Grounded Visual Question Answering systems place heavy reliance on substantial computational power and data resources in pretraining. In response to this challenge, this paper introduces the LCV2 modular approach, which utilizes a frozen large language model (LLM) to bridge the off-the...
Recently the new task of visual question answering (QA) has been proposed to evaluate a model's capacity for deep image understanding. Previous works have established a loose, global association between QA sentences and images. However, many questions and answers, in practice, relate to local ...
Code for the Grounded Visual Question Answering (GVQA) model from the paper -- Don't Just Assume; Look and Answer: Overcoming Priors for Visual Question Answering - AishwaryaAgrawal/GVQA
在具有不同期望输出格式(即文本、box或其组合)的一般VL任务中,使用单一网络的UniTAB比现有技术中的特定任务实现更好或可比的性能。实验涵盖7个VL基准,包括grounded captioning,visual grounding, image captioning, and visual question answering。此外,UniTAB的统一多任务网络和与任务无关的输出序列设计使模型参数高效且...
VLMs在VQA(Visual Question Answering,视觉问答)等多模态任务上取得了巨大进步,通过利用互联网规模的图像和文本数据[8]–[10],[12]。在我们的实验中,我们使用InstructBLIP[11]作为我们的基础VLM进行微调和比较,因为它是我们实验时最先进的开源VLM。PaLM-E在一般视觉-语言任务和机器人规划上表现出色[13],但还没有...
However, previous benchmark tasks for panoramic videos are still limited to evaluate the semantic understanding of audio-visual relationships or spherical spatial property in surroundings. We propose a novel benchmark named Pano-AVQA as a large-scale grounded audio-visual question answering dataset on...
captioning, and visual question answering. Furthermore, UniTAB’s unified multi-task network and the task-agnostic output sequence design make the model parameter efficient and generalizable to new tasks. (在新选项卡中打开) Publication 研究领域 Artificial in...
Second, we propose a novel Grounded Visual Question Answering model (GVQA) that contains inductive biases and restrictions in the architecture specifically ... A Agrawal,D Batra,D Parikh,... 被引量: 37发表: 2017年 Mitigating External Barriers to Implementing Green Supply Chain Management: A Grou...
Can I Trust Your Answer? Visually Grounded Video Question Answering Introduction We study visually grounded VideoQA by forcing vision-language models (VLMs) to answer questions and simultaneously ground the relevant video moments as visual evidences. We show that this task is easy for human yet is...
In this paper, we delve into open-ended question-answering (QA) in long, egocentric videos, which allows individuals or robots to inquire about their own past visual experiences. This task presents unique challenges, including the complexity of temporally grounding queries within extensive video ...