4 papers
NeuroGuard: Neural Gradient Update Aware of Representation Damage
Taigo Sakai, Kazuhito Hotta
Long-tailed class-incremental learning (LT-CIL) must learn new classes from imbalanced streams while retaining old classes. Existing methods mainly change replay, classifiers, or l…
Dynamic Distillation and Gradient Consistency for Robust Long-Tailed Incremental Learning
Taigo Sakai, Kazuhiro Hotta
The task of Long-tailed Class Incremental Learning (LT-CIL) addresses the sequential learning of new classes from datasets with imbalanced class distributions. This scenario intens…
ATLASFusion: Aggregation Tracking with Location-Aware Sparse Fusion for Robust Spatio-Temporal Multi-View Pedestrian Tracking
Keisuke Toida, Taigo Sakai, Naoki Kato +4
For multimedia spatial intelligence through time, multi-view multi-object tracking (MVMOT) suffers from persistent challenges in maintaining consistent object identities across dif…
Long-Tailed Data Classification by Increasing and Decreasing Neurons During Training
Taigo Sakai, Kazuhiro Hotta
In conventional deep learning, the number of neurons typically remains fixed during training. However, insights from biology suggest that the human hippocampus undergoes continuous…