This paper studies event causality identification, which aims at predicting the causality relation for a pair of events in a sentence.
Out-of-distribution (OOD) detection is critical for ensuring the reliability of open-world intelligent systems.
Camouflage object detection (COD) poses a significant challenge due to the high resemblance between camouflaged objects and their surroundings.
This paper proposes a cross-supervised learning framework based on dual classifiers (DC-Net), including an evidential classifier and a vanilla classifier.
To address these issues, we propose a self-aware and cross-sample prototypical learning method (SCP-Net) to enhance the diversity of prediction in consistency learning by utilizing a broader range of semantic information derived from multiple inputs.
FRDF utilizes the directional information between object pixels to effectively enhance the intra-class compactness of salient regions.
FNs-player and FPs-player are designed with different strategies: One is to minimize FNs and the other is to minimize FPs.
Dissipative Kerr soliton microcomb has been recognized as a promising on-chip multi-wavelength laser source for fiber optical communications, as its comb lines possess frequency and phase stability far beyond independent lasers.