Dequan Wang 王德泉
Dequan Wang 王德泉
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Back to the Source: Diffusion-Driven Adaptation to Test-Time Corruption
Our diffusion-driven adaptation method, DDA, shares its models for classification and generation across all domains. Both models are trained on the source domain, then fixed during testing. We augment diffusion with image guidance and self-ensembling to automatically decide how much to adapt.
Jin Gao
,
Jialing Zhang
,
Xihui Liu
,
Trevor Darrell
,
Evan Shelhamer
,
Dequan Wang
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Tent: Fully Test-time Adaptation by Entropy Minimization
Tent equips a model to adapt itself to new and different data during testing ☀️ 🌧 ❄️. Tented models adapt online and batch-by-batch to reduce error on dataset shifts like corruptions, simulation-to-real discrepancies, and other differences between training and testing data.
Dequan Wang
,
Evan Shelhamer
,
Shaoteng Liu
,
Bruno Olshausen
,
Trevor Darrell
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Objects as Points
We represent objects by a single point at their bounding box center. Other properties, such as object size, dimension, 3D extent, orientation, and pose are then regressed directly from image features at the center location.
Xingyi Zhou
,
Dequan Wang
,
Philipp Krähenbühl
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