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. However, enabling general inference in the real world, e. launch --nproc_per_node 4 train_retriever. We thus propose the LXMERT (Learning Cross-Modality Encoder Representations from Transformers) framework to learn these vision-and-language connections. No milestone. Saved searches Use saved searches to filter your results more quicklyStatistics. OK-VQA and A-OKVQA, delivering 61. 2 Kosmos-2 - 80. Comments: 13 pages, 6 figures, 2 tables. 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. 2022) datasets, as utilized in InstructBLIP (Dai et al. 1 WIT w/o L contra 47. 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. PromptCap outperforms generic captions by a large margin and achieves state-of-the-art accuracy on knowledge-based VQA tasks (60. 8 Flamingo-80B - 67. {"payload":{"allShortcutsEnabled":false,"fileTree":{"vigc/projects/instruct_blip_vicuna7b/generate_qa/a-okvqa":{"items":[{"name":"generate_answer. You will need to create a JSON file with the name "output. Zero-shot results on WebQA show. {"payload":{"allShortcutsEnabled":false,"fileTree":{"vigc/configs/datasets/a-okvqa/vic":{"items":[{"name":"train. g. , S3 (select, substitute and search), and build a new data set and challenge around it. 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. 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. distributed. Mia Qiao et al. The MC component of the dataset bypasses many difficulties inherent in direct answer evaluation and allows for a simple, clean accuracy score. 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 the task of free-form and open-ended Visual Question Answering (VQA). 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. 6% needed to be removed. We propose embodied language models to directly incorporate real-world continuous sensor modalities into language models and thereby establish the link. 1 - Flamingo 138. 8Vision-and-language reasoning requires an understanding of visual concepts, language semantics, and, most importantly, the alignment and relationships between these two modalities. @inproceedings{wang-etal-2021-li, title = "利用图像描述与知识图谱增强表示的视觉问答(Exploiting Image Captions and External Knowledge as Representation Enhancement for Visual Question Answering)", author = "Wang, Gechao and Zhu, Muhua and Xu, Chen and Zhang, Yan and Wang, Huizhen and Zhu, Jingbo", editor = "Li, Sheng and Sun,. The MC component of the dataset bypasses many difficulties inherent in direct answer evaluation and allows for a simple, In this paper, we propose an end-to-end Retrieval-Augmented Visual Language Model (REVEAL) that learns to encode world knowledge into a large-scale memory, and to retrieve from it to answer knowledge-intensive queries. Emu is a multimodal generalist that can seamlessly generate images and texts in multimodal context. Our language guidance improves the performance of CLIP by. 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. Visual Question Answering (VQA) is a task in computer vision that involves answering questions about an image. 1. Constantin Eichenberg 3 publications . 1. In this paper we create a dataset with questions exclusively about detailed propertiesoutperform Flamingo [3] by 5. md","path":"README. 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. To prompt GPT-3 with answer heuristics and generate better answers, run the following command: okvqa. initializing a BertForSequenceClassification model from a BertForPreTraining model). • GCP Vision APIを⽤いてOCRも実施し，学習に利⽤. 3 50. 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. It features a unified interface to easily access state-of-the-art image-language, video-language models and common datasets. A major step in developing OKVQA systems is to retrieve relevant documents for the given multimodal query. There are also other advantages to booting in UEFI mode v. No need to download if you want to train your own model; Sample. A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge The Visual Question Answering (VQA) task aspires to provide a meaningful. 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. 6 CC12M (12M) 53. On the challenging A-OKVQA dataset, our method outperforms some few-shot methods by as much as 20\%. 2% on VQAv2) over a generic captioning model that shares the same architecture and training data. Before you begin, it is recommended that you setup SBERT in a new conda environment. 9 vs 56. Implemented in one code library. in A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge. We show that the use of language guidance is a simple but powerful and effective strategy for visual question an-swering. Answer vocabularies for the OK-VQA and A-OKVQA . These datasets, necessitating. LAVIS is a Python deep learning library for LAnguage-and-VISion intelligence research and applications. 0 (Goyal et al. S3VQA provides a new approach that involves Select, Substitute, and Search (SSS) for open-domain visual question answering. Here, A-OKVQA was converted to a multiple-choice task and the following format was used for the prompt: Answer with the option’s letter from the given choices directly. Zhenwei Shao, Zhou Yu, Meng Wang, Jun Yu; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023, pp. 2% of the number of samples used to train SimVLM. 