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Found 8 papers, 4 papers with code
Hierarchical Dynamic Image Harmonization
The MGD effectively applies distinct convolution to the foreground and background, learning the representations of foreground and background regions as well as their correlations to the global harmonization, facilitating local visual consistency for the images much more efficiently.
Ranked #1 on
Image Harmonization
on HAdobe5k(1024$\times$1024)
Model-Aware Contrastive Learning: Towards Escaping the Dilemmas
Contrastive learning (CL) continuously achieves significant breakthroughs across multiple domains.
Shaping Visual Representations with Attributes for Few-Shot Recognition
Under the guidance of attribute modality, our method can learn enhanced semantic-aware representation for classification.
Sparse Spatial Transformers for Few-Shot Learning
Finally, we propose using an image patch-matching module to calculate the distance between dense local representations, thus determining which category the query image belongs to in the support set.
Ranked #16 on
Few-Shot Image Classification
on FC100 5-way (1-shot)
Multi-level Metric Learning for Few-shot Image Recognition
Moreover, a Multi-level Metric Learning (MML) method is proposed, which not only calculates the pixel-level similarity but also considers the similarity of part-level features and global-level features.
Hierarchical Representation based Query-Specific Prototypical Network for Few-Shot Image Classification
Few-shot image classification aims at recognizing unseen categories with a small number of labeled training data.
Multi-scale Adaptive Task Attention Network for Few-Shot Learning
Then, an adaptive task attention module is proposed to select the most important LRs among the entire task.
WIDER Face and Pedestrian Challenge 2018: Methods and Results
This paper presents a review of the 2018 WIDER Challenge on Face and Pedestrian.
