Search Results for author:
Found 5 papers, 1 papers with code
ACLS: Adaptive and Conditional Label Smoothing for Network Calibration
We present in this paper an in-depth analysis of existing regularization-based methods, providing a better understanding on how they affect to network calibration.
ALIFE: Adaptive Logit Regularizer and Feature Replay for Incremental Semantic Segmentation
Based on this, we then introduce an adaptive logit regularizer (ALI) that enables our model to better learn new categories, while retaining knowledge for previous ones.
Decomposed Knowledge Distillation for Class-Incremental Semantic Segmentation
We introduce a CISS framework that alleviates the forgetting problem and facilitates learning novel classes effectively.
Class-Incremental Semantic Segmentation
Knowledge Distillation
OIMNet++: Prototypical Normalization and Localization-aware Learning for Person Search
To this end, we introduce a novel normalization layer, dubbed ProtoNorm, that calibrates features from pedestrian proposals, while considering a long-tail distribution of person IDs, enabling L2 normalized person representations to be discriminative.
Exploiting a Joint Embedding Space for Generalized Zero-Shot Semantic Segmentation
To this end, we leverage visual and semantic encoders to learn a joint embedding space, where the semantic encoder transforms semantic features to semantic prototypes that act as centers for visual features of corresponding classes.
