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Found 6 papers, 1 papers with code
Test-time Adaptation Meets Image Enhancement: Improving Accuracy via Uncertainty-aware Logit Switching
We hypothesize that enhancing the input image reduces prediction's uncertainty and increase the accuracy of TTA methods.
Test-time Similarity Modification for Person Re-identification toward Temporal Distribution Shift
However, the uncertainty cannot be computed in the same way as classification in re-id since it is an open-set task, which does not share person labels between training and testing.
Improving Image Coding for Machines through Optimizing Encoder via Auxiliary Loss
Image coding for machines (ICM) aims to compress images for machine analysis using recognition models rather than human vision.
Incorporating Supervised Domain Generalization into Data Augmentation
With the increasing utilization of deep learning in outdoor settings, its robustness needs to be enhanced to preserve accuracy in the face of distribution shifts, such as compression artifacts.
Dynamic Test-Time Augmentation via Differentiable Functions
We propose a novel image enhancement method, DynTTA, which is based on differentiable data augmentation techniques and generates a blended image from many augmented images to improve the recognition accuracy under distribution shifts.
Learning to Cascade: Confidence Calibration for Improving the Accuracy and Computational Cost of Cascade Inference Systems
This paper focuses on cascade inference systems, one kind of systems using confidence scores, and discusses the desired confidence score to improve system performance in terms of inference accuracy and computational cost.
