PolyMaX: General Dense Prediction with Mask Transformer
Dense prediction tasks, such as semantic segmentation, depth estimation, and
surface normal prediction, can be easily formulated as per-pixel classification
(discrete outputs) or regression (continuous outputs). This per-pixel
prediction paradigm has remained popular due to the prevalence of fully
convolutional networks. However, on the recent frontier of segmentation task,
the community has been witnessing a shift of paradigm from per-pixel prediction
to cluster-prediction with the emergence of transformer architectures,
particularly the mask transformers, which directly predicts a label for a mask
instead of a pixel. Despite this shift, methods based on the per-pixel
prediction paradigm still dominate the benchmarks on the other dense prediction
tasks that require continuous outputs, such as depth estimation and surface
normal prediction. Motivated by the success of DORN and AdaBins in depth
estimation, achieved by discretizing the continuous output space, we propose to
generalize the cluster-prediction based method to general dense prediction
tasks. This allows us to unify dense prediction tasks with the mask transformer
framework. Remarkably, the resulting model PolyMaX demonstrates
state-of-the-art performance on three benchmarks of NYUD-v2 dataset. We hope
our simple yet effective design can inspire more research on exploiting mask
transformers for more dense prediction tasks. Code and model will be made
available.
65519e3d45c0dccd57473ef3
Xuan Yang
65519e3d45c0dccd57473ef4
Liangzhe Yuan
65519e3d45c0dccd57473ef5
Kimberly Wilber
admin_assigned
2023-11-13T10:34:13.539Z
630d6b268e3ff0c723322bff
/avatars/af0a07c2fb49f9e93e897631de0ba0a7.svg
Kimberly Wilber
user
kwilber
65519e3d45c0dccd57473ef6
Astuti Sharma
65519e3d45c0dccd57473ef7
Xiuye Gu
65519e3d45c0dccd57473ef8
Siyuan Qiao
65519e3d45c0dccd57473ef9
Stephanie Debats
65519e3d45c0dccd57473efa
Huisheng Wang
65519e3d45c0dccd57473efb
Hartwig Adam
65519e3d45c0dccd57473efc
Mikhail Sirotenko
claimed_verified
2024-02-21T16:29:52.533Z
65b13272dfa3789ef39937ce
/avatars/c7803adb1b82b881946b3b0aeaee5f4c.svg
Mikhail Sirotenko
user
msirotenko-g
65519e3d45c0dccd57473efd
Liang-Chieh Chen
PolyMaX: General Dense Prediction with Mask Transformer
Dense prediction tasks, such as semantic segmentation, depth estimation, and
surface normal prediction, can be easily formulated as per-pixel classification
(discrete outputs) or regression (continuous outputs). This per-pixel
prediction paradigm has remained popular due to the prevalence of fully
convolutional networks. However, on the recent frontier of segmentation task,
the community has been witnessing a shift of paradigm from per-pixel prediction
to cluster-prediction with the emergence of transformer architectures,
particularly the mask transformers, which directly predicts a label for a mask
instead of a pixel. Despite this shift, methods based on the per-pixel
prediction paradigm still dominate the benchmarks on the other dense prediction
tasks that require continuous outputs, such as depth estimation and surface
normal prediction. Motivated by the success of DORN and AdaBins in depth
estimation, achieved by discretizing the continuous output space, we propose to
generalize the cluster-prediction based method to general dense prediction
tasks. This allows us to unify dense prediction tasks with the mask transformer
framework. Remarkably, the resulting model PolyMaX demonstrates
state-of-the-art performance on three benchmarks of NYUD-v2 dataset. We hope
our simple yet effective design can inspire more research on exploiting mask
transformers for more dense prediction tasks. Code and model will be made
available.
A cluster-prediction method using mask transformers is generalized to achieve state-of-the-art performance across various dense prediction tasks, including depth estimation and surface normal prediction.
transformer architectures mask transformers cluster-prediction per-pixel prediction depth estimation surface normal prediction DORN AdaBins continuous output space discrete output space PolyMaX NYUD-v2 dataset