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Found 6 papers, 1 papers with code
Soften to Defend: Towards Adversarial Robustness via Self-Guided Label Refinement
However, most AT methods suffer from robust overfitting, i. e., a significant generalization gap in adversarial robustness between the training and testing curves.
Skeleton-Guided Instance Separation for Fine-Grained Segmentation in Microscopy
One of the fundamental challenges in microscopy (MS) image analysis is instance segmentation (IS), particularly when segmenting cluster regions where multiple objects of varying sizes and shapes may be connected or even overlapped in arbitrary orientations.
Panoptic Scene Graph Generation with Semantics-Prototype Learning
To promise consistency and accuracy during the transfer process, we propose to measure the invariance of representations in each predicate class, and learn unbiased prototypes of predicates with different intensities.
Ranked #3 on
Panoptic Scene Graph Generation
on PSG Dataset
Graph-Theoretic Spatiotemporal Context Modeling for Video Saliency Detection
As a result, a key issue in video saliency detection is how to effectively capture the intrinsical properties of atomic video structures as well as their associated contextual interactions along the spatial and temporal dimensions.
Group-wise Deep Co-saliency Detection
In this paper, we propose an end-to-end group-wise deep co-saliency detection approach to address the co-salient object discovery problem based on the fully convolutional network (FCN) with group input and group output.
DeepSaliency: Multi-Task Deep Neural Network Model for Salient Object Detection
A key problem in salient object detection is how to effectively model the semantic properties of salient objects in a data-driven manner.
