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Found 16 papers, 3 papers with code
Cycle Pixel Difference Network for Crisp Edge Detection
Edge detection, as a fundamental task in computer vision, has garnered increasing attention.
Learning to utilize image second-order derivative information for crisp edge detection
In the end, we propose a U-shape network named LUS-Net which is based on the SDMCM and BRM for crisp edge detection.
SFOD: Spiking Fusion Object Detector
Thereby, we establish state-of-the-art classification results based on SNNs, achieving 93. 7\% accuracy on the NCAR dataset.
CX-ToM: Counterfactual Explanations with Theory-of-Mind for Enhancing Human Trust in Image Recognition Models
More concretely, our CX-ToM framework generates sequence of explanations in a dialog by mediating the differences between the minds of machine and human user.
X-ToM: Explaining with Theory-of-Mind for Gaining Justified Human Trust
We present a new explainable AI (XAI) framework aimed at increasing justified human trust and reliance in the AI machine through explanations.
Action Recognition
Explainable Artificial Intelligence (XAI)
+2
Occlusion Robust Face Recognition Based on Mask Learning with PairwiseDifferential Siamese Network
Deep Convolutional Neural Networks (CNNs) have been pushing the frontier of the face recognition research in the past years.
Learning with Rethinking: Recurrently Improving Convolutional Neural Networks through Feedback
However, most of the existing CNN models only learn features through a feedforward structure and no feedback information from top to bottom layers is exploited to enable the networks to refine themselves.
FoveaNet: Perspective-aware Urban Scene Parsing
Thus, they suffer from heterogeneous object scales caused by perspective projection of cameras on actual scenes and inevitably encounter parsing failures on distant objects as well as other boundary and recognition errors.
Prune the Convolutional Neural Networks with Sparse Shrink
These results have demonstrated the effectiveness of our "Sparse Shrink" algorithm.
Heteroscedastic Max-Min Distance Analysis
Many discriminant analysis methods such as LDA and HLDA actually maximize the average pairwise distances between classes, which often causes the class separation problem.
