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Found 5 papers, 5 papers with code
Task-Disruptive Background Suppression for Few-Shot Segmentation
Based on these two scores, we define a query background relevant score that captures the similarity between the backgrounds of the query and the support, and utilize it to scale support background features to adaptively restrict the impact of disruptive support backgrounds.
Task-Oriented Channel Attention for Fine-Grained Few-Shot Classification
While TDM influences high-level feature maps by task-adaptive calibration of channel-wise importance, we further introduce Instance Attention Module (IAM) operating in intermediate layers of feature extractors to instance-wisely highlight object-relevant channels, by extending QAM.
Leveraging Hidden Positives for Unsupervised Semantic Segmentation
Specifically, we add the loss propagating to local hidden positives, semantically similar nearby patches, in proportion to the predefined similarity scores.
Ranked #2 on
Unsupervised Semantic Segmentation
on Potsdam-3
Minority-Oriented Vicinity Expansion with Attentive Aggregation for Video Long-Tailed Recognition
A dramatic increase in real-world video volume with extremely diverse and emerging topics naturally forms a long-tailed video distribution in terms of their categories, and it spotlights the need for Video Long-Tailed Recognition (VLTR).
Difficulty-Aware Simulator for Open Set Recognition
Furthermore, moderate- and easy-difficulty samples are also yielded by our modified GAN and Copycat, respectively.