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. 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. yml. A module object is the type of thing you get when you import a module. {"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":"code","path":"code","contentType":"directory"},{"name":"competition files","path. AI that explains properly. Finally, 3% of the questions require knowledge about physics. 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. 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. General enquiries . g. ,2022). Knowledge-based visual question answering (VQA) requires external knowledge beyond the image to answer the question. It achieves SOTA performance on COCO captioning (150 CIDEr). We provided Baidu Cloud (password:r42d) and Google Link. Visual Question Answering (VQA) 682 papers with code • 59 benchmarks • 106 datasets. The field of visual question answering (VQA) has recently seen a surge in research focused on providing explanations for predicted answers. 1% and 55. 5 ground truth answers per question. , GPT-3) as an implicit. yaml","path":"minigpt4/configs/datasets/cc_sbu/align. 3 70. okvqa. To achieve. {"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":". "Question: {question} Answer:"). Vision-Language Pre-training: Basics, Recent Advances, and Future Trends. Benefiting from large-scale vision- $ bash scripts/pretrain. and. Knowledge-based visual question answering (VQA) requires external knowledge beyond the image to answer the question. 4 questions on average) per image. “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. Introduction. 可以看到，尽管AN效. Large-scale pretraining. OpenFlamingo is a multimodal language model that can be used for a variety of tasks. yaml","path":"vigc/configs/datasets/a-okvqa/vqg/train. Links: [Leaderboard] Abstract. our idea on 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. [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. Themulti-modalitycanbeinthequeries, with a corpus of uni-modal documents, which enables the under-In contrast to data_source. The text-only version of the original. 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. Improving and Diagnosing Knowledge-Based Visual Question Answering via Entity Enhanced Knowledge Injection install dependencies download data/models set paths for KVQA and OKVQA to train / test models on KVQA for evaluating finetuned models with explanations from integrated Bi-Modal attention explanation system Finetune/Test/Get Explainations. RLHF further enhances human alignment, reduces hallucination, and encourages truthfulness based on evaluations. Benefiting from large-scale vision-{"payload":{"allShortcutsEnabled":false,"fileTree":{"okvqa/function":{"items":[{"name":"__init__. 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. Our language guidance improves the performance of CLIP by 7. 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. md. LAVIS is a Python deep learning library for LAnguage-and-VISion intelligence research and applications. Annotators were provided the audio tracks together with category hints (and with additional video hints. 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. Key tasks are translated into languages with an advanced translation system. 0 124. 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. SelTDA. passage_id_to_line_id. You signed out in another tab or window. To effectively incorporate an external KG, we transfer triples into textual format and propose a late injection mechanism for knowledge fusion. 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). Mirroring real-world scenarios, such as helping the visually impaired, both the questions and answers are open-ended. Focusing on two visual question answering tasks, we show that RepARe can result in a 3. However, in our analysis, we found that 41. Please save the files to the appropriate locations. Knowledge-based visual question answering is a very challenging and widely concerned task. 2 ). MBR, they are entirely 2 different comparisons. 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. The latest such methods simultaneously introduce LLM-based code generation to build programs and a number of. A-OKVQA is crowdsourced visual question. Instead, some are. 2RelatedWork Visual Question Answering. 4 million instances and 400 manually written task instructions, reformatted into a vision-to-text structure. 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. OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge Kenneth Marino, Mohammad Rastegari, Ali Farhadi, Roozbeh Mottaghi. 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. We demonstrate that by making subtle but important changes to the model architecture and. . More than 100 million people use GitHub to discover, fork, and contribute to over 420 million projects. We observe that many visual questions, which contain deictic referential phrases referring to entities in the image, can be rewritten as "non-grounded". 