We propose a multimodal framework that uses language guidance (LG) in the form of rationales, image captions, scene graphs, etc to answer questions more accurately. 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. We leverage semantic representations of both the scenes and questions to mitigate language. 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. bin file generated: from_pretrained: same pre-trained Bert model (OK-VQA) as step2: task: task = 42 OKVQA is usedstate-of-the-art OKVQA systems, we are surprised to find existing OKVQA models yield close to 0 evaluation score on S3VQA. Additionally, we find that using gold answers for oracle question candidate selection achieves a substantial gain in VQA accuracy by up to 14. READ FULL TEXT. Get an approximate text prompt, with style, matching an image. Minor improvements. , 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. The benchmarks section lists all benchmarks using a given dataset or any of its variants. Figure 3. 8% in CIDEr), and VQA (+1. Knowledge-based visual question answering (VQA) requires external knowledge beyond the image to answer the question. Try for $5/month. A-OKVQA, COCO Caption, and OCR VQA datasets is considered inferior compared to LLaV A and. VL-LLaMA, VL-Vicuna. There are about 29,000 unique words in all captions. However, the popular data set has serious limitations. 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. 6% on VQAv2. We experimented with the older engine davinci instead of the current default text-davinci-001 that is boosted for instruction. Our system. txt -. We observe that many visual questions, which contain deictic referential phrases referring to entities in the image, can be rewritten as "non-grounded". OK-VQA (Outside Knowledge Visual Question Answering) Introduced by Marino et al. py inside the above 'meta data' folder. Retrieval Augmented Visual Question Answering. The "text_input" returns the instruction (e. Mirroring real-world scenarios, such as helping the visually impaired, both the questions and answers are open-ended. For example, we outperform Flamingo \cite{Deepmind:Flamingo2022} by 5. 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. 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. ,2022), models are free to use any existing knowledge bases to re-trieve relevant knowledge. 4% on OK-VQA and 59. I'd like to implement my own dataset, I tried to do that using the tutorial of adding dataset in the documentation but I always end up with something unclear. 9 32. Zero-shot results on WebQA show. OK-VQA is a new dataset for visual question answering that requires methods which can draw upon outside knowledge to answer questions. , 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). 6% needed to be removed. A-OKVQA, COCO Caption, and OCR VQA datasets is considered inferior compared to LLaVA and Mini-GPT4. In addition, some questions (18%) in A-OKVQA do require knowledge of detailed properties, but about basic-level categories. We simply treat the transformer decoder like an image transformer. , GPT-3) as an implicit. In this paper, we. 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. [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. 7% accuracies on their testing sets, respectively. Train and test sets, contains 6765 question-image pairs. from A-OKVQA (left) and VQAv2 (right) datasets along with REPARE outputs. {"payload":{"allShortcutsEnabled":false,"fileTree":{"Datasets/OKVQA":{"items":[{"name":"Readme. 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. 6 65. The goal of VQA is to teach machines to understand the content of an image and answer questions about it in natural language. Abstract. 1. json ├── vizwiz . First, download the. Prophet significantly outperforms all existing state-of-the-art methods on two challenging knowledge-based VQA datasets, OK-VQA and A-OKVQA, delivering 61. Here is a way to logically break down this. The Victorian Registration and Qualifications Authority (VRQA) is the official regulator of education and training providers and qualifications in Victoria. With a semi-supervised learning. A major step in developing OKVQA systems is to retrieve relevant documents for the given multimodal query. image is not su cient to answer the question. Through our evaluation on the knowledge-intensive OK-VQA and A-OKVQA datasets, we show that VLC-BERT is. • 約10Bの画像・alt-textペアをフィルタリングし，約1Bのデータを学習に利⽤. Finetuning details are available in C. Model type: BLIVA is an open-source Vision-Languagde model trained by initializing from InstructBLIP and alignment with Vicuna on multimodal instruction-finetuning data. OK-VQA [36]. md. 6 CIDEr score vs previous best 113. py and then follow the instruction on the prompts to view in browser. 2% on VQAv2) over a generic captioning model that shares the same architecture and training data. Mini-GPT4. We introduce various ways to retrieve knowledge using text and images and two reader styles: classification. Some example questions and their corresponding images and answers have been shown. The path of the model trained previously (step2 OKVQA). OK-VQA and A-OKVQA, delivering 61. Specifically, we advance the big convergence from three aspects: backbone. No milestone. This can be done using the option --write_crossattention_scores in test. 1. 