQA, VizWiz, Visual Dialogue, Coco, Flickr30k, and HatefulMemes. yaml","path":"vigc/configs/datasets/a-okvqa/vic/train. ,2017) collects. Extensive experiments demonstrate the effectiveness of the proposed approach on the knowledge-based VQA task. 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. Knowledge-based visual question answering (VQA) requires external knowledge beyond the image to answer the question. Visual. Case study shows VLM trained our models provide accurate answers for challenging. You signed in with another tab or window. The MC component of the dataset bypasses many difficulties inherent in direct answer evaluation and allows for a simple, clean accuracy score. ,2021) is an augmented ver-sion of OKVQA, improving both the quantity and quality of some question types. {"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":"code","path":"code","contentType":"directory"},{"name":"competition files","path. This model runs on Nvidia T4 GPU hardware. Model details. g. We group these approaches into three categories: () VLP for image-text tasks, such as image captioning, image-text retrieval,. Statistics of our instructions: Statistics of our dataset grouped by task: Model Evaluation. . 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 . LAVIS is a Python deep learning library for LAnguage-and-VISion intelligence research and applications. For example, we outperform Flamingo <cit. The VRQA regulates school education in Victoria, including senior secondary education and international education. Apoorv Khandelwal's 4 research works with 124 citations and 29 reads, including: A-OKVQA: A Benchmark for Visual Question Answering Using World Knowledge{"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":"data_process","path":"data_process","contentType":"directory"},{"name":"figure","path. json" containing your results in the correct format and submit the ". 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. PromptCap outperforms generic captions by a large margin and achieves state-of-the-art accuracy on knowledge-based VQA tasks (60. , predict-the-next-element, including both visual embeddings and textual tokens. yaml","path":"vigc/configs/datasets/a-okvqa/vqg/train. py and then follow the instruction on the prompts to view in browser. 4% on OK-VQA and 59. A-OKVQA is crowdsourced visual question. Yes you need to reimplement vqa dataset. Specifically, we used OKVQA (Marino et al. zip, we provide a processing script and some source data for both vqa2 and okvqa datasets. Jan 2023, LAVIS is now available on PyPI for installation! A plug-and-play module that enables off-the-shelf use of Large Language Models (LLMs) for visual question answering (VQA). 2% vs 44. These questions. 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. Knowledge-based visual question answering is a very challenging and widely concerned task. We also conduct extensive ablation stud-ies on the contribution of each component, showing that PROMPTCAP gives a consistent performance gain (3. Multi-modal dense re-trieval can be defined in different categories based on where the multi-modalitytakesplace. {"payload":{"allShortcutsEnabled":false,"fileTree":{"vigc/configs/datasets/a-okvqa/vqg":{"items":[{"name":"train. The field of visual question answering (VQA) has recently seen a surge in research focused on providing explanations for predicted answers. LLaVA, A-OKVQA, OKVQA. On the challenging A-OKVQA dataset, our method even outperforms few-shot methods by as much as 20%. Benefiting from large-scale vision- $ bash scripts/pretrain. Answer vocabularies for the OK-VQA and A-OKVQA . This work introduces A-OKVQA, a crowdsourced dataset composed of a diverse set of about 25K questions requiring a broad base of commonsense and world knowledge to answer, and demonstrates the potential of this new dataset through a detailed analysis of its contents and baseline performance measurements over a variety of state. 6 Web-Image-Text (1. , 2022) is a multi-hop reasoning dataset that requires a system to aggregate multiple sources to answer a question, where the answers can be found ei-ther via image search or general web search. . Img2Prompt-VQA surpasses Flamingo on zero-shot VQA on VQAv2 (61. 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. A-OKVQA, COCO Caption, and OCR VQA datasets is considered inferior compared to LLaVA and Mini-GPT4. * 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(). g. 6% needed to be removed. jsonl ├── iconvqa │ └── iconvqa_images │ ├── choose_text_val. It has been split into 9K/5K for train and test. json ├── vizwiz . NExT-QA Video question answering (VideoQA) benchmark to advance video understanding from describing to explaining the temporal actions. A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge. By using the commonly used bottom-up-attention visual features, a single MCAN model delivers 70. data: train/val/test split and a small validation collection. passage_id_to_line_id. 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. Our model consists of three components: mutual modulation, knowledge-based key–value memory network and knowledge-based representation learning. The goal of VQA is to teach machines to understand the content of an image and answer questions about it in natural language. treat OKVQA as a task of fusing structured data from the image with the unstructured text rather than a visual recog-nition problem. 3 50. In this paper, we define and explore a comprehensive list of advanced vision tasks that are intriguing to solve, but may exceed the capabilities of existing vision and vision-language models. or try full training process to get the Attention signal for iterative training. 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. Shanghai Artificial Intellegence Laboratory. A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge Dustin Schwenk, Apoorv Khandelwal, Christopher Clark, Kenneth Marino, Roozbeh Mottaghi In EMNLP 2021 [project page] Webly Supervised Concept Expansion for General Purpose Vision Models. 