Search Results for author:
Found 10 papers, 5 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 #2 on
Image Harmonization
on HAdobe5k(1024$\times$1024)
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)
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.
Joint Projection Learning and Tensor Decomposition Based Incomplete Multi-view Clustering
We further consider the graph noise of projected data caused by missing samples and use a tensor-decomposition based graph filter for robust clustering. JPLTD decomposes the original tensor into an intrinsic tensor and a sparse tensor.
Model-Aware Contrastive Learning: Towards Escaping the Dilemmas
Contrastive learning (CL) continuously achieves significant breakthroughs across multiple domains.
WIDER Face and Pedestrian Challenge 2018: Methods and Results
This paper presents a review of the 2018 WIDER Challenge on Face and Pedestrian.
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.
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-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.
Segment Anything Model Meets Image Harmonization
Image harmonization is a crucial technique in image composition that aims to seamlessly match the background by adjusting the foreground of composite images.