3 An interpretable OKVQA system Continuinginthespiritof“smallstepsbeforegiantleap”,wepresent S3 (c. Building SBERT annotations: . Knowledge graphs are commonly. 6\% on VQAv2. 9 54. On the challenging A-OKVQA dataset, our method even outperforms few-shot methods by as much as 20%. Visual. 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. 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。. Submitting to the leaderboard. 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. VQAv2 and OKVQA are natural image question-answering datasets, COCO is a captioning dataset, and AI2D is a multiple-choice dataset involving scientific diagrams. 9 67. Figure 2: Dataset examples. Follow the below link to access the challenge : 3) It achieves comparable or better performance than methods relying on end-to-end training. 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. 观察分析可知，MUTAN和BAN这类专门用于学习图像和问题之间的高级关联的VQA模型也在OK-VQA数据集上得到了远低于VQA数据集上的结果，表明OK-VQA不能简单地由一个聪明的模型来解决，而实际上需要结合图像之外信息的方法。. 0 dataset: train2015. json: map passages ids to line ids in all_blocks. 1 - - - - BLIP-2(Vicuna-13B) 103. LAVIS简介. 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,. {"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":". 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. The result on OKVQA by Flamingo (with “*”) is obtained in a 32-shot learning setup. 1% and 55. We propose the task of free-form and open-ended Visual Question Answering (VQA). 1 WIT w/o L contra 47. in AudioCaps: Generating Captions for Audios in The Wild. Instead, some are. 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. multimodal-dense-retriever-for-okvqa 2 RELATED WORK Multi-Modal Dense Passage Retrieval. We show that the use of language guidance is a simple but powerful and effective strategy for visual question answering. {"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":"code","path":"code","contentType":"directory"},{"name":"competition files","path. formance on VQA-X [13] and A-OKVQA [49] benchmark datasets. 7 - - 28. 5 ground truth answers per question. 1% and 55. 1% and 55. The visual retriever aims to retrieve relevant knowledge, and the visual reader seeks to predict answers based on given knowledge. It is trained on a large multimodal dataset (e. We demonstrate PromptCap's effectiveness on an existing pipeline in which GPT-3 is prompted with image captions to carry out VQA. In this paper, we propose PROOFREAD -PROmpting vision language. Case study shows VLM trained our models provide accurate answers for challenging. 7% accuracies on their testing sets, respectively. 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. treat OKVQA as a task of fusing structured data from the image with the unstructured text rather than a visual recog-nition problem. Our method continuously boosts the performance of baselines methods by an average gain of 2. 6 - - 31. In this paper, we propose LaKo, a knowledge-driven VQA method via Late Knowledge-to-text Injection. 8 3) It achieves comparable or better performance than methods relying on end-to-end training. * update runner - configurable beta. 3% on A-OKVQA, and 9. The goal of VQA is to teach machines to understand the content of an image and answer questions about it in natural language. Co-authors. A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge. Benefiting from large-scale vision-{"payload":{"allShortcutsEnabled":false,"fileTree":{"okvqa/function":{"items":[{"name":"__init__. High-quality instruction tuning data (VQA-v2, A-OKVQA, Flickr30k) significantly improves LMM capabilities on benchmarks. 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). A-OKVQA is crowdsourced visual question. For example, the 2019 Outside Knowledge VQA dataset "OKVQA" extends VQA by adding more challenging questions that require complex, factual, and commonsense knowledge. 7% accuracies on their testing sets, respectively. okvqa_train_clean_corpus: the corpus is based on okvqa_train_corpus but filtered with similar process as T5, detailed process referred to paper. 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. 26% on test-std and test-challenge splits, respectively. launch --nproc_per_node 4 train_retriever. {"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":". Zero-shot results on WebQA show. txt. Multimodal IR, spanning text corpus, knowledge graph and images, called outside knowledge visual question answering (OKVQA), is of much recent interest. Large-scale pretraining. You need to enable JavaScript to run this app. a. > by 5. ternal corpus. On the challenging A-OKVQA dataset, our method even outperforms few-shot methods by as much as 20%. 3) It eliminates the need to specialize LLMs using end-to-end finetuning and serve highly specialized LLMs to end users, thereby reduc-ing cost. A-OKVQA has shifted its core task to reasoning questions . However, the popular data set has serious limitations. 