Visual Question Answering (VQA) within the surgical domain, utilizing Large Language Models (LLMs), offers a distinct opportunity to improve intra-operative decision-making and facilitate intuitive surgeon-AI interaction. However, the development of LLMs for surgical VQA is hindered by the scarcity ...
PitVQA: Image-Grounded Text Embedding LLM forVisual Question Answering inPituitary Surgerydoi:10.1007/978-3-031-72089-5_46Visual Question Answering (VQA) within the surgical domain, utilizing Large Language Models (LLMs), offers a distinct opportunity to improve intra-operative decision-making and ...
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
Our work aims to advance Visual Question Answering (VQA) in the surgical context with scene graph knowledge, addressing two main challenges in the current surgical VQA systems: removing question-condition bias in the surgical VQA dataset and incorporating scene-aware reasoning in the surgical VQA ...
More broadly, Pidgeon and Henwood suggest that this phase of coding is answering the question: “what categories or labels do I need in order to account for what is of importance to me in this paragraph?” (1996, p. 92). Such coding is intensive and time consuming. For example, Table...
Paper tables with annotated results for A Gaze-grounded Visual Question Answering Dataset for Clarifying Ambiguous Japanese Questions
We introduce the first goal-driven training for visual question answering and dialog agents. Specifically, we pose a cooperative 'image guessing' game betw... A Das,S Kottur,Moura, José M. F,... - IEEE Computer Society 被引量: 130发表: 2017年 Emotional Dialogue Generation using Image-Gr...
Gqa: A new dataset for real-world visual reasoning and compositional question answering. In Proceedings of the IEEE/CVF con- ference on computer vision and pattern recognition, pages 6700–6709, 2019. 5 [17] Jingwei Ji, Ranjay Krishna, Li Fei-Fei, and Juan Carlos Niebles. Action genome: ...
An example of how to use the COCO Entities annotations can be found in thecoco_entities_demo.ipynbfile. [1] P. Anderson, X. He, C. Buehler, D. Teney, M. Johnson, S. Gould, and L. Zhang. Bottom-up and top-down attention for image captioning and visual question answering. InProceed...
GeoChat can accomplish multiple tasks for remote-sensing (RS) image comprehension in a unified framework. Given suitable task tokens and user queries, the model can generate visually grounded responses (text with corresponding object locations - shown on top), visual question answering on images and...