Test-time Adaptation
116 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Test-time Adaptation
Libraries
Use these libraries to find Test-time Adaptation models and implementationsMost implemented papers
Active Test-Time Adaptation: Theoretical Analyses and An Algorithm
divelab/atta
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Extensive experimental results confirm consistency with our theoretical analyses and show that the proposed ATTA method yields substantial performance improvements over TTA methods while maintaining efficiency and shares similar effectiveness to the more demanding active domain adaptation (ADA) methods.
Unified Entropy Optimization for Open-Set Test-Time Adaptation
To address these issues, we propose a simple but effective framework called unified entropy optimization (UniEnt), which is capable of simultaneously adapting to covariate-shifted in-distribution (csID) data and detecting covariate-shifted out-of-distribution (csOOD) data.
Multi-Stream Cellular Test-Time Adaptation of Real-Time Models Evolving in Dynamic Environments
In the era of the Internet of Things (IoT), objects connect through a dynamic network, empowered by technologies like 5G, enabling real-time data sharing.
Meta-Learning Initializations for Image Segmentation
ml4ai/mliis
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We extend first-order model agnostic meta-learning algorithms (including FOMAML and Reptile) to image segmentation, present a novel neural network architecture built for fast learning which we call EfficientLab, and leverage a formal definition of the test error of meta-learning algorithms to decrease error on out of distribution tasks.
Scene-Adaptive Video Frame Interpolation via Meta-Learning
myungsub/meta-interpolation
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CVPR 2020
Finally, we show that our meta-learning framework can be easily employed to any video frame interpolation network and can consistently improve its performance on multiple benchmark datasets.
Self-Adaptively Learning to Demoire from Focused and Defocused Image Pairs
baolp/demoireing_with_focused_and_defocused_images_pairs
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In this paper, we propose a self-adaptive learning method for demoireing a high-frequency image, with the help of an additional defocused moire-free blur image.
A-SDF: Learning Disentangled Signed Distance Functions for Articulated Shape Representation
To deal with the large shape variance, we introduce Articulated Signed Distance Functions (A-SDF) to represent articulated shapes with a disentangled latent space, where we have separate codes for encoding shape and articulation.
Test time Adaptation through Perturbation Robustness
Data samples generated by several real world processes are dynamic in nature \textit{i. e.}, their characteristics vary with time.
AuxAdapt: Stable and Efficient Test-Time Adaptation for Temporally Consistent Video Semantic Segmentation
ChenyangLEI/deep-video-prior
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Since inconsistency mainly arises from the model's uncertainty in its output, we propose an adaptation scheme where the model learns from its own segmentation decisions as it streams a video, which allows producing more confident and temporally consistent labeling for similarly-looking pixels across frames.
The Emergence of Objectness: Learning Zero-Shot Segmentation from Videos
rt219/the-emergence-of-objectness
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NeurIPS 2021
Our model starts with two separate pathways: an appearance pathway that outputs feature-based region segmentation for a single image, and a motion pathway that outputs motion features for a pair of images.
