Multimodal intelligence: Representation learning, information fusion, and applications
Deep learning methods haverevolutionized speech recognition, image recognition, and
natural language processing since 2010. Each of these tasks involves a single modality in …
natural language processing since 2010. Each of these tasks involves a single modality in …
Deep learning in microscopy image analysis: A survey
Computerized microscopy image analysis plays an important role in computer aided
diagnosis and prognosis. Machine learning techniques have powered many aspects of …
diagnosis and prognosis. Machine learning techniques have powered many aspects of …
A-okvqa: A benchmark for visual question answering using world knowledge
Abstract The Visual Question Answering (VQA) task aspires to provide a meaningful testbed
for the development of AI models that can jointly reason over visual and natural language …
for the development of AI models that can jointly reason over visual and natural language …
Pali-x: On scaling up a multilingual vision and language model
We present the training recipe and results of scaling up PaLI-X, a multilingual vision and
language model, both in terms of size of the components and the breadth of its training task …
language model, both in terms of size of the components and the breadth of its training task …
Next-qa: Next phase of question-answering to explaining temporal actions
We introduce NExT-QA, a rigorously designed video question answering (VideoQA)
benchmark to advance video understanding from describing to explaining the temporal …
benchmark to advance video understanding from describing to explaining the temporal …
Deep modular co-attention networks for visual question answering
Abstract Visual Question Answering (VQA) requires a fine-grained and simultaneous
understanding of both the visual content of images and the textual content of questions …
understanding of both the visual content of images and the textual content of questions …
Gqa: A new dataset for real-world visual reasoning and compositional question answering
DA Hudson, CD Manning - … of the IEEE/CVF conference on …, 2019 - openaccess.thecvf.com
We introduce GQA, a new dataset for real-world visual reasoning and compositional
question answering, seeking to address key shortcomings of previous VQA datasets. We …
question answering, seeking to address key shortcomings of previous VQA datasets. We …
Ok-vqa: A visual question answering benchmark requiring external knowledge
Abstract Visual Question Answering (VQA) in its ideal form lets us study reasoning in the
joint space of vision and language and serves as a proxy for the AI task of scene …
joint space of vision and language and serves as a proxy for the AI task of scene …
Towards vqa models that can read
Studies have shown that a dominant class of questions asked by visually impaired users on
images of their surroundings involves reading text in the image. But today's VQA models can …
images of their surroundings involves reading text in the image. But today's VQA models can …
The neuro-symbolic concept learner: Interpreting scenes, words, and sentences from natural supervision
We propose the Neuro-Symbolic Concept Learner (NS-CL), a model that learns visual
concepts, words, and semantic parsing of sentences without explicit supervision on any of …
concepts, words, and semantic parsing of sentences without explicit supervision on any of …