Learn what Visual Question Answering (VQA) is, how it works, and explore models commonly used for VQA.
is licensed under a MIT license. Performance Benchmarks Compared to other generalist and specialist models, Florence 2 performs similar to models exponentially larger than itself. In terms of text visual question answering (TextVQA), Florence-2 out performs all other existing specialist and gen...
Upcoming trends and why on-device AI is key Transformers, with their ability to scale, have become the de facto architecture for generative AI. An ongoing trend is transformers extending to more modalities, moving beyond text and language to enable new capabilities. We’re seeing this trend in ...
To this end, 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. ...
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...
LAVIS is a Python deep learning library for LAnguage-and-VISion research and applications. It features a unified design to access state-of-the-art foundation language-vision models (ALBEF,BLIP,ALPRO,CLIP), common tasks (retrieval, captioning, visual question answering, multimodal classification etc....
We reveal that currently popular multimodal LLMs are influenced by different prompt aspects through extensive ablation experiments on VQA. We draw some valuable insights: (1) Visual prompt probing reveals that model inference is associated with key regions and some models suffer from an over-reliance...
is open source and can be run on your own infrastructure. The paper states CogVLM achieves state-of-the-art performance through 9 benchmarks, and achieves second on 4. In our testing, CogVLM performed well at a range of vision tasks in our testing, from visual question answering to docum...
However, to ensure generalization, training such networks requires enormous data sets; one visual query system was trained on more than \(1{0}^{7}\)“labeled” examples (question-answer pairs)15. Although the final result of this training is an ANN with a capability that, superficially at ...