Search Results for author:
Found 8 papers, 4 papers with code
MatchDet: A Collaborative Framework for Image Matching and Object Detection
Image matching and object detection are two fundamental and challenging tasks, while many related applications consider them two individual tasks (i. e. task-individual).
Clustered-patch Element Connection for Few-shot Learning
According to this, we propose a novel Clustered-patch Element Connection (CEC) layer to correct the mismatch problem.
Ranked #47 on
Few-Shot Semantic Segmentation
on COCO-20i (5-shot)
SpatialFormer: Semantic and Target Aware Attentions for Few-Shot Learning
Then we derive two specific attention modules, named SpatialFormer Semantic Attention (SFSA) and SpatialFormer Target Attention (SFTA), to enhance the target object regions while reduce the background distraction.
Rethinking the Metric in Few-shot Learning: From an Adaptive Multi-Distance Perspective
In this paper, for the first time, we investigate the contributions of different distance metrics, and propose an adaptive fusion scheme, bringing significant improvements in few-shot classification.
tSF: Transformer-based Semantic Filter for Few-Shot Learning
Few-Shot Learning (FSL) alleviates the data shortage challenge via embedding discriminative target-aware features among plenty seen (base) and few unseen (novel) labeled samples.
nVFNet-RDC: Replay and Non-Local Distillation Collaboration for Continual Object Detection
Continual Learning (CL) focuses on developing algorithms with the ability to adapt to new environments and learn new skills.
Decoupling Classifier for Boosting Few-shot Object Detection and Instance Segmentation
This paper focus on few-shot object detection~(FSOD) and instance segmentation~(FSIS), which requires a model to quickly adapt to novel classes with a few labeled instances.
Ranked #3 on
Few-Shot Object Detection
on MS-COCO (1-shot)
Fast and robust template matching with majority neighbour similarity and annulus projection transformation
In the paper, a novel fast and robust template matching method named A-MNS based on Majority Neighbour Similarity (MNS) and the annulus projection transformation (APT) is proposed.