6\% on VQAv2. 基于知识的数据集有R-VQA , FVQA , KVQA ,OKVQA,KBVQA. {"payload":{"allShortcutsEnabled":false,"fileTree":{"vigc/configs/datasets/a-okvqa/vqg":{"items":[{"name":"train. High-quality instruction tuning data (VQA-v2, A-OKVQA, Flickr30k) significantly improves LMM capabilities on benchmarks. It has 17K/1K/6K questions for train/val/test. 1, the winning entry from Facebook AI Research (FAIR)'s A-STAR team to the VQA Challenge 2018. In this release, we use LLaVA at [email protected]) 55. We introduce various ways to retrieve knowledge using text and images and two reader styles: classification and extraction. 9 67. We observe that many visual questions, which contain deictic referential phrases referring to entities in the image, can be rewritten as "non-grounded" questions. Edit social preview. VQAv2 NAME@inproceedings{subramanian-etal-2023-modular, title = "Modular Visual Question Answering via Code Generation", author = "Subramanian, Sanjay and Narasimhan, Medhini and Khangaonkar, Kushal and Yang, Kevin and Nagrani, Arsha and Schmid, Cordelia and Zeng, Andy and Darrell, Trevor and Klein, Dan", booktitle = "Proceedings of the 61st. Hence, we call it Augmented OK-VQA (A-OKVQA). The model of VIGC are finetuned on these datasets. 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. github","path":". 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. 5只需要120万公开数据，即可超越用了14. Some studies have further explored the use of LLMs for planning and invoking models or APIs to address more general multi-modal user queries. 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. 3 ), establishing new state-of-the-art on zero-shot captioning (on NoCaps 121. To install training or eval dependencies, run one of the first two commands. 8 3) It achieves comparable or better performance than methods relying on end-to-end training. 14,055 open-ended questions. 8% on OK-VQA, 5. We design a new dataset, GQA, to address these shortcomings, featuring compositional questions over real-world images. in A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge. Train and test sets, contains 6765 question-image pairs. See a full comparison of 11 papers with code. This week presented PaLI which is a language visual model that can perform tasks in 100 languages. Resources and Tools ; Benchmarks: see Benchmark for instructions to evaluate and train supported models. 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. On the challenging A-OKVQA dataset, our method even outperforms few-shot methods by as much as 20\%. We select the checkpoint at step 65'000 for IDEFICS-9B and at step 37'500 for IDEFICS. 70% (small model) and 70. 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. 7 - - 28. 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. Student exchange. 41%. Co-authors. (with “ † ”) is the winning model of TextVQA Challenge 2021, based on fine-tuning T5-XL Raffel et al. The goal of VQA is to teach machines to understand the content of an image and answer questions about it in natural language. PDF. 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. Official repository for A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge. 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. Train and test sets, contains 2640 question-image pairs. Against the formidable image-understanding datasets like VQAv2, OKVQA, COCO Captions, and AI2D, Fuyu-8B didn’t just survive; it thrived, challenging even the behemoths with more parameters!This work identifies a key structural idiom in OKVQA ,viz. On the challenging A-OKVQA dataset, our method even outperforms few-shot methods by as much as 20%. The standard splits uses 6,513 clips for training, 497 clips for validation, and 2,990 clips. 2) It renders end-to-end training unnecessary and significantly reduces the cost of deploying LLM for VQA tasks. This can be done using the option --write_crossattention_scores in test. Updated on May 11. Numbers shown in gray are from models using closed-vocabulary classification. PromptCap outperforms generic captions by a large margin and achieves state-of-the-art accuracy on knowledge-based VQA tasks (60. In this paper, we propose an end-to-end Retrieval-Augmented Visual Language Model (REVEAL) that learns to encode world knowledge into a large-scale memory, and to retrieve from it to answer knowledge-intensive queries. 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. 0 19. 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. The. 3) It eliminates the need to specialize LLMs using end-to-end finetuning and serve highly specialized LLMs to end users, thereby reducing cost. However, the popular data set has serious limitations. Our model consists of three components: mutual modulation, knowledge-based key–value memory network and knowledge-based representation learning. {"payload":{"allShortcutsEnabled":false,"fileTree":{"projects/krisp/configs/krisp/vqa2":{"items":[{"name":"krisp_pretrain. ---, 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. 