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Found 3 papers, 1 papers with code
AnyMAL: An Efficient and Scalable Any-Modality Augmented Language Model
We present Any-Modality Augmented Language Model (AnyMAL), a unified model that reasons over diverse input modality signals (i. e. text, image, video, audio, IMU motion sensor), and generates textual responses.
Ranked #9 on
Video Question Answering
on STAR Benchmark
Self-similarity Student for Partial Label Histopathology Image Segmentation
Following this similarity learning, our similarity ensemble merges similar patches' ensembled predictions as the pseudo-label of a given patch to counteract its noisy label.
A Cascaded Learning Strategy for Robust COVID-19 Pneumonia Chest X-Ray Screening
We introduce a comprehensive screening platform for the COVID-19 (a. k. a., SARS-CoV-2) pneumonia.
