This subset of natural language processing models uses input images to answer questions about those images.Model ClassReferenceDescription VQA: Visual Question Answering Agrawal et al. A model that takes an image and a free-form, open-ended natural language question about the image and outputs a ...
A key solution to visual question answering (VQA) exists in how to fuse visual and language features extracted from an input image and question. We show th... DK Nguyen,T Okatani - IEEE 被引量: 16发表: 2018年 Method for automating the construction of data stores for storing complex relat...
These models are compatible with Caffe master, unlike earlier FCNs that required a pre-release branch (note: this reference edition of the models is still in progress and not all of the models have yet been ported to master). The models are available under the same license as the Caffe-bun...
This bovine disease provides a unique animal model of the human Marfan syndrome. A herd of cattle with this disease is being developed for further studies.John Wiley & Sons, Inc.American Journal of Medical Genetics Part C: Seminars in Medical Genetics...
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Visual question answering (VQA) requires joint comprehension of images and natural language questions, where many questions can't be directly or clearly an... Z Su,C Zhu,Y Dong,... - IEEE/CVF Conference on Computer Vision & Pattern Recognition 被引量: 5发表: 2018年 The remote sensing imag...
However, softmax's output probability distribution often has the long-tail... S Guo,Y Si,J Zhao - Springer, Cham 被引量: 0发表: 2022年 Sparse And Structured Visual Attention Visual attention mechanisms are widely used in multimodal tasks, as visual question answering (VQA). One drawback ...
We perform both image-to-text and text-to-image retrieval for evaluation, and report the results with Recall@k (k = 1, 5, 10) as well as Recall@SUM (i.e., the summation of six Recall@k metrics). Fig. 6: Cross-modal retrieval and visual question answering (VQA) results....
🎉The finetuning(VQA/OCR/Grounding/Video) for Qwen2-VL-Chat series models has been supported, please check the documentation below for details: English https://github.com/modelscope/ms-swift/blob/main/docs/source_en/Multi-Modal/qwen2-vl-best-practice.md Chinese https://github.com/modelscop...
SWIFT supports training(PreTraining/Fine-tuning/RLHF), inference, evaluation and deployment of 350+ LLMs and 100+ MLLMs (multimodal large models). Developers can directly apply our framework to their own research and production environments to realize the complete workflow from model training and ...