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. github","contentType":"directory"},{"name":"app","path":"app","contentType. captioning, feature extraction, VQA, GradCam, zeros-shot classification. bash run_okvqa_train. , Section 5), a neural OKVQA system that targets this class of queries and reasoning structure. READ FULL TEXT. , for robotics problems, raises the challenge of grounding. To submit your method to the leaderboard, contact okvqa. json ├── vizwiz . 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. We propose. 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. 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. Our starting point is a modular re-implementation of the bottom-up top-down (up-down) model. It covers a range of. We perform checkpoint selection based on validation sets of VQAv2, TextVQA, OKVQA, VizWiz, Visual Dialogue, Coco, Flickr30k, and HatefulMemes. 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. Data Preparation . 这个库的目的是为工程师和研究人员提供一个一站式的解决方案，为他们特定的多模态场景快速开发模型，并在标准和定制的数据集中对其进行基准测试。. 1. yaml","path":"lavis/projects/blip2/eval. . 8 145. 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 ）. Reload to refresh your session. 4% of the dataset needed to be corrected and 10. VQA 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。. github","path":". image is not su cient to answer the question. LAVIS简介. It features a unified interface to easily access state-of-the-art image-language, video-language models and common datasets. We demonstrate PromptCap's effectiveness on an existing pipeline in which GPT-3 is prompted with image captions to carry out VQA. Emu is trained with a unified autoregressive objective, i. 93% (large model) overall accuracy on the test-dev split of. md","path":"Datasets/OKVQA/Readme. Benefiting from large-scale vision-OKVQA S3. pip install open-flamingo. 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. This version of Multimodal Instruction Data includes diverse and high-quality dowanstream data. A big convergence of language, vision, and multimodal pretraining is emerging. This library aims to provide engineers and researchers with a one-stop. 13 Dustin Schwenk, et al. Assuming that we have already retrieved relevant passages for each question, the first step consists in generating cross-attention scores. txt. Code is available via the LAVIS [28] frameworkBeside the performance gain, Cola is also more robust to the VLMs' errors. 3 61. Summary. The proposed method consists in several steps: 1. To effectively incorporate an external KG, we transfer triples into textual format and propose a late injection mechanism for knowledge fusion. A-OKVQA, COCO Caption, and OCR VQA datasets is considered inferior compared to LLaVA and Mini-GPT4. Run download. Dongxu Li. In contrast to the existing knowledge-based VQA datasets, the questions generally cannot be answered by simply querying a knowledge base, and instead require some form of commonsense. 3) It achieves comparable or better performance than methods relying on end-to-end training. You switched accounts on another tab or window. In this paper, we address the task of knowledge-based visual question answering and provide a benchmark, called OK-VQA, where the image content is not sufficient to answer the questions, encouraging methods that rely on external knowledge resources. Instead, some are. Specifically, we advance the big convergence from three aspects: backbone. VLC-BERT is a vision-language-commonsense transformer model that incoporates contextualized commonsense for external knowledge visual questioning tasks, OK-VQA and A-OKVQA. Contributions. 6% on VQAv2. zip" file. In this work, we show that retrieval can be practically implemented using dense representations alone, where embeddings are learned from a. BIOS mode,. 9 71. In this paper, we propose PROOFREAD -PROmpting vision language. 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. github","path":". 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. S3VQA. All code has been uploaded, but I'm still working on the documentation. py. 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/. We thus propose the LXMERT (Learning Cross-Modality Encoder Representations from Transformers) framework to learn these vision-and-language. If our work (including the software provided) helped your research, please kindly cite our paper at EMNLP 2022: Lin, Weizhe, and Bill Byrne. 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. 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). from A-OKVQA (left) and VQAv2 (right) datasets along with REPARE outputs. • 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. These questions. 6% on VQAv2. For example, we outperform Flamingo \cite{Deepmind:Flamingo2022} by 5. Prophet significantly outperforms all existing state-of-the-art methods on two challenging knowledge-based VQA datasets, OK-VQA and A-OKVQA, delivering 61. Reload to refresh your session. "Retrieval Augmented Visual Question Answering with. Follow the below link to access the challenge : 3) It achieves comparable or better performance than methods relying on end-to-end training. Setup. jsonl ├── iconvqa │ └── iconvqa_images │ ├── choose_text_val. json and candidates_okvqa. multimodal-dense-retriever-for-okvqa 2 RELATED WORK Multi-Modal Dense Passage Retrieval. 