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Found 9 papers, 3 papers with code
MetaFusion: Infrared and Visible Image Fusion via Meta-Feature Embedding From Object Detection
Conversely, detection task furnishes object semantic information to improve the infrared and visible image fusion.
Interactive Feature Embedding for Infrared and Visible Image Fusion
General deep learning-based methods for infrared and visible image fusion rely on the unsupervised mechanism for vital information retention by utilizing elaborately designed loss functions.
SRRT: Search Region Regulation Tracking
Trackers tend to lose the target object due to the limited search region or be interfered with by distractors due to the excessive search region.
Bridging the Model-Reality Gap with Lipschitz Network Adaptation
In particular, we present a learning-based model reference adaptation approach that makes a robot system, with possibly uncertain dynamics, behave as a predefined reference model.
Self-Generated Defocus Blur Detection via Dual Adversarial Discriminators
The core insight is that a defocus blur region/focused clear area can be arbitrarily pasted to a given realistic full blurred image/full clear image without affecting the judgment of the full blurred image/full clear image.
Learning-based Bias Correction for Time Difference of Arrival Ultra-wideband Localization of Resource-constrained Mobile Robots
Accurate indoor localization is a crucial enabling technology for many robotics applications, from warehouse management to monitoring tasks.
Learning-based Bias Correction for Ultra-wideband Localization of Resource-constrained Mobile Robots
Accurate indoor localization is a crucial enabling technology for many robotics applications, from warehouse management to monitoring tasks.
Enhancing Diversity of Defocus Blur Detectors via Cross-Ensemble Network
Specifically, we design an end-to-end network composed of two logical parts: feature extractor network (FENet) and defocus blur detector cross-ensemble network (DBD-CENet).
Ranked #1 on
Defocus Estimation
on CUHK - Blur Detection Dataset
(MAE metric)
Defocus Blur Detection via Multi-Stream Bottom-Top-Bottom Fully Convolutional Network
To address these issues, we propose a multi-stream bottom-top-bottom fully convolutional network (BTBNet), which is the first attempt to develop an end-to-end deep network for DBD.
Ranked #2 on
Defocus Estimation
on CUHK - Blur Detection Dataset
(MAE metric)
