Search Results for author:
Found 10 papers, 7 papers with code
DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning
During testing, we adapt the visual representation parameters using a self-supervised MAE loss.
Distribution Alignment for Fully Test-Time Adaptation with Dynamic Online Data Streams
Given a model trained on source data, Test-Time Adaptation (TTA) enables adaptation and inference in test data streams with domain shifts from the source.
Adapting to Distribution Shift by Visual Domain Prompt Generation
In this work, we propose an approach on top of the pre-computed features of the foundation model.
Ranked #2 on
Domain Generalization
on DomainNet
Meta-DMoE: Adapting to Domain Shift by Meta-Distillation from Mixture-of-Experts
Most existing methods perform training on multiple source domains using a single model, and the same trained model is used on all unseen target domains.
Ranked #31 on
Domain Generalization
on DomainNet
Improving ProtoNet for Few-Shot Video Object Recognition: Winner of ORBIT Challenge 2022
In this work, we present the winning solution for ORBIT Few-Shot Video Object Recognition Challenge 2022.
Few-Shot Class-Incremental Learning via Entropy-Regularized Data-Free Replay
In this paper, we show through empirical results that adopting the data replay is surprisingly favorable.
class-incremental learning
Few-Shot Class-Incremental Learning
+2
MetaFSCIL: A Meta-Learning Approach for Few-Shot Class Incremental Learning
The learning objective of these methods is often hand-engineered and is not directly tied to the objective (i. e. incrementally learning new classes) during testing.
class-incremental learning
Few-Shot Class-Incremental Learning
+2
DMM-Net: Differentiable Mask-Matching Network for Video Object Segmentation
In practice, it performs similarly to the Hungarian algorithm during inference.
Distilling the Posterior in Bayesian Neural Networks
We propose a framework, Adversarial Posterior Distillation, to distill the SGLD samples using a Generative Adversarial Network (GAN).
Adversarial Distillation of Bayesian Neural Network Posteriors
We propose a framework, Adversarial Posterior Distillation, to distill the SGLD samples using a Generative Adversarial Network (GAN).
