Search Results for author:
Found 8 papers, 2 papers with code
Low-rank Attention Side-Tuning for Parameter-Efficient Fine-Tuning
Because learnable parameters from these methods are entangled with the pretrained model, gradients related to the frozen pretrained model's parameters have to be computed and stored during finetuning.
Rectify the Regression Bias in Long-Tailed Object Detection
While existing methods fail to handle the regression bias, the class-specific regression head for rare classes is hypothesized to be the main cause of it in this paper.
DTL: Disentangled Transfer Learning for Visual Recognition
When pre-trained models become rapidly larger, the cost of fine-tuning on downstream tasks steadily increases, too.
Multi-Label Self-Supervised Learning with Scene Images
Self-supervised learning (SSL) methods targeting scene images have seen a rapid growth recently, and they mostly rely on either a dedicated dense matching mechanism or a costly unsupervised object discovery module.
ESTISR: Adapting Efficient Scene Text Image Super-resolution for Real-Scenes
While scene text image super-resolution (STISR) has yielded remarkable improvements in accurately recognizing scene text, prior methodologies have placed excessive emphasis on optimizing performance, rather than paying due attention to efficiency - a crucial factor in ensuring deployment of the STISR-STR pipeline.
Instance-based Max-margin for Practical Few-shot Recognition
With both the new pFSL setting and novel IbM2 method, this paper shows that practical few-shot learning is both viable and promising.
Worst Case Matters for Few-Shot Recognition
Few-shot recognition learns a recognition model with very few (e. g., 1 or 5) images per category, and current few-shot learning methods focus on improving the average accuracy over many episodes.
Deeply Aligned Adaptation for Cross-domain Object Detection
Cross-domain object detection has recently attracted more and more attention for real-world applications, since it helps build robust detectors adapting well to new environments.