8 145. a A-OKVQA is composed of about 25K questions paired with both multiple choice (MC) answer options and ten free-form answers to allow for direct answer (DA) evaluation. DataEngine-InstData, high-quality and targeted VQA data generated by MLLM-DataEngine, also refered to as. ing A-OKVQA dataset, our method outperforms few-shot methods by as much as 20%. OK-VQA: A Visual Question Answering Benchmark Requiring. 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. SelTDA. BLIP-2 beats Flamingo on zero-shot VQAv2 ( 65. 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. Updated on May 11. This document describes Pythia v0. OKVQA w/ pretrain Bibtex @inproceedings{Ding2022mukea, title={MuKEA: Multimodal Knowledge Extraction and Accumulation for Knowledge-based Visual Question Answering}, author={Yang Ding and Jing Yu and Bang Liu and Yue Hu and Mingxin Cui and Qi Wug}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. {"payload":{"allShortcutsEnabled":false,"fileTree":{"vigc/projects/instruct_blip_vicuna7b/generate_qa/a-okvqa":{"items":[{"name":"generate_answer. By defining new functions in ModuleParser, e. Finally, 3% of the questions require knowledge about physics. Annotators were provided the audio tracks together with category hints (and with additional video hints. 1. We show that the use of language guidance is a simple but powerful and effective strategy for visual question an-swering. 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. Visual. Underspecification in VL tasks like VQA can manifest in several ways, leading to incorrect model predictions. ,2022) typically lead to. 8 44. However, the popular data set has serious limitations. In this paper we create a dataset with questions exclusively about detailed propertiesoutperform Flamingo [3] by 5. 1 - Flamingo 138. 5只需要120万公开数据，即可超越用了14. Ablation on Pre-Training Corpus: We pre-train REVEAL-Base on WIT and CC12M dataset, and report the fine-tuned OKVQA performance. Introduction. A-OKVQA, COCO Caption, and OCR VQA datasets is considered inferior compared to LLaVA and Mini-GPT4. 它有一个统一的界面设计. Knowledge-based visual question answering is a very challenging and widely concerned task. datasets: pre-extracted image features. Model details. It features a unified interface to easily access state-of-the-art image-language, video-language models and common datasets. Note: Code release is in progress. Some studies have further explored the use of LLMs for planning and invoking models or APIs to address more general multi-modal user queries. 1. PromptCap outperforms generic captions by a large margin and achieves state-of-the-art accuracy on knowledge-based VQA tasks (60. {"payload":{"allShortcutsEnabled":false,"fileTree":{"okvqa":{"items":[{"name":"data","path":"okvqa/data","contentType":"directory"},{"name":"function","path":"okvqa. In this paper, we propose a novel knowledge memory embedding model with mutual modulation, named KM 4, to address the challenges of visual reasoning. Thanks. Then you can run the shell in folder VL_captioning to reproduce results, e. PDF Abstract Vision-and-language reasoning requires an understanding of visual concepts, language semantics, and, most importantly, the alignment and relationships between these two modalities. 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 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. Mirroring real-world scenarios, such as helping the visually impaired, both the questions and answers are open-ended. If possible, fine-tune it on that dataset to compare the results. [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. OK-VQA (Outside Knowledge Visual Question Answering) Introduced by Marino et al. In OKVQA (Marino 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. A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge. MLLM-DataEngine, a novel closed-loop system that bridges data generation, model training, and evaluation. Our model consists of three components: mutual modulation, knowledge-based key–value memory network and knowledge-based representation learning. ,2022;Lin et al. bash run_okvqa_train. Introduction The field of Visual Question Answering (VQA) has made amazing strides in recent years,. Early studies retrieve required knowledge from explicit knowledge. Retrieval-augmented visual-language pre-training. 8 - - 49. Our new dataset includes more than 14,000 questions that require external knowledge to answer. Given an image and a natural language question about the image, the task is to provide an accurate natural language answer. Our new dataset includes more than 14,000 questions that require external knowledge to answer. 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. It covers a range of. 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. model (FLAN-T5) of a question in A-OKVQA dataset. We provided Baidu Cloud (password:r42d) and Google Link. captioning, feature extraction, VQA, GradCam, zeros-shot classification. {"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":"coco_annotations","path":"coco_annotations","contentType":"directory"},{"name":"coco_clip. GitHub is where people build software. 