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. 8 44. 7%, which would no longer be SOTA as it is a bit less than your own group's work on PNP-VQA). OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge Kenneth Marino, Mohammad Rastegari, Ali Farhadi, Roozbeh Mottaghi. Knowledge graphs are commonly. In our experiments, UMAE models surpass the prior SOTA answer accuracy on A-OKVQA by 10~15%, show competitive results on OK-VQA, achieve new SOTA explanation scores on A-OKVQA and VCR, and. Instead, some are. Visual Question Answering (VQA) is a task in computer vision that involves answering questions about an image. On the challenging A-OKVQA dataset, our method even outperforms few-shot methods by as much as 20%. 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. We demonstrate PROMPTCAP's effectiveness on an existing pipeline in which GPT-3 is prompted with image captions to carry out VQA. Knowledge-based visual question answering (VQA) requires external knowledge beyond the image to answer the question. captioning, feature extraction, VQA, GradCam, zeros-shot classification. ,2022), models are free to use any existing knowledge bases to re-trieve relevant knowledge. For example, the 2019 Outside Knowledge VQA dataset "OKVQA" extends VQA by adding more challenging questions that require complex, factual, and commonsense knowledge. 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. ,2019) and its augmented versions S3VQA (Jain et al. 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 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. First download all OK-VQA files. g. 8 145. Codes for VPGTrans: Transfer Visual Prompt Generator across LLMs. However, current systems mostly rely on separate models to predict answers and generate explanations, leading to less grounded and frequently inconsistent results. 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. json │ ├── gqa_images ├── hateful_meme │ └── hm_images │ ├── dev. 3% on A-OKVQA, and 9. 41%. Model type: BLIVA is an open-source Vision-Languagde model trained by initializing from InstructBLIP and alignment with Vicuna on multimodal instruction-finetuning data. json' and 'okvqa_ans_to_cap_dict. 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. Student exchange. For example, you can download 'okvqa_question. Finally, we investigate PROMPTCAP’sVQAv2 OKVQA GQA SciQA-Img (0-shot) VizWiz (0-shot) Generalist Models Flamingo-9B - 61. 7% accuracies on their testing sets, respectively. "Question: {question} Answer:"). {"payload":{"allShortcutsEnabled":false,"fileTree":{"okvqa":{"items":[{"name":"data","path":"okvqa/data","contentType":"directory"},{"name":"function","path":"okvqa. self. Project Explorer. 1. 2019) and A-OKVQA (Schwenk et al. Some example questions and their corresponding images and answers have been shown. OK-VQA (Outside Knowledge Visual Question Answering) Introduced by Marino et al. VQA Questions about images that require an understanding of vision, language and. Key tasks are translated into languages with an advanced translation system. datasets: pre-extracted image features with this script (Optional) checkpoint: our model checkpoint. 6% on A-OKVQA). corpus size 112,724. Retrieval Augmented Visual Question Answering. A module object is the type of thing you get when you import a module. Reload to refresh your session. 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. To account for this disparity while still benefiting from the additional data, we include a. Our method continuously boosts the performance of baselines methods by an average gain of 2. 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。. Modular vision-language models (Vision-LLMs) align pretrained image encoders with frozen large language models (LLMs), representing a computationally much more efficient alternative to end-to-end training of large vision-language models from scratch, which is prohibitively expensive for most. BLIP also demonstrates strong generalization ability when directly transferred to videolanguage tasks in a zero-shot manner. A big convergence of language, vision, and multimodal pretraining is emerging. Visual Question Answering (VQA) 682 papers with code • 59 benchmarks • 106 datasets. There are about 29,000 unique words in all captions. To start training, you need to apply for and download the LLaMA-2-7B-chat-hf checkpoints here and download the LLaVA pretrained. In this paper, we propose a new Semi-Supervised VQA-NLE via Self-Critical Learning (S3C), which evaluates the candidate explanations by answering rewards to improve the logical consistency between answers and rationales. 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. GPT-3) as implicit knowledge sources, which achieve much better performance with the. 3亿数据. 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. We demonstrate PromptCap's effectiveness on an existing pipeline in which GPT-3 is prompted with image captions to carry out VQA. from A-OKVQA (left) and VQAv2 (right) datasets along with REPARE outputs. M3IT-80 is the translated version of M3IT, an open-source, large-scale Multi-modal, Multilingual Instruction Tuning dataset, designed to enable the development of general-purpose multi-modal agents. sh. state-of-the-art OKVQA systems, we are surprised to find existing OKVQA models yield close to 0 evaluation score on S3VQA. However, current systems mostly rely on separate models to predict answers and generate explanations, leading to less grounded and frequently inconsistent results. This IS expected if you are initializing LxmertModel from the checkpoint of a model trained on another task or with another architecture (e. The MC component of the dataset bypasses many dificulties inherent in direct answer evaluation and allows for a simple, clean accuracy score. By defining new functions in ModuleParser, e. 