0 81. However, in our analysis, we found that 41. 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,. Image patches are instead linearly projected into the first layer of the transformer, bypassing the embedding lookup. 实验结果. Reload to refresh your session. 7. “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。针对以上问题,本文提出了利用图像描述和外部知识增强表示的视觉问答模型。该. 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. Our new dataset includes more than 14,000 questions that require external knowledge to answer. 3 An interpretable OKVQA system Continuinginthespiritof“smallstepsbeforegiantleap”,wepresent S3 (c. Summary. Introduced by Schwenk et al. This implementation is based on python3. [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. 1% and 55. To install training or eval dependencies, run one of the first two commands. 10 ground truth answers per question. okvqa_train_corpus: the corpus is collected based on the training data. okvqa. {"payload":{"allShortcutsEnabled":false,"fileTree":{"okvqa":{"items":[{"name":"data","path":"okvqa/data","contentType":"directory"},{"name":"function","path":"okvqa. PROMPTCAP outperforms generic captions by a large margin and achieves state-of-the-art accuracy on knowledge-based VQA tasks (60. OK-VQA and A-OKVQA, delivering 61. , 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). 2. The MC component of the dataset bypasses many dificulties inherent in direct answer evaluation and allows for a simple, clean accuracy score. We experimented with the older engine davinci instead of the current default text-davinci-001 that is boosted for instruction. BLIP also demonstrates strong generalization ability when directly transferred to videolanguage tasks in a zero-shot manner. * 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(). On the challenging A-OKVQA dataset, our method outperforms few-shot methods by as much as 20%. 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" containing your results in the correct format and submit the ". {"payload":{"allShortcutsEnabled":false,"fileTree":{"okvqa":{"items":[{"name":"data","path":"okvqa/data","contentType":"directory"},{"name":"function","path":"okvqa. 3. pip install open-flamingo [training] pip install open-flamingo [eval] pip install open-flamingo. 15% on OK-VQA, and achieves consistent improvements across different LLMs1. On the challenging A-OKVQA dataset, our method even outperforms few-shot methods by as much as 20\%. 0 - 77. 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. 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. 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). Then download the collecton file (all_blocks. 4% on OK-VQA and 59. 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. # Evaluation ## Dependencies ```bash pip install pycocoevalcap tqdm ``` ## Image Caption ### [Flickr30K](Data Preparation. 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. The recent GPT-4 has demonstrated extraordinary multi-modal abilities, such as directly generating websites from handwritten text and identifying humorous elements within images. {"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":"coco_annotations","path":"coco_annotations","contentType":"directory"},{"name":"coco_clip. Fuyu-8B is a multi-modal text and image transformer trained by Adept AI. OK-VQA (Outside Knowledge Visual Question Answering) Introduced by Marino et al. 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. A generic and efficient pre-training strategy that easily harvests development of pretrained vision models and large language models (LLMs) for vision-language pretraining. 2 56. 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. 7% accuracies on their testing sets, respectively. Introduced by Kim et al. Fangas initialization of word embeddings. 6% on A-OKVQA). For example, the 2019 Outside Knowledge VQA dataset "OKVQA" extends VQA by adding more challenging questions that require complex, factual, and commonsense knowledge. Sidney Black. Python. OK-VQA (Outside Knowledge Visual Question Answering) Introduced by Marino et al. Model details. "Frozen train-blind" blacks out the image. A-OKVQA, COCO Caption, and OCR VQA datasets is considered inferior compared to LLaVA and Mini-GPT4. . 3% on A-OKVQA, and 9. 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. {"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":". Recent. 🚀 Train. We propose MM-REACT, a system paradigm that integrates ChatGPT with a pool of vision experts to achieve multimodal reasoning and action. VQAv2 and OKVQA are natural image question-answering datasets, COCO is a captioning dataset, and AI2D is a multiple-choice dataset involving scientific diagrams. It features a unified design to access state-of-the-art foundation language-vision models (ALBEF, BLIP,. OK-VQA and A-OKVQA, delivering 61. 26% on test-std and test-challenge splits, respectively. in A-OKVQA; (iv) An extensive analysis of the results leading to interesting findings (e. This week presented PaLI which is a language visual model that can perform tasks in 100 languages. yaml","path":"vigc. The "text_input" returns the instruction (e.