7. Hence, we call it Augmented OK-VQA (A-OKVQA). 0 vs 56. MLLM-DataEngine: An Iterative Refinement Approach for MLLM . These questions. The VRQA regulates school education in Victoria, including senior secondary education and international education. To achieve. On the challenging A-OKVQA dataset, our method even outperforms few-shot methods by as much as 20%. To start training, you need to apply for and download the LLaMA-2-7B-chat-hf checkpoints here and download the LLaVA pretrained. LAVIS是一个用于LAnguage-and-VISion智能研究和应用的Python深度学习库。. We thus propose the LXMERT (Learning Cross-Modality Encoder Representations from Transformers) framework to learn these vision-and. py. {"payload":{"allShortcutsEnabled":false,"fileTree":{"eval_mm":{"items":[{"name":"mmbench","path":"eval_mm/mmbench","contentType":"directory"},{"name":"mme","path. 41%. This IS NOT expected if you are initializing LxmertModel from the checkpoint of a model. 3 70. Conclusion. yaml","path":"minigpt4/configs/datasets/cc_sbu/align. 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. We demonstrate PromptCap's effectiveness on an existing pipeline in which GPT-3 is prompted with image captions to carry out VQA. 2) It flexibly interfaces with a wide range of LLMs to perform VQA. GQA Compositional questions over real-world images. S3VQA. sh for fine-tuning on image captioning. Themulti-modalitycanbeinthequeries, with a corpus of uni-modal documents, which enables the under-In contrast to data_source. Benefiting from large-scale vision- Especially, the candidates. Sidney Black. 1 65. datasets: pre-extracted image features with this script (Optional) checkpoint: our model checkpoint. 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. See to download and browse the dataset. 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). You switched accounts on another tab or window. Python. {"payload":{"allShortcutsEnabled":false,"fileTree":{"projects/krisp/configs/krisp/vqa2":{"items":[{"name":"krisp_pretrain. 7% accuracies on their testing sets, respectively. However, current systems mostly rely on separate models to predict answers and generate explanations, leading to less grounded and frequently inconsistent results. A-OKVQA, COCO Caption, and OCR VQA datasets is considered inferior compared to LLaVA and Mini-GPT4. 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 . Dongxu Li. 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. md","path":"Datasets/OKVQA/Readme. Underspecification in VL tasks like VQA can manifest in several ways, leading to incorrect model predictions. The field of visual question answering (VQA) has recently seen a surge in research focused on providing explanations for predicted answers. OK-VQA and A-OKVQA, delivering 61. 6% on VQAv2. 8 Flamingo-80B - 67. Train and test sets, contains 6765 question-image pairs. yml. 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. The field of visual question answering (VQA) has recently seen a surge in research focused on providing explanations for predicted answers. g. 1. You signed in with another tab or window. 7% accuracies on their testing sets, respectively. 3) It achieves comparable or better performance than methods relying on end-to-end training. This paper surveys vision-language pre-training (VLP) methods for multimodal intelligence that have been developed in the last few years. For example, we outperform Flamingo \cite{Deepmind:Flamingo2022} by 5. This library aims to provide engineers and researchers with a one-stop solution to rapidly develop models for their specific multimodal scenarios, and benchmark them across standard and customized datasets. . {"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":"coco_annotations","path":"coco_annotations","contentType":"directory"},{"name":"coco_clip. yaml","path":"lavis/projects/blip2/eval. Mia Qiao et al. 1% and 55. There is not any. sh provides the script for evaluation. The models are evaluated with in-context few-shot learning, where the priming instances are selected. 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. MBR, they are entirely 2 different comparisons. Jupyter Notebook Examples . , image caption generation), which limit the. py","path":"okvqa/function/__init__. Through our evaluation on the knowledge-intensive OK-VQA and A-OKVQA datasets, we show that VLC-BERT is. In our experiments, UMAE models surpass the prior state-of-the-art answer accuracy on A-OKVQA by 10 15%, show competitive results on OK-VQA, achieve new state-of-the-art explanation scores on A-OKVQA and VCR, and demonstrate promising out-of-domain performance on VQA-X. A-OKVQA. The MC component of the dataset bypasses many difficulties inherent in direct answer evaluation and allows for a simple, clean accuracy score. Summary. 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. 