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. md","path":"README. 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. For example, OpenFlamingo can be used to generate a caption for an image, or to generate a question given an image and a. On the challenging A-OKVQA dataset, our method even outperforms few-shot methods by as much as 20%. 9 67. However, in our analysis, we found that 41. Recent works have sought to use a large language model (i. 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. {"payload":{"allShortcutsEnabled":false,"fileTree":{"vigc/projects/instruct_blip_vicuna7b/vigc/a-okvqa":{"items":[{"name":"lora_vig. • 上記に加えて，物体検出⽤のデータセットやVQA⽤の. As shown in Figure[4] the Q-Former consists of two transformer submodules sharing the same self-attention layers. exact ground truth common-sense fact triple for question support. 1. python -u -m torch. okvqa. . py --input_file=DATA_DIR/data/{}_pairs_cap_combine_sum. 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). 6% on VQAv2. 0 dataset: train2015. However, enabling general inference in the real world, e. json and candidates_okvqa. A generic and efficient pre-training strategy that easily harvests development of pretrained vision models and large language models (LLMs) for vision-language pretraining. A-OKVQA, COCO Caption, and OCR VQA datasets is considered inferior compared to LLaVA and Mini-GPT4. yaml","path":"projects/krisp/configs/krisp. We simply treat the transformer decoder like an image transformer. 0 is a dataset containing open-ended questions about images. GQA Compositional questions over real-world images. Setup. 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). To achieve. LAVIS是一个用于LAnguage-and-VISion智能研究和应用的Python深度学习库。. Reload to refresh your session. 4. 4 questions on average) per image. bash run_okvqa_full. 70% (small model) and 70. 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. 2% of the number of samples used to train SimVLM. 8 145. You need to enable JavaScript to run this app. 0 vs 56. In addition, some questions (18%) in A-OKVQA do require knowledge of detailed properties, but about basic-level categories. 7% in average recall@1), image captioning (+2. in A-OKVQA; (iv) An extensive analysis of the results leading to interesting findings (e. "Frozen finetuned" has the language model finetuned, while "Frozen" keeps LM frozen. 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,. ---, 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. 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]. It is based on the following paper: Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, Wen-tau Yih. LAVIS简介. We are still working on providing support for VQA fine-tuning. 5 51. What is LAVIS? LAVIS is a Python deep learning library for LAnguage-and-VISion research and applications. 8 Flamingo-80B - 67. ,2022). 12 Tasks Edit Add Remove. SelTDA. 6 Unified-IO-XL 100. VQA 2. The goal of VQA is to teach machines to understand the content of an image and answer questions about it in natural language. Large language models excel at a wide range of complex tasks. 8% on OK-VQA, 5. (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),. 0 45. 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. g. Fangas initialization of word embeddings. (with “ † ”) is the winning model of TextVQA Challenge 2021, based on fine-tuning T5-XL Raffel et al. 6\% on VQAv2. 0 (Goyal et al. A-OKVQA [46]). "Frozen scratch" does not load a pre-trained LM and is trained from scratch. 4% on OK-VQA and 59. Comments: 13 pages, 6 figures, 2 tables. MLLM-DataEngine: An Iterative Refinement Approach for MLLM . For example, we outperform Flamingo \cite{Deepmind:Flamingo2022} by 5. We experimented with the older engine davinci instead of the current default text-davinci-001 that is boosted for instruction. yaml","path":"vigc/configs/datasets/a-okvqa/vig/train. Knowledge-based visual question answering (VQA) requires external knowledge beyond the image to answer the question. 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. 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. 1 Introduction Large-scale language models (LLMs) have exhib-ited impressive capabilities in terms of their world${MINIGPTv2_EVALUATION_DATASET} ├── gqa │ └── test_balanced_questions. , 2022) is a multi-hop reasoning dataset that requires a system to aggregate multiple sources to answer1．OK-VQA、A-OKVQAの2種類のデータセットで実験をしている。 2．QK-VQA、A-OKVQAともに知識ベースでの回答が必要なVQA の問題で、A-OKVQAのほうが後発のもの。 3．OK-VQAを⽤いて、⼿法に関するAblation Studyを実施した。2) Human-annotated explanations are expensive and time-consuming to collect. pip install open-flamingo [training] pip install open-flamingo [eval] pip install. Some studies have further explored the use of LLMs for planning and invoking models or APIs to address more general multi-modal user queries. The text-only version of the original. 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. This library aims to provide engineers and researchers with a one-stop. Specifically, on the challenging A-OKVQA dataset, LAMOC outperforms several competitive zero-shot methods and even achieves comparable results to a fine-tuned VLP model. 3) It achieves comparable or better performance than methods relying on end-to-end training. We show one example question for each knowledge category. 7% accuracies on their testing sets, respectively. BLIP-2 framework with the two stage pre-training strategy. 0 - - - 29. Benefiting from large-scale vision- Especially, the candidates. pip install open-flamingo. 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. 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. 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. 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. The model of VIGC are finetuned on these datasets. Summary. Visual Question Answering (VQA) has been a common and popular form of vision–language. WebQA (Chang et al. The current state-of-the-art on A-OKVQA is Prophet.