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Found 5 papers, 1 papers with code
DST: Data Selection and joint Training for Learning with Noisy Labels
In this paper, we propose a Data Selection and joint Training (DST) method to automatically select training samples with accurate annotations.
Feature Pyramid and Hierarchical Boosting Network for Pavement Crack Detection
To demonstrate the superiority and generality of the proposed method, we evaluate the proposed method on five crack datasets and compare it with state-of-the-art crack detection, edge detection, semantic segmentation methods.
Multi-level Contextual RNNs with Attention Model for Scene Labeling
Context in image is crucial for scene labeling while existing methods only exploit local context generated from a small surrounding area of an image patch or a pixel, by contrast long-range and global contextual information is ignored.
MUlti-Store Tracker (MUSTer): A Cognitive Psychology Inspired Approach to Object Tracking
Variations in the appearance of a tracked object, such as changes in geometry/photometry, camera viewpoint, illumination, or partial occlusion, pose a major challenge to object tracking.
Adaptive Objectness for Object Tracking
Noting that the way we integrate objectness in visual tracking is generic and straightforward, we expect even more improvement by using tracker-specific objectness.