2RelatedWork Visual Question Answering. Emu is trained with a unified autoregressive objective, i. The current state-of-the-art on A-OKVQA is Prophet. We propose a method to generate, select, and encode external commonsense knowledge alongside visual and textual cues in a new pre-trained Vision-Language-Commonsense transformer model, VLC-BERT. If you're using VIGC in your research or applications, please cite using this BibTeX: Prophet significantly outperforms all existing state-of-the-art methods on two challenging knowledge-based VQA datasets, OK-VQA and A-OKVQA, delivering 61. okvqa. The text-only version of the original. md","contentType":"file. {"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":". Please save the files to the appropriate locations. gov. With an ensemble of 27 models, we achieved an overall accuracy 75. "Frozen train-blind" blacks out the image. json and examples. 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. Official repository for A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge. . 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. 1% and 55. Download the meta data, which also can be found in the main page (Resources-Data) of SBU Captions Dataset. 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. Manually filtered to ensure all questions require outside knowledge (e. 传统的VQA数据集作者分为两大类：是否需要外部知识进行支持（ knowledge-based ）. 基于知识的数据集有R-VQA , FVQA , KVQA ,OKVQA,KBVQA. In this paper, we present OtterHD-8B, an innovative multimodal model evolved from Fuyu-8B, specifically engineered to interpret high-resolution visual inputs with granular precision. 6% on VQAv2. Paper ID Paper Title Authors : 8 : Learning Uncoupled-Modulation CVAE for 3D Action-Conditioned Human Motion Synthesis : Chongyang Zhong. 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. For example, OpenFlamingo can be used to generate a caption for an image, or to generate a question given an image and a. 6% on VQAv2. 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. 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. VATEX is multilingual, large, linguistically complex, and diverse dataset in terms of both video and natural language descriptions. Vision-and-language reasoning requires an understanding of visual concepts, language semantics, and, most importantly, the alignment and relationships between these two modalities. We also conduct extensive ablation stud-ies on the contribution of each component, showing that PROMPTCAP gives a consistent performance gain (3. This category is called outside-knowledge visual question answering (OK-VQA). {"payload":{"allShortcutsEnabled":false,"fileTree":{"vigc/configs/datasets/a-okvqa/vqg":{"items":[{"name":"train. 我们在三个基于外部知识的数据集上做了相关实验：FVQA,Visual7w+KB,OKVQA。FVQA前面已经介绍过了，包括2190张图像，5286个问题，193449条知识。Visual7w+KB是通过模板在Visual7w的基础上自动生成的需要使用conceptnet知识的数据集，包含8425张图像，16850个问题。To address this challenge, we propose PromptCap (Prompt-guided image Captioning), a captioning model designed to serve as a better connector between images and black-box LMs. {"payload":{"allShortcutsEnabled":false,"fileTree":{"okvqa":{"items":[{"name":"data","path":"okvqa/data","contentType":"directory"},{"name":"function","path":"okvqa. 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. To effectively incorporate an external KG, we transfer triples into textual format and propose a late injection mechanism for knowledge fusion. Our language guidance improves the performance of CLIP by 7. Paper and Citing VIGC. Image patches are instead linearly projected into the first layer of the transformer, bypassing the embedding lookup. Multimodal C4) and can be used to generate text conditioned on interleaved images/text. Factually Augmented RLHF effectively utilizes existing human annotations to improve. Shanghai Artificial Intellegence Laboratory. 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. github","path":". Dense Passage Retrieval. g. Running. * fix optimizer zero_grad under amp * zero-shot gqa evaluation * Fix #119. 2 Kosmos-2 - 80. 🤗 Transformers provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio. 1, the winning entry from Facebook AI Research (FAIR)'s A-STAR team to the VQA Challenge 2018. On the challenging A-OKVQA dataset, our method even outperforms few-shot methods by as much as 20\%. 3 70. KBVQA:文中没有引用. 1 Introduction Large-scale language models (LLMs) have exhib-ited impressive capabilities in terms of their world${MINIGPTv2_EVALUATION_DATASET} ├── gqa │ └── test_balanced_questions. It composes of an EVA-CLIP vision encoder, a Q-Former, a projection layer and an auto-regressive language model, based on the decoder only transformer architecture. Contributions. We observe that many visual questions, which contain deictic referential phrases referring to entities in the image, can be rewritten as "non-grounded". We thus propose the LXMERT (Learning Cross-Modality Encoder Representations from Transformers) framework to learn these vision-and-language. 1 54. ,2022), models are free to use any existing knowledge bases to re-trieve relevant knowledge. 1. in OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge Outside Knowledge Visual Question. TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK REMOVE; Visual Question Answering (VQA) A-OKVQA ViLBERT - OK-VQAPre-Training Corpus OKVQA Accuracy WIT (5M) 51. 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.