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Found 10 papers, 2 papers with code
Efficient and Accurate Scene Text Recognition with Cascaded-Transformers
In recent years, vision transformers with text decoder have demonstrated remarkable performance on Scene Text Recognition (STR) due to their ability to capture long-range dependencies and contextual relationships with high learning capacity.
Accurate Scene Text Recognition with Efficient Model Scaling and Cloze Self-Distillation
Scaling architectures have been proven effective for improving Scene Text Recognition (STR), but the individual contribution of vision encoder and text decoder scaling remain under-explored.
Trust And Balance: Few Trusted Samples Pseudo-Labeling and Temperature Scaled Loss for Effective Source-Free Unsupervised Domain Adaptation
Deep Neural Networks have significantly impacted many computer vision tasks.
Key Design Choices in Source-Free Unsupervised Domain Adaptation: An In-depth Empirical Analysis
This study provides a comprehensive benchmark framework for Source-Free Unsupervised Domain Adaptation (SF-UDA) in image classification, aiming to achieve a rigorous empirical understanding of the complex relationships between multiple key design factors in SF-UDA methods.
Sim2Real Bilevel Adaptation for Object Surface Classification using Vision-Based Tactile Sensors
Our evaluation is conducted on a dataset of tactile images obtained from a set of ten 3D printed YCB objects.
RECALL+: Adversarial Web-based Replay for Continual Learning in Semantic Segmentation
Catastrophic forgetting of previous knowledge is a critical issue in continual learning typically handled through various regularization strategies.
Key Design Choices for Double-Transfer in Source-Free Unsupervised Domain Adaptation
Fine-tuning and Domain Adaptation emerged as effective strategies for efficiently transferring deep learning models to new target tasks.
RECALL: Replay-based Continual Learning in Semantic Segmentation
Replay data are then blended with new samples during the incremental steps.
From Handheld to Unconstrained Object Detection: a Weakly-supervised On-line Learning Approach
We show that the robot can improve adaptation to novel domains, either by interacting with a human teacher (Active Learning) or with an autonomous supervision (Semi-supervised Learning).
Unsupervised Domain Adaptation in Semantic Segmentation: a Review
The aim of this paper is to give an overview of the recent advancements in the Unsupervised Domain Adaptation (UDA) of deep networks for semantic segmentation.
