Learn what Visual Question Answering (VQA) is, how it works, and explore models commonly used for VQA.
visual question answeringattention mechanismtransformerAttention has become an indispensable component of the models of various multimedia tasks like Image Captioning (IC) and Visual Question Answering (VQA). However, most existing attention modules are designed for capturing the spatial dependency, and are...
Limited to capturing momentary snapshots of reality in a Visual Question Answering-style (VQA) dialogue. We’ve made progress with situated LMMs, where the model is able to process a live video stream in real time and dynamically interact with users. One key innovation was the end-to-end tra...
We introduce a new Visual Question Answering Baseline (VQA) based on Condtional Batch Normalization technique. In a few words, A ResNet pipeline is altered by conditioning the Batch Normalization parameters on the question. It differs from classic approach that mainly focus on developing new attenti...
Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C. Lawrence Zitnick, and Devi Parikh. VQA: visual question answering. In Int. Conf. Comput. Vis. (ICCV), 2015. 27 Anas Awadalla, Irena Gao, Josh Gardner, Jack Hessel, Yusuf Hanafy, Wanrong Zhu, Kalyani Marath...
GuessWhat?! is an image object-guessing game between two players. Recently it has attracted considerable research interest in computer vision and natural language processing community. come back I'm back again, and I'll continue researching the GuessWhat visual dialogue task, with the help of LLM...
An additional evaluation that we perform is to analyse whether the attention module is accurate or not for the image-based VQA baselines. To summarize, through this work we thoroughly analyze the localization abilities through visual question answering for autonomous driving and provide a new bench...
An attention mechanism is a machine learning technique that directs deep learning models, like transformers, to focus on the most relevant parts of input data.
Claude 3 Haiku, Google’s Gemini 1.5 Flash 8B and Microsoft’s Phi 3.5 Vision models on benchmarks measuring college-level problem solving (MMMU), visual mathematical reasoning (MathVista), chart understanding (ChartQA), document understanding (DocQA), and general vision question answering (VQA...
we provide GRiD-3D, a novel dataset that features relative directions and complements existing visual question answering (VQA) datasets, such as CLEVR, that involve only absolute directions. We also provide baselines for the dataset with two established end-to-end VQA models. Experimental evaluations...