10 citations · 23 across the 6 of their papers we have counts for
6 papers
MODUS: Decoder-Only Any-to-Any Modeling of Diverse Modalities
Mingqiao Ye, Zhaochong An, Zhitong Gao +11
Any-to-any models predict any modality from any combination of others within a single network, a formulation used in multimodal vision and vision-language models, and increasingly…
Language Models Improve When Pretraining Data Matches Target Tasks
David Mizrahi, Anders Boesen Lindbo Larsen, Jesse Allardice +7
Every data selection method inherently has a target. In practice, these targets often emerge implicitly through benchmark-driven iteration: researchers develop selection strategies…
FlexTok: Resampling Images into 1D Token Sequences of Flexible Length
Roman Bachmann, Jesse Allardice, David Mizrahi +6
Image tokenization has enabled major advances in autoregressive image generation by providing compressed, discrete representations that are more efficient to process than raw pixel…
4M-21: An Any-to-Any Vision Model for Tens of Tasks and Modalities
Roman Bachmann, Oğuzhan Fatih Kar, David Mizrahi +6
Current multimodal and multitask foundation models like 4M or UnifiedIO show promising results, but in practice their out-of-the-box abilities to accept diverse inputs and perform…
4M: Massively Multimodal Masked Modeling
David Mizrahi, Roman Bachmann, Oğuzhan Fatih Kar +4
Current machine learning models for vision are often highly specialized and limited to a single modality and task. In contrast, recent large language models exhibit a wide range of…
MultiMAE: Multi-modal Multi-task Masked Autoencoders
Roman Bachmann, David Mizrahi, Andrei Atanov +1
We propose a pre-training strategy called Multi-modal Multi-task Masked Autoencoders (MultiMAE). It differs from standard Masked Autoencoding in two key aspects: I) it can optional…