method: SMoLA-PaLI-X Generalist Model2023-12-06
Authors: SMoLA PaLI Team
Affiliation: Google Research
Description: Omni-SMoLA uses the Soft MoE approach to (softly) mix many multimodal low rank experts.
method: PaLI-X2023-05-31
Authors: Xi Chen et al
Affiliation: Google Research
Description: Scaled up PaLI-X model
method: PaLI-3B2022-09-20
Authors: Xi Chen et al
Affiliation: Google Research
Description: PaLI (Pathways Language and Image model) 3B version
Description Paper Source Code
| Date | Method | Score | |||
|---|---|---|---|---|---|
| 2023-12-06 | SMoLA-PaLI-X Generalist Model | 0.8603 | |||
| 2023-05-31 | PaLI-X | 0.8446 | |||
| 2022-09-20 | PaLI-3B | 0.6972 | |||
| 2022-05-27 | GIT, Single Model | 0.6964 | |||
| 2022-11-07 | unitnt blip | 0.6633 | |||
| 2022-09-20 | PreSTU CC15M-SplitOCR B+B | 0.6546 | |||
| 2022-07-28 | TAG | 0.6019 | |||
| 2020-12-08 | TAP | 0.5967 | |||
| 2022-03-15 | TWA | 0.5774 | |||
| 2020-09-09 | ssbaseline | 0.5500 | |||
| 2020-08-15 | TIG | 0.5051 | |||
| 2019-11-02 | M4C (single model) | 0.4621 | |||
| 2019-11-15 | RUArt | 0.3108 | |||
| 2019-04-30 | VTA | 0.2820 | |||
| 2019-04-29 | Focus: A bottom-up approach for Scene Text VQA | 0